From df20012eb69818135c1db5b1ad7629f41b3a8789 Mon Sep 17 00:00:00 2001 From: Your Name Date: Fri, 6 Feb 2026 19:58:07 -0500 Subject: [PATCH 01/33] Update to Transformers 5.1.0 --- .gitignore | 7 +- invokeai.example.yaml | 62 +++++++ invokeai.yaml | 4 + invokeai/app/api/routers/model_manager.py | 8 +- invokeai/app/invocations/flux_text_encoder.py | 6 +- invokeai/app/invocations/sd3_text_encoder.py | 3 +- .../model_install/model_install_default.py | 4 +- invokeai/backend/image_util/safety_checker.py | 10 +- .../model_manager/load/model_loaders/flux.py | 13 +- .../backend/model_manager/load/model_util.py | 7 +- .../metadata/fetch/huggingface.py | 36 +++- .../model_manager/metadata/metadata_base.py | 3 +- .../scripts/quantize_t5_xxl_bnb_llm_int8.py | 7 +- .../stable_diffusion/diffusers_pipeline.py | 6 +- nodes/README.md | 51 ++++++ plan.md | 171 ++++++++++++++++++ pyproject.toml | 5 +- 17 files changed, 366 insertions(+), 37 deletions(-) create mode 100644 invokeai.example.yaml create mode 100644 invokeai.yaml create mode 100644 nodes/README.md create mode 100644 plan.md diff --git a/.gitignore b/.gitignore index 3e2a5bc7663..1bb2f412ffd 100644 --- a/.gitignore +++ b/.gitignore @@ -194,4 +194,9 @@ installer/InvokeAI-Installer/ .claude/ # Weblate configuration file -weblate.ini \ No newline at end of file +weblate.ini + +models/ +databases/ +configs/ +outputs/ \ No newline at end of file diff --git a/invokeai.example.yaml b/invokeai.example.yaml new file mode 100644 index 00000000000..654964ddd97 --- /dev/null +++ b/invokeai.example.yaml @@ -0,0 +1,62 @@ +# This is an example file with default and example settings. +# You should not copy this whole file into your config. +# Only add the settings you need to change to your config file. + +# Internal metadata - do not edit: +schema_version: 4.0.2 + +# Put user settings here - see https://invoke-ai.github.io/InvokeAI/configuration/: +host: 127.0.0.1 +port: 9090 +allow_origins: [] +allow_credentials: true +allow_methods: +- '*' +allow_headers: +- '*' +log_tokenization: false +patchmatch: true +models_dir: models +convert_cache_dir: models\.convert_cache +download_cache_dir: models\.download_cache +legacy_conf_dir: configs +db_dir: databases +outputs_dir: outputs +custom_nodes_dir: nodes +style_presets_dir: style_presets +workflow_thumbnails_dir: workflow_thumbnails +log_handlers: +- console +log_format: color +log_level: info +log_sql: false +log_level_network: warning +use_memory_db: false +dev_reload: false +profile_graphs: false +profiles_dir: profiles +log_memory_usage: false +model_cache_keep_alive_min: 0.0 +device_working_mem_gb: 3.0 +enable_partial_loading: false +keep_ram_copy_of_weights: true +lazy_offload: true +device: auto +precision: auto +sequential_guidance: false +attention_type: auto +attention_slice_size: auto +force_tiled_decode: false +pil_compress_level: 1 +max_queue_size: 10000 +clear_queue_on_startup: false +node_cache_size: 512 +hashing_algorithm: blake3_single +remote_api_tokens: +- url_regex: cool-models.com + token: my_secret_token +- url_regex: nifty-models.com + token: some_other_token +scan_models_on_startup: false +unsafe_disable_picklescan: false +allow_unknown_models: true diff --git a/invokeai.yaml b/invokeai.yaml new file mode 100644 index 00000000000..f9b7a42a5dd --- /dev/null +++ b/invokeai.yaml @@ -0,0 +1,4 @@ +# Internal metadata - do not edit: +schema_version: 4.0.2 +enable_partial_loading: true +# Put user settings here - see https://invoke-ai.github.io/InvokeAI/configuration/: diff --git a/invokeai/app/api/routers/model_manager.py b/invokeai/app/api/routers/model_manager.py index ddc26d9bece..2d86a3956f6 100644 --- a/invokeai/app/api/routers/model_manager.py +++ b/invokeai/app/api/routers/model_manager.py @@ -1043,10 +1043,14 @@ class HFTokenHelper: @classmethod def get_status(cls) -> HFTokenStatus: try: - if huggingface_hub.get_token_permission(huggingface_hub.get_token()): + token = huggingface_hub.get_token() + if token is None: + # No token set + return HFTokenStatus.INVALID + if huggingface_hub.get_token_permission(token): # Valid token! return HFTokenStatus.VALID - # No token set + # Token exists but has no permissions (shouldn't normally happen) return HFTokenStatus.INVALID except Exception: return HFTokenStatus.UNKNOWN diff --git a/invokeai/app/invocations/flux_text_encoder.py b/invokeai/app/invocations/flux_text_encoder.py index 56ebbe7fd9d..580aad3286a 100644 --- a/invokeai/app/invocations/flux_text_encoder.py +++ b/invokeai/app/invocations/flux_text_encoder.py @@ -2,7 +2,7 @@ from typing import Iterator, Literal, Optional, Tuple, Union import torch -from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer, T5TokenizerFast +from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation from invokeai.app.invocations.fields import ( @@ -86,7 +86,7 @@ def _t5_encode(self, context: InvocationContext) -> torch.Tensor: ExitStack() as exit_stack, ): assert isinstance(t5_text_encoder, T5EncoderModel) - assert isinstance(t5_tokenizer, (T5Tokenizer, T5TokenizerFast)) + assert isinstance(t5_tokenizer, T5Tokenizer) # Determine if the model is quantized. # If the model is quantized, then we need to apply the LoRA weights as sidecar layers. This results in @@ -186,7 +186,7 @@ def _t5_lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[ModelP def _log_t5_tokenization( self, context: InvocationContext, - tokenizer: Union[T5Tokenizer, T5TokenizerFast], + tokenizer: T5Tokenizer, ) -> None: """Logs the tokenization of a prompt for a T5-based model like FLUX.""" diff --git a/invokeai/app/invocations/sd3_text_encoder.py b/invokeai/app/invocations/sd3_text_encoder.py index 24647c9cfc7..e2ade1ddf93 100644 --- a/invokeai/app/invocations/sd3_text_encoder.py +++ b/invokeai/app/invocations/sd3_text_encoder.py @@ -8,7 +8,6 @@ CLIPTokenizer, T5EncoderModel, T5Tokenizer, - T5TokenizerFast, ) from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation @@ -102,7 +101,7 @@ def _t5_encode(self, context: InvocationContext, max_seq_len: int) -> torch.Tens ): context.util.signal_progress("Running T5 encoder") assert isinstance(t5_text_encoder, T5EncoderModel) - assert isinstance(t5_tokenizer, (T5Tokenizer, T5TokenizerFast)) + assert isinstance(t5_tokenizer, T5Tokenizer) text_inputs = t5_tokenizer( prompt, diff --git a/invokeai/app/services/model_install/model_install_default.py b/invokeai/app/services/model_install/model_install_default.py index 77dc3dfa70a..240757a0368 100644 --- a/invokeai/app/services/model_install/model_install_default.py +++ b/invokeai/app/services/model_install/model_install_default.py @@ -16,7 +16,7 @@ import torch import yaml -from huggingface_hub import HfFolder +from huggingface_hub import get_token as hf_get_token from pydantic.networks import AnyHttpUrl from pydantic_core import Url from requests import Session @@ -750,7 +750,7 @@ def _import_from_hf( ) -> ModelInstallJob: # Add user's cached access token to HuggingFace requests if source.access_token is None: - source.access_token = HfFolder.get_token() + source.access_token = hf_get_token() remote_files, metadata = self._remote_files_from_source(source) return self._import_remote_model( source=source, diff --git a/invokeai/backend/image_util/safety_checker.py b/invokeai/backend/image_util/safety_checker.py index ab09a296197..1f2acc58c56 100644 --- a/invokeai/backend/image_util/safety_checker.py +++ b/invokeai/backend/image_util/safety_checker.py @@ -9,7 +9,7 @@ import numpy as np from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker from PIL import Image, ImageFilter -from transformers import AutoFeatureExtractor +from transformers import AutoImageProcessor import invokeai.backend.util.logging as logger from invokeai.app.services.config.config_default import get_config @@ -36,14 +36,14 @@ def _load_safety_checker(cls): try: model_path = get_config().models_path / CHECKER_PATH if model_path.exists(): - cls.feature_extractor = AutoFeatureExtractor.from_pretrained(model_path) + cls.feature_extractor = AutoImageProcessor.from_pretrained(model_path) cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(model_path) else: model_path.mkdir(parents=True, exist_ok=True) - cls.feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id) - cls.feature_extractor.save_pretrained(model_path, safe_serialization=True) + cls.feature_extractor = AutoImageProcessor.from_pretrained(repo_id) + cls.feature_extractor.save_pretrained(model_path) cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(repo_id) - cls.safety_checker.save_pretrained(model_path, safe_serialization=True) + cls.safety_checker.save_pretrained(model_path) except Exception as e: logger.warning(f"Could not load NSFW checker: {str(e)}") diff --git a/invokeai/backend/model_manager/load/model_loaders/flux.py b/invokeai/backend/model_manager/load/model_loaders/flux.py index 2de51a8acae..be9f6e457e2 100644 --- a/invokeai/backend/model_manager/load/model_loaders/flux.py +++ b/invokeai/backend/model_manager/load/model_loaders/flux.py @@ -13,7 +13,7 @@ CLIPTextModel, CLIPTokenizer, T5EncoderModel, - T5TokenizerFast, + T5Tokenizer, ) from invokeai.app.services.config.config_default import get_config @@ -409,7 +409,7 @@ def _load_model( ) match submodel_type: case SubModelType.Tokenizer2 | SubModelType.Tokenizer3: - return T5TokenizerFast.from_pretrained( + return T5Tokenizer.from_pretrained( Path(config.path) / "tokenizer_2", max_length=512, local_files_only=True ) case SubModelType.TextEncoder2 | SubModelType.TextEncoder3: @@ -437,8 +437,11 @@ def _load_state_dict_into_t5(cls, model: T5EncoderModel, state_dict: dict[str, t missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False, assign=True) assert len(unexpected_keys) == 0 assert set(missing_keys) == {"encoder.embed_tokens.weight"} - # Assert that the layers we expect to be shared are actually shared. - assert model.encoder.embed_tokens.weight is model.shared.weight + # Re-tie shared weights. In transformers 5.x, weight tying is implemented at the + # parameter level (via _tie_weights / tie_weights) rather than as a Python object + # alias. load_state_dict(assign=True) replaces parameters in-place, which severs + # the parameter-level tie. Calling tie_weights() re-establishes it. + model.tie_weights() @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T5Encoder, format=ModelFormat.T5Encoder) @@ -455,7 +458,7 @@ def _load_model( match submodel_type: case SubModelType.Tokenizer2 | SubModelType.Tokenizer3: - return T5TokenizerFast.from_pretrained( + return T5Tokenizer.from_pretrained( Path(config.path) / "tokenizer_2", max_length=512, local_files_only=True ) case SubModelType.TextEncoder2 | SubModelType.TextEncoder3: diff --git a/invokeai/backend/model_manager/load/model_util.py b/invokeai/backend/model_manager/load/model_util.py index c3477fa6603..0c6c8f7f4ab 100644 --- a/invokeai/backend/model_manager/load/model_util.py +++ b/invokeai/backend/model_manager/load/model_util.py @@ -10,7 +10,7 @@ import torch from diffusers.pipelines.pipeline_utils import DiffusionPipeline from diffusers.schedulers.scheduling_utils import SchedulerMixin -from transformers import CLIPTokenizer, PreTrainedTokenizerBase, T5Tokenizer, T5TokenizerFast +from transformers import CLIPTokenizer, PreTrainedTokenizerBase, T5Tokenizer from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import DepthAnythingPipeline from invokeai.backend.image_util.grounding_dino.grounding_dino_pipeline import GroundingDinoPipeline @@ -64,10 +64,7 @@ def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int: return 0 elif isinstance( model, - ( - T5TokenizerFast, - T5Tokenizer, - ), + T5Tokenizer, ): # HACK(ryand): len(model) just returns the vocabulary size, so this is blatantly wrong. It should be small # relative to the text encoder that it's used with, so shouldn't matter too much, but we should fix this at some diff --git a/invokeai/backend/model_manager/metadata/fetch/huggingface.py b/invokeai/backend/model_manager/metadata/fetch/huggingface.py index 1b2b6c36742..0734a4e11af 100644 --- a/invokeai/backend/model_manager/metadata/fetch/huggingface.py +++ b/invokeai/backend/model_manager/metadata/fetch/huggingface.py @@ -16,10 +16,11 @@ import json import re from pathlib import Path +from types import SimpleNamespace from typing import Optional import requests -from huggingface_hub import HfApi, configure_http_backend, hf_hub_url +from huggingface_hub import HfApi, hf_hub_url from huggingface_hub.errors import RepositoryNotFoundError, RevisionNotFoundError from pydantic.networks import AnyHttpUrl from requests.sessions import Session @@ -47,7 +48,7 @@ def __init__(self, session: Optional[Session] = None): this module without an internet connection. """ self._requests = session or requests.Session() - configure_http_backend(backend_factory=lambda: self._requests) + self._has_custom_session = session is not None @classmethod def from_json(cls, json: str) -> HuggingFaceMetadata: @@ -55,6 +56,30 @@ def from_json(cls, json: str) -> HuggingFaceMetadata: metadata = HuggingFaceMetadata.model_validate_json(json) return metadata + def _model_info_via_session(self, repo_id: str, variant: Optional[ModelRepoVariant] = None) -> SimpleNamespace: + """Fetch model info using the injected requests session (for testing/custom backends).""" + params = {"blobs": "true"} + url = f"https://huggingface.co/api/models/{repo_id}" + if variant is not None: + url += f"/revision/{variant}" + resp = self._requests.get(url, params=params) + if resp.status_code == 404: + error_code = resp.headers.get("X-Error-Code", "") + if error_code == "RevisionNotFound" or (variant is not None): + raise RevisionNotFoundError(f"Revision '{variant}' not found for repo '{repo_id}'.") + raise RepositoryNotFoundError(f"Repository '{repo_id}' not found.") + resp.raise_for_status() + data = resp.json() + # Convert siblings dicts to SimpleNamespace objects matching HfApi.model_info() shape + siblings = [] + for s in data.get("siblings", []): + siblings.append(SimpleNamespace( + rfilename=s.get("rfilename"), + size=s.get("size") or (s.get("lfs", {}) or {}).get("size"), + lfs=s.get("lfs"), + )) + return SimpleNamespace(id=data["id"], siblings=siblings) + def from_id(self, id: str, variant: Optional[ModelRepoVariant] = None) -> AnyModelRepoMetadata: """Return a HuggingFaceMetadata object given the model's repo_id.""" # Little loop which tries fetching a revision corresponding to the selected variant. @@ -67,7 +92,12 @@ def from_id(self, id: str, variant: Optional[ModelRepoVariant] = None) -> AnyMod repo_id = id.split("::")[0] or id while not model_info: try: - model_info = HfApi().model_info(repo_id=repo_id, files_metadata=True, revision=variant) + # Use the injected session when provided (supports testing with mock adapters). + # Otherwise use HfApi which uses httpx internally. + if self._has_custom_session: + model_info = self._model_info_via_session(repo_id, variant) + else: + model_info = HfApi().model_info(repo_id=repo_id, files_metadata=True, revision=variant) except RepositoryNotFoundError as excp: raise UnknownMetadataException(f"'{repo_id}' not found. See trace for details.") from excp except RevisionNotFoundError: diff --git a/invokeai/backend/model_manager/metadata/metadata_base.py b/invokeai/backend/model_manager/metadata/metadata_base.py index e16ad4cbc47..b048144e547 100644 --- a/invokeai/backend/model_manager/metadata/metadata_base.py +++ b/invokeai/backend/model_manager/metadata/metadata_base.py @@ -17,7 +17,7 @@ from pathlib import Path from typing import List, Literal, Optional, Union -from huggingface_hub import configure_http_backend, hf_hub_url +from huggingface_hub import hf_hub_url from pydantic import BaseModel, Field, TypeAdapter from pydantic.networks import AnyHttpUrl from requests.sessions import Session @@ -111,7 +111,6 @@ def download_urls( full-precision model is returned. """ session = session or Session() - configure_http_backend(backend_factory=lambda: session) # used in testing paths = filter_files([x.path for x in self.files], variant, subfolder, subfolders) # all files in the model diff --git a/invokeai/backend/quantization/scripts/quantize_t5_xxl_bnb_llm_int8.py b/invokeai/backend/quantization/scripts/quantize_t5_xxl_bnb_llm_int8.py index 2e610404cdc..1bcb1227fe8 100644 --- a/invokeai/backend/quantization/scripts/quantize_t5_xxl_bnb_llm_int8.py +++ b/invokeai/backend/quantization/scripts/quantize_t5_xxl_bnb_llm_int8.py @@ -15,8 +15,11 @@ def load_state_dict_into_t5(model: T5EncoderModel, state_dict: dict): missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False, assign=True) assert len(unexpected_keys) == 0 assert set(missing_keys) == {"encoder.embed_tokens.weight"} - # Assert that the layers we expect to be shared are actually shared. - assert model.encoder.embed_tokens.weight is model.shared.weight + # Re-tie shared weights. In transformers 5.x, weight tying is implemented at the + # parameter level (via _tie_weights / tie_weights) rather than as a Python object + # alias. load_state_dict(assign=True) replaces parameters in-place, which severs + # the parameter-level tie. Calling tie_weights() re-establishes it. + model.tie_weights() def main(): diff --git a/invokeai/backend/stable_diffusion/diffusers_pipeline.py b/invokeai/backend/stable_diffusion/diffusers_pipeline.py index de5253f0733..054e04dcb28 100644 --- a/invokeai/backend/stable_diffusion/diffusers_pipeline.py +++ b/invokeai/backend/stable_diffusion/diffusers_pipeline.py @@ -17,7 +17,7 @@ from diffusers.schedulers.scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin from diffusers.utils.import_utils import is_xformers_available from pydantic import Field -from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer +from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from invokeai.app.services.config.config_default import get_config from invokeai.backend.stable_diffusion.diffusion.conditioning_data import IPAdapterData, TextConditioningData @@ -139,7 +139,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline): safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details. - feature_extractor ([`CLIPFeatureExtractor`]): + feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ @@ -151,7 +151,7 @@ def __init__( unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: Optional[StableDiffusionSafetyChecker], - feature_extractor: Optional[CLIPFeatureExtractor], + feature_extractor: Optional[CLIPImageProcessor], requires_safety_checker: bool = False, ): super().__init__( diff --git a/nodes/README.md b/nodes/README.md new file mode 100644 index 00000000000..d93bb65539c --- /dev/null +++ b/nodes/README.md @@ -0,0 +1,51 @@ +# Custom Nodes / Node Packs + +Copy your node packs to this directory. + +When nodes are added or changed, you must restart the app to see the changes. + +## Directory Structure + +For a node pack to be loaded, it must be placed in a directory alongside this +file. Here's an example structure: + +```py +. +├── __init__.py # Invoke-managed custom node loader +│ +├── cool_node +│ ├── __init__.py # see example below +│ └── cool_node.py +│ +└── my_node_pack + ├── __init__.py # see example below + ├── tasty_node.py + ├── bodacious_node.py + ├── utils.py + └── extra_nodes + └── fancy_node.py +``` + +## Node Pack `__init__.py` + +Each node pack must have an `__init__.py` file that imports its nodes. + +The structure of each node or node pack is otherwise not important. + +Here are examples, based on the example directory structure. + +### `cool_node/__init__.py` + +```py +from .cool_node import CoolInvocation +``` + +### `my_node_pack/__init__.py` + +```py +from .tasty_node import TastyInvocation +from .bodacious_node import BodaciousInvocation +from .extra_nodes.fancy_node import FancyInvocation +``` + +Only nodes imported in the `__init__.py` file are loaded. diff --git a/plan.md b/plan.md new file mode 100644 index 00000000000..476b09cfcf6 --- /dev/null +++ b/plan.md @@ -0,0 +1,171 @@ +# Regression Testing Plan: Transformers 5.1.0 + HuggingFace Hub Migration + +Below is a change-by-change plan. Each section explains **what changed, why, and how to test it**. Tests are ordered from quickest smoke tests to longer end-to-end runs. + +--- + +## Change 1: `CLIPFeatureExtractor` → `CLIPImageProcessor` + +**File:** `invokeai/backend/stable_diffusion/diffusers_pipeline.py` +**Why:** `CLIPFeatureExtractor` was removed in transformers 5.x in favour of `CLIPImageProcessor`. + +| # | Test | How | +|---|------|-----| +| 1a | **Import smoke test** | `python -c "from invokeai.backend.stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline"` — should not raise `ImportError` | +| 1b | **SD 1.5 text-to-image** | In the UI, load any SD 1.5 model and generate an image with a simple prompt (e.g. `"a cat on a couch"`). Confirm the image generates without errors. This exercises the full `StableDiffusionGeneratorPipeline` including the feature extractor type. | + +--- + +## Change 2: `AutoFeatureExtractor` → `AutoImageProcessor` + removed `safe_serialization` + +**File:** `invokeai/backend/image_util/safety_checker.py` +**Why:** All vision `FeatureExtractor` classes were removed in transformers 5.x. The `safe_serialization` parameter was also removed from `save_pretrained` (safetensors is now the only format). + +| # | Test | How | +|---|------|-----| +| 2a | **Import smoke test** | `python -c "from invokeai.backend.image_util.safety_checker import SafetyChecker"` | +| 2b | **NSFW checker first-time download** | Delete the local cache at `/models/core/convert/stable-diffusion-safety-checker/` if it exists. Enable the NSFW checker in config (`nsfw_checker: true`). Generate any SD 1.5 image. Confirm the safety checker downloads, saves to disk (no `safe_serialization` error), and the image either passes or is correctly blurred. | +| 2c | **NSFW checker cached load** | With the cache from 2b still present, generate another image. Confirm it loads from local path via `AutoImageProcessor.from_pretrained()` without re-downloading. | + +--- + +## Change 3: `T5TokenizerFast` → `T5Tokenizer` (4 files) + +**Files:** +- `invokeai/app/invocations/flux_text_encoder.py` +- `invokeai/app/invocations/sd3_text_encoder.py` +- `invokeai/backend/model_manager/load/model_util.py` +- `invokeai/backend/model_manager/load/model_loaders/flux.py` + +**Why:** Transformers 5.x unified slow/fast tokenizers — `T5TokenizerFast` no longer exists as a separate class. `T5Tokenizer` now uses the Rust backend by default. + +| # | Test | How | +|---|------|-----| +| 3a | **Import smoke tests** | Run each: `python -c "from invokeai.app.invocations.flux_text_encoder import FluxTextEncoderInvocation"`, same for `sd3_text_encoder`, `model_util`, and the flux loader module. | +| 3b | **FLUX text-to-image** | Load a FLUX model (e.g. FLUX.1-dev or schnell). Generate an image with prompt `"a lighthouse on a cliff at sunset"`. This exercises `T5Tokenizer.from_pretrained()` in the loader and the `isinstance(t5_tokenizer, T5Tokenizer)` assertion in the text encoder invocation. | +| 3c | **FLUX with long prompt (truncation path)** | Use a very long prompt (500+ words) with a FLUX model. Check that: the image generates, and the console shows a truncation warning (this exercises the tokenizer's truncation detection logic). | +| 3d | **SD3 text-to-image** | Load an SD3 model. Generate an image with a simple prompt. This covers the SD3 text encoder's T5 tokenization path and its `batch_decode` for truncation warnings. | +| 3e | **Model size calculation** | Load a FLUX or SD3 model and check that the model manager correctly reports the tokenizer memory footprint in the logs (exercises the `isinstance(model, T5Tokenizer)` path in `model_util.py`). No crash = pass. | + +--- + +## Change 4: `configure_http_backend` removed + session-aware metadata fetching + +**Files:** +- `invokeai/backend/model_manager/metadata/metadata_base.py` — removed `configure_http_backend` import and call +- `invokeai/backend/model_manager/metadata/fetch/huggingface.py` — removed `configure_http_backend`, added `_model_info_via_session()` fallback, added `_has_custom_session` flag + +**Why (root cause chain):** +1. `transformers>=5.1.0` pulls in `huggingface_hub>=1.0.0` as a dependency. +2. `huggingface_hub` 1.0 switched its HTTP backend from `requests` to `httpx` and removed the `configure_http_backend()` function entirely. +3. InvokeAI called `configure_http_backend(backend_factory=lambda: session)` in two places to inject a custom `requests.Session` — this was used in production for `download_urls()` and, critically, in tests to inject a `TestSession` with mock HTTP adapters so tests could run without real network calls. +4. Simply removing the calls fixed the import crash in production (since `HfApi()` now uses `httpx` internally and works fine for real HTTP). However, it broke the test suite: `HfApi().model_info()` now bypasses the mock `requests.TestSession` entirely and hits the real HuggingFace API, causing `RepositoryNotFoundError` for test-only repos like `InvokeAI-test/textual_inversion_tests`. +5. The fix: `HuggingFaceMetadataFetch` now tracks whether a custom session was injected (`_has_custom_session`). When true, `from_id()` calls a new `_model_info_via_session()` method that uses the injected `requests.Session` to query the HF API directly (matching the URL patterns the test mocks expect). When false (production), it uses `HfApi()` as before. + +| # | Test | How | +|---|------|-----| +| 4a | **Import smoke test** | `python -c "from invokeai.backend.model_manager.metadata.fetch import HuggingFaceMetadataFetch"` | +| 4b | **Automated test suite** | `pytest tests/app/services/model_install/test_model_install.py -x -v` — all 19 tests should pass, especially `test_heuristic_import_with_type`, `test_huggingface_install`, and `test_huggingface_repo_id` which depend on mock HF API responses via the injected session. | +| 4c | **Install a model from HuggingFace** | In the UI's Model Manager, add a model by HuggingFace repo ID (e.g. `stabilityai/sd-turbo`). Confirm the metadata (name, description, tags) is correctly fetched and displayed, and the model downloads successfully. This exercises the production `HfApi().model_info()` path, plus `hf_hub_url()` and `download_urls()`. | +| 4d | **Browse HF model metadata** | If the UI has a model info/details view, open it for an already-installed HF model and confirm metadata fields are populated. | + +--- + +## Change 5: `HfFolder` → `huggingface_hub.get_token()` + +**File:** `invokeai/app/services/model_install/model_install_default.py` +**Why:** `HfFolder` was removed in `huggingface_hub` 1.0+. The replacement is the top-level `get_token()` function. + +| # | Test | How | +|---|------|-----| +| 5a | **Import smoke test** | `python -c "from invokeai.app.services.model_install.model_install_default import ModelInstallService"` | +| 5b | **Install gated model (with token)** | If you have a HuggingFace account with an access token cached (`huggingface-cli login`), try installing a gated model (e.g. `black-forest-labs/FLUX.1-dev`). Confirm the token is automatically injected and the download succeeds. | +| 5c | **Install public model (no token)** | Without explicit token, install a public model. Confirm `get_token()` returns `None` gracefully and the install proceeds. | + +--- + +## Change 6: `transformers>=5.1.0` override for compel + +**File:** `pyproject.toml` — `override-dependencies` +**Why:** `compel==2.1.1` requires `transformers ~= 4.25` (`<5.0`). The uv override forces past this constraint. + +| # | Test | How | +|---|------|-----| +| 6a | **SD 1.5 prompt weights** | Generate an image with weighted prompts: `"a (red:1.5) car on a (blue:0.5) road"`. Compare to an unweighted `"a red car on a blue road"`. The weighted version should show noticeably more red and less blue. This is the core compel functionality. | +| 6b | **SD 1.5 negative prompts** | Generate with prompt `"a photo of a dog"` and negative prompt `"blurry, low quality"`. Confirm it generates without crash. | +| 6c | **SDXL prompt weights** | Same as 6a but with an SDXL model. SDXL uses a different compel path (`SDXLCompelPromptInvocation`). | +| 6d | **Prompt blending (compel syntax)** | Try compel blend syntax if supported: `"a photo of a cat".blend("a photo of a dog", 0.5)` or `("a cat", "a dog").blend(0.5, 0.5)`. This exercises deeper compel internals. | + +--- + +## Change 7: T5 shared-weight assertion → `model.tie_weights()` + +**Files:** +- `invokeai/backend/model_manager/load/model_loaders/flux.py` — `_load_state_dict_into_t5()` classmethod +- `invokeai/backend/quantization/scripts/quantize_t5_xxl_bnb_llm_int8.py` — `load_state_dict_into_t5()` function + +**Why (root cause chain):** +1. T5 models have a shared weight: `model.shared.weight` and `model.encoder.embed_tokens.weight` should refer to the same tensor. +2. In **transformers 4.x**, this sharing was implemented as a Python object alias — both attributes literally pointed to the same `nn.Parameter` object, so `a is b` was `True`. +3. In **transformers 5.x**, weight tying is implemented at the **parameter level** via `_tie_weights()` / `tie_weights()`. The two attributes may be distinct `nn.Parameter` objects that are kept in sync by the framework, so `a is b` can be `False`. +4. InvokeAI calls `model.load_state_dict(state_dict, strict=False, assign=True)`. The `assign=True` flag replaces parameters in-place rather than copying data into existing tensors. This severs even the parameter-level tie that transformers 5.x establishes. +5. The old code then asserted `model.encoder.embed_tokens.weight is model.shared.weight`, which was guaranteed `True` in 4.x but fails in 5.x after `assign=True`. +6. **Fix:** Replace the identity assertion with `model.tie_weights()`, which re-establishes the tie regardless of how it is internally implemented. This is forward-compatible and is the officially recommended approach. + +| # | Test | How | +|---|------|-----| +| 7a | **Import smoke test** | `python -c "from invokeai.backend.model_manager.load.model_loaders.flux import FluxBnbQuantizednf4bCheckpointModel"` | +| 7b | **FLUX text-to-image** | Load a FLUX model and generate an image. This is the primary code path that calls `_load_state_dict_into_t5()`. The generation should complete without `AssertionError`. (Same as test 3b — this change and Change 3 are both exercised together.) | +| 7c | **FLUX BnB quantized model** | If you have a BnB-quantized FLUX model, load and generate with it. This exercises the `FluxBnbQuantizednf4bCheckpointModel` loader which also calls `_load_state_dict_into_t5()`. | +| 7d | **Quantize script (manual)** | If you need to re-quantize a T5 model: run `quantize_t5_xxl_bnb_llm_int8.py` and confirm it completes without assertion errors. | + +--- + +## Change 8: `HFTokenHelper.get_status()` — null token guard for `get_token_permission()` + +**File:** `invokeai/app/api/routers/model_manager.py` +**Why (root cause chain):** +1. `HFTokenHelper.get_status()` calls `huggingface_hub.get_token_permission(huggingface_hub.get_token())` to check whether a valid HF token is present. +2. When no token is configured, `get_token()` returns `None`. +3. In **huggingface_hub <1.0**, `get_token_permission(None)` returned a falsy value, so the code fell through to `return HFTokenStatus.INVALID` — correct behavior. +4. In **huggingface_hub 1.0+**, `get_token_permission(None)` **raises an exception** (it now validates the input and rejects `None`). +5. The `except Exception` catch returned `HFTokenStatus.UNKNOWN`, which the frontend interprets as a network error, showing the misleading message: *"Unable to Verify HF Token — Unable to verify HuggingFace token. This is likely due to a network error."* +6. **Fix:** Check `get_token()` for `None` first and return `INVALID` immediately, before ever calling `get_token_permission()`. This restores the correct "no token" UI message. + +| # | Test | How | +|---|------|-----| +| 8a | **No token → INVALID status** | Remove/rename your HF token file (`~/.cache/huggingface/token`), clear `$env:HF_TOKEN`, restart InvokeAI. The UI should show the proper "no token" message, **not** the "unable to verify / network error" message. | +| 8b | **Valid token → VALID status** | Restore your token (`huggingface-cli login`), restart InvokeAI. The UI should show the token as valid. | +| 8c | **Install gated model without token** | With no token, try to install a gated model (e.g. `black-forest-labs/FLUX.1-dev`). The UI should clearly indicate a token is needed, not a network error. | + +--- + +## Automated Test Suite + +| # | Command | What it covers | +|---|---------|----------------| +| A1 | `pytest ./tests -x -m "not slow"` | Run the full fast test suite. Any existing tests that touch model loading, metadata, or imports will catch regressions. The `-x` flag stops on first failure for quick feedback. | +| A2 | `pytest ./tests -x -m "slow"` | Run slow tests (if you have models available). These likely include integration tests. | + +--- + +## Quick Smoke Test Script (all imports at once) + +Run this to verify none of the changed files crash on import: + +```python +python -c " +from invokeai.backend.stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline +from invokeai.backend.image_util.safety_checker import SafetyChecker +from invokeai.app.invocations.flux_text_encoder import FluxTextEncoderInvocation +from invokeai.app.invocations.sd3_text_encoder import Sd3TextEncoderInvocation +from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data +from invokeai.backend.model_manager.load.model_loaders.flux import FluxBnbQuantizednf4bCheckpointModel +from invokeai.backend.model_manager.metadata.metadata_base import HuggingFaceMetadata +from invokeai.backend.model_manager.metadata.fetch import HuggingFaceMetadataFetch +from invokeai.app.services.model_install.model_install_default import ModelInstallService +print('All imports OK') +" +``` + +If this prints `All imports OK`, you've passed the baseline. Then proceed to the UI-based tests in priority order: **6a → 3b/7b → 3d → 4b → 5c → 2b → 1b**. diff --git a/pyproject.toml b/pyproject.toml index adfe5982baf..2fac347f320 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -49,7 +49,7 @@ dependencies = [ "torch~=2.7.0", # torch and related dependencies are loosely pinned, will respect requirement of `diffusers[torch]` "torchsde", # diffusers needs this for SDE solvers, but it is not an explicit dep of diffusers "torchvision", - "transformers>=4.56.0", + "transformers>=5.1.0", # Core application dependencies, pinned for reproducible builds. "fastapi-events", @@ -123,7 +123,8 @@ dependencies = [ [tool.uv] # Prevent opencv-python from ever being chosen during dependency resolution. # This prevents conflicts with opencv-contrib-python, which Invoke requires. -override-dependencies = ["opencv-python; sys_platform=='never'"] +# Force transformers>=5.1.0 past compel==2.1.1's ~=4.25 (<5.0) constraint. +override-dependencies = ["opencv-python; sys_platform=='never'", "transformers>=5.1.0"] conflicts = [[{ extra = "cpu" }, { extra = "cuda" }, { extra = "rocm" }]] index-strategy = "unsafe-best-match" From f69f22ccea2896f6c1bb5ede20fd607511f4711f Mon Sep 17 00:00:00 2001 From: Your Name Date: Fri, 6 Feb 2026 20:12:01 -0500 Subject: [PATCH 02/33] remove extra stuff --- .gitignore | 7 +- invokeai.example.yaml | 62 --------------- invokeai.yaml | 4 - nodes/README.md | 51 ------------- plan.md | 171 ------------------------------------------ 5 files changed, 1 insertion(+), 294 deletions(-) delete mode 100644 invokeai.example.yaml delete mode 100644 invokeai.yaml delete mode 100644 nodes/README.md delete mode 100644 plan.md diff --git a/.gitignore b/.gitignore index 1bb2f412ffd..3e2a5bc7663 100644 --- a/.gitignore +++ b/.gitignore @@ -194,9 +194,4 @@ installer/InvokeAI-Installer/ .claude/ # Weblate configuration file -weblate.ini - -models/ -databases/ -configs/ -outputs/ \ No newline at end of file +weblate.ini \ No newline at end of file diff --git a/invokeai.example.yaml b/invokeai.example.yaml deleted file mode 100644 index 654964ddd97..00000000000 --- a/invokeai.example.yaml +++ /dev/null @@ -1,62 +0,0 @@ -# This is an example file with default and example settings. -# You should not copy this whole file into your config. -# Only add the settings you need to change to your config file. - -# Internal metadata - do not edit: -schema_version: 4.0.2 - -# Put user settings here - see https://invoke-ai.github.io/InvokeAI/configuration/: -host: 127.0.0.1 -port: 9090 -allow_origins: [] -allow_credentials: true -allow_methods: -- '*' -allow_headers: -- '*' -log_tokenization: false -patchmatch: true -models_dir: models -convert_cache_dir: models\.convert_cache -download_cache_dir: models\.download_cache -legacy_conf_dir: configs -db_dir: databases -outputs_dir: outputs -custom_nodes_dir: nodes -style_presets_dir: style_presets -workflow_thumbnails_dir: workflow_thumbnails -log_handlers: -- console -log_format: color -log_level: info -log_sql: false -log_level_network: warning -use_memory_db: false -dev_reload: false -profile_graphs: false -profiles_dir: profiles -log_memory_usage: false -model_cache_keep_alive_min: 0.0 -device_working_mem_gb: 3.0 -enable_partial_loading: false -keep_ram_copy_of_weights: true -lazy_offload: true -device: auto -precision: auto -sequential_guidance: false -attention_type: auto -attention_slice_size: auto -force_tiled_decode: false -pil_compress_level: 1 -max_queue_size: 10000 -clear_queue_on_startup: false -node_cache_size: 512 -hashing_algorithm: blake3_single -remote_api_tokens: -- url_regex: cool-models.com - token: my_secret_token -- url_regex: nifty-models.com - token: some_other_token -scan_models_on_startup: false -unsafe_disable_picklescan: false -allow_unknown_models: true diff --git a/invokeai.yaml b/invokeai.yaml deleted file mode 100644 index f9b7a42a5dd..00000000000 --- a/invokeai.yaml +++ /dev/null @@ -1,4 +0,0 @@ -# Internal metadata - do not edit: -schema_version: 4.0.2 -enable_partial_loading: true -# Put user settings here - see https://invoke-ai.github.io/InvokeAI/configuration/: diff --git a/nodes/README.md b/nodes/README.md deleted file mode 100644 index d93bb65539c..00000000000 --- a/nodes/README.md +++ /dev/null @@ -1,51 +0,0 @@ -# Custom Nodes / Node Packs - -Copy your node packs to this directory. - -When nodes are added or changed, you must restart the app to see the changes. - -## Directory Structure - -For a node pack to be loaded, it must be placed in a directory alongside this -file. Here's an example structure: - -```py -. -├── __init__.py # Invoke-managed custom node loader -│ -├── cool_node -│ ├── __init__.py # see example below -│ └── cool_node.py -│ -└── my_node_pack - ├── __init__.py # see example below - ├── tasty_node.py - ├── bodacious_node.py - ├── utils.py - └── extra_nodes - └── fancy_node.py -``` - -## Node Pack `__init__.py` - -Each node pack must have an `__init__.py` file that imports its nodes. - -The structure of each node or node pack is otherwise not important. - -Here are examples, based on the example directory structure. - -### `cool_node/__init__.py` - -```py -from .cool_node import CoolInvocation -``` - -### `my_node_pack/__init__.py` - -```py -from .tasty_node import TastyInvocation -from .bodacious_node import BodaciousInvocation -from .extra_nodes.fancy_node import FancyInvocation -``` - -Only nodes imported in the `__init__.py` file are loaded. diff --git a/plan.md b/plan.md deleted file mode 100644 index 476b09cfcf6..00000000000 --- a/plan.md +++ /dev/null @@ -1,171 +0,0 @@ -# Regression Testing Plan: Transformers 5.1.0 + HuggingFace Hub Migration - -Below is a change-by-change plan. Each section explains **what changed, why, and how to test it**. Tests are ordered from quickest smoke tests to longer end-to-end runs. - ---- - -## Change 1: `CLIPFeatureExtractor` → `CLIPImageProcessor` - -**File:** `invokeai/backend/stable_diffusion/diffusers_pipeline.py` -**Why:** `CLIPFeatureExtractor` was removed in transformers 5.x in favour of `CLIPImageProcessor`. - -| # | Test | How | -|---|------|-----| -| 1a | **Import smoke test** | `python -c "from invokeai.backend.stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline"` — should not raise `ImportError` | -| 1b | **SD 1.5 text-to-image** | In the UI, load any SD 1.5 model and generate an image with a simple prompt (e.g. `"a cat on a couch"`). Confirm the image generates without errors. This exercises the full `StableDiffusionGeneratorPipeline` including the feature extractor type. | - ---- - -## Change 2: `AutoFeatureExtractor` → `AutoImageProcessor` + removed `safe_serialization` - -**File:** `invokeai/backend/image_util/safety_checker.py` -**Why:** All vision `FeatureExtractor` classes were removed in transformers 5.x. The `safe_serialization` parameter was also removed from `save_pretrained` (safetensors is now the only format). - -| # | Test | How | -|---|------|-----| -| 2a | **Import smoke test** | `python -c "from invokeai.backend.image_util.safety_checker import SafetyChecker"` | -| 2b | **NSFW checker first-time download** | Delete the local cache at `/models/core/convert/stable-diffusion-safety-checker/` if it exists. Enable the NSFW checker in config (`nsfw_checker: true`). Generate any SD 1.5 image. Confirm the safety checker downloads, saves to disk (no `safe_serialization` error), and the image either passes or is correctly blurred. | -| 2c | **NSFW checker cached load** | With the cache from 2b still present, generate another image. Confirm it loads from local path via `AutoImageProcessor.from_pretrained()` without re-downloading. | - ---- - -## Change 3: `T5TokenizerFast` → `T5Tokenizer` (4 files) - -**Files:** -- `invokeai/app/invocations/flux_text_encoder.py` -- `invokeai/app/invocations/sd3_text_encoder.py` -- `invokeai/backend/model_manager/load/model_util.py` -- `invokeai/backend/model_manager/load/model_loaders/flux.py` - -**Why:** Transformers 5.x unified slow/fast tokenizers — `T5TokenizerFast` no longer exists as a separate class. `T5Tokenizer` now uses the Rust backend by default. - -| # | Test | How | -|---|------|-----| -| 3a | **Import smoke tests** | Run each: `python -c "from invokeai.app.invocations.flux_text_encoder import FluxTextEncoderInvocation"`, same for `sd3_text_encoder`, `model_util`, and the flux loader module. | -| 3b | **FLUX text-to-image** | Load a FLUX model (e.g. FLUX.1-dev or schnell). Generate an image with prompt `"a lighthouse on a cliff at sunset"`. This exercises `T5Tokenizer.from_pretrained()` in the loader and the `isinstance(t5_tokenizer, T5Tokenizer)` assertion in the text encoder invocation. | -| 3c | **FLUX with long prompt (truncation path)** | Use a very long prompt (500+ words) with a FLUX model. Check that: the image generates, and the console shows a truncation warning (this exercises the tokenizer's truncation detection logic). | -| 3d | **SD3 text-to-image** | Load an SD3 model. Generate an image with a simple prompt. This covers the SD3 text encoder's T5 tokenization path and its `batch_decode` for truncation warnings. | -| 3e | **Model size calculation** | Load a FLUX or SD3 model and check that the model manager correctly reports the tokenizer memory footprint in the logs (exercises the `isinstance(model, T5Tokenizer)` path in `model_util.py`). No crash = pass. | - ---- - -## Change 4: `configure_http_backend` removed + session-aware metadata fetching - -**Files:** -- `invokeai/backend/model_manager/metadata/metadata_base.py` — removed `configure_http_backend` import and call -- `invokeai/backend/model_manager/metadata/fetch/huggingface.py` — removed `configure_http_backend`, added `_model_info_via_session()` fallback, added `_has_custom_session` flag - -**Why (root cause chain):** -1. `transformers>=5.1.0` pulls in `huggingface_hub>=1.0.0` as a dependency. -2. `huggingface_hub` 1.0 switched its HTTP backend from `requests` to `httpx` and removed the `configure_http_backend()` function entirely. -3. InvokeAI called `configure_http_backend(backend_factory=lambda: session)` in two places to inject a custom `requests.Session` — this was used in production for `download_urls()` and, critically, in tests to inject a `TestSession` with mock HTTP adapters so tests could run without real network calls. -4. Simply removing the calls fixed the import crash in production (since `HfApi()` now uses `httpx` internally and works fine for real HTTP). However, it broke the test suite: `HfApi().model_info()` now bypasses the mock `requests.TestSession` entirely and hits the real HuggingFace API, causing `RepositoryNotFoundError` for test-only repos like `InvokeAI-test/textual_inversion_tests`. -5. The fix: `HuggingFaceMetadataFetch` now tracks whether a custom session was injected (`_has_custom_session`). When true, `from_id()` calls a new `_model_info_via_session()` method that uses the injected `requests.Session` to query the HF API directly (matching the URL patterns the test mocks expect). When false (production), it uses `HfApi()` as before. - -| # | Test | How | -|---|------|-----| -| 4a | **Import smoke test** | `python -c "from invokeai.backend.model_manager.metadata.fetch import HuggingFaceMetadataFetch"` | -| 4b | **Automated test suite** | `pytest tests/app/services/model_install/test_model_install.py -x -v` — all 19 tests should pass, especially `test_heuristic_import_with_type`, `test_huggingface_install`, and `test_huggingface_repo_id` which depend on mock HF API responses via the injected session. | -| 4c | **Install a model from HuggingFace** | In the UI's Model Manager, add a model by HuggingFace repo ID (e.g. `stabilityai/sd-turbo`). Confirm the metadata (name, description, tags) is correctly fetched and displayed, and the model downloads successfully. This exercises the production `HfApi().model_info()` path, plus `hf_hub_url()` and `download_urls()`. | -| 4d | **Browse HF model metadata** | If the UI has a model info/details view, open it for an already-installed HF model and confirm metadata fields are populated. | - ---- - -## Change 5: `HfFolder` → `huggingface_hub.get_token()` - -**File:** `invokeai/app/services/model_install/model_install_default.py` -**Why:** `HfFolder` was removed in `huggingface_hub` 1.0+. The replacement is the top-level `get_token()` function. - -| # | Test | How | -|---|------|-----| -| 5a | **Import smoke test** | `python -c "from invokeai.app.services.model_install.model_install_default import ModelInstallService"` | -| 5b | **Install gated model (with token)** | If you have a HuggingFace account with an access token cached (`huggingface-cli login`), try installing a gated model (e.g. `black-forest-labs/FLUX.1-dev`). Confirm the token is automatically injected and the download succeeds. | -| 5c | **Install public model (no token)** | Without explicit token, install a public model. Confirm `get_token()` returns `None` gracefully and the install proceeds. | - ---- - -## Change 6: `transformers>=5.1.0` override for compel - -**File:** `pyproject.toml` — `override-dependencies` -**Why:** `compel==2.1.1` requires `transformers ~= 4.25` (`<5.0`). The uv override forces past this constraint. - -| # | Test | How | -|---|------|-----| -| 6a | **SD 1.5 prompt weights** | Generate an image with weighted prompts: `"a (red:1.5) car on a (blue:0.5) road"`. Compare to an unweighted `"a red car on a blue road"`. The weighted version should show noticeably more red and less blue. This is the core compel functionality. | -| 6b | **SD 1.5 negative prompts** | Generate with prompt `"a photo of a dog"` and negative prompt `"blurry, low quality"`. Confirm it generates without crash. | -| 6c | **SDXL prompt weights** | Same as 6a but with an SDXL model. SDXL uses a different compel path (`SDXLCompelPromptInvocation`). | -| 6d | **Prompt blending (compel syntax)** | Try compel blend syntax if supported: `"a photo of a cat".blend("a photo of a dog", 0.5)` or `("a cat", "a dog").blend(0.5, 0.5)`. This exercises deeper compel internals. | - ---- - -## Change 7: T5 shared-weight assertion → `model.tie_weights()` - -**Files:** -- `invokeai/backend/model_manager/load/model_loaders/flux.py` — `_load_state_dict_into_t5()` classmethod -- `invokeai/backend/quantization/scripts/quantize_t5_xxl_bnb_llm_int8.py` — `load_state_dict_into_t5()` function - -**Why (root cause chain):** -1. T5 models have a shared weight: `model.shared.weight` and `model.encoder.embed_tokens.weight` should refer to the same tensor. -2. In **transformers 4.x**, this sharing was implemented as a Python object alias — both attributes literally pointed to the same `nn.Parameter` object, so `a is b` was `True`. -3. In **transformers 5.x**, weight tying is implemented at the **parameter level** via `_tie_weights()` / `tie_weights()`. The two attributes may be distinct `nn.Parameter` objects that are kept in sync by the framework, so `a is b` can be `False`. -4. InvokeAI calls `model.load_state_dict(state_dict, strict=False, assign=True)`. The `assign=True` flag replaces parameters in-place rather than copying data into existing tensors. This severs even the parameter-level tie that transformers 5.x establishes. -5. The old code then asserted `model.encoder.embed_tokens.weight is model.shared.weight`, which was guaranteed `True` in 4.x but fails in 5.x after `assign=True`. -6. **Fix:** Replace the identity assertion with `model.tie_weights()`, which re-establishes the tie regardless of how it is internally implemented. This is forward-compatible and is the officially recommended approach. - -| # | Test | How | -|---|------|-----| -| 7a | **Import smoke test** | `python -c "from invokeai.backend.model_manager.load.model_loaders.flux import FluxBnbQuantizednf4bCheckpointModel"` | -| 7b | **FLUX text-to-image** | Load a FLUX model and generate an image. This is the primary code path that calls `_load_state_dict_into_t5()`. The generation should complete without `AssertionError`. (Same as test 3b — this change and Change 3 are both exercised together.) | -| 7c | **FLUX BnB quantized model** | If you have a BnB-quantized FLUX model, load and generate with it. This exercises the `FluxBnbQuantizednf4bCheckpointModel` loader which also calls `_load_state_dict_into_t5()`. | -| 7d | **Quantize script (manual)** | If you need to re-quantize a T5 model: run `quantize_t5_xxl_bnb_llm_int8.py` and confirm it completes without assertion errors. | - ---- - -## Change 8: `HFTokenHelper.get_status()` — null token guard for `get_token_permission()` - -**File:** `invokeai/app/api/routers/model_manager.py` -**Why (root cause chain):** -1. `HFTokenHelper.get_status()` calls `huggingface_hub.get_token_permission(huggingface_hub.get_token())` to check whether a valid HF token is present. -2. When no token is configured, `get_token()` returns `None`. -3. In **huggingface_hub <1.0**, `get_token_permission(None)` returned a falsy value, so the code fell through to `return HFTokenStatus.INVALID` — correct behavior. -4. In **huggingface_hub 1.0+**, `get_token_permission(None)` **raises an exception** (it now validates the input and rejects `None`). -5. The `except Exception` catch returned `HFTokenStatus.UNKNOWN`, which the frontend interprets as a network error, showing the misleading message: *"Unable to Verify HF Token — Unable to verify HuggingFace token. This is likely due to a network error."* -6. **Fix:** Check `get_token()` for `None` first and return `INVALID` immediately, before ever calling `get_token_permission()`. This restores the correct "no token" UI message. - -| # | Test | How | -|---|------|-----| -| 8a | **No token → INVALID status** | Remove/rename your HF token file (`~/.cache/huggingface/token`), clear `$env:HF_TOKEN`, restart InvokeAI. The UI should show the proper "no token" message, **not** the "unable to verify / network error" message. | -| 8b | **Valid token → VALID status** | Restore your token (`huggingface-cli login`), restart InvokeAI. The UI should show the token as valid. | -| 8c | **Install gated model without token** | With no token, try to install a gated model (e.g. `black-forest-labs/FLUX.1-dev`). The UI should clearly indicate a token is needed, not a network error. | - ---- - -## Automated Test Suite - -| # | Command | What it covers | -|---|---------|----------------| -| A1 | `pytest ./tests -x -m "not slow"` | Run the full fast test suite. Any existing tests that touch model loading, metadata, or imports will catch regressions. The `-x` flag stops on first failure for quick feedback. | -| A2 | `pytest ./tests -x -m "slow"` | Run slow tests (if you have models available). These likely include integration tests. | - ---- - -## Quick Smoke Test Script (all imports at once) - -Run this to verify none of the changed files crash on import: - -```python -python -c " -from invokeai.backend.stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline -from invokeai.backend.image_util.safety_checker import SafetyChecker -from invokeai.app.invocations.flux_text_encoder import FluxTextEncoderInvocation -from invokeai.app.invocations.sd3_text_encoder import Sd3TextEncoderInvocation -from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data -from invokeai.backend.model_manager.load.model_loaders.flux import FluxBnbQuantizednf4bCheckpointModel -from invokeai.backend.model_manager.metadata.metadata_base import HuggingFaceMetadata -from invokeai.backend.model_manager.metadata.fetch import HuggingFaceMetadataFetch -from invokeai.app.services.model_install.model_install_default import ModelInstallService -print('All imports OK') -" -``` - -If this prints `All imports OK`, you've passed the baseline. Then proceed to the UI-based tests in priority order: **6a → 3b/7b → 3d → 4b → 5c → 2b → 1b**. From 43afb3758e9c60da3b1b774a1b7f849668a7454d Mon Sep 17 00:00:00 2001 From: Your Name Date: Fri, 29 May 2026 01:24:32 -0400 Subject: [PATCH 03/33] chore(deps): compel fork + transformers>=5.9.0 + remove override Switches compel from PyPI 2.1.1 to invoke-ai/compel@main fork which supports transformers 5.x. Bumps transformers floor to 5.9.0. Removes the transformers>=5.1.0 uv override that was only needed to bypass compel 2.1.1's <5.0 constraint. NOTE: compel fork pulls notebook dep (full Jupyter stack); flag to maintainer for cleanup. Co-Authored-By: Claude Opus 4.8 --- pyproject.toml | 7 +- uv.lock | 1142 +++++++++++++++++++++++++++++++++++++++++++----- 2 files changed, 1038 insertions(+), 111 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 2fac347f320..9b5c9ae2c4f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,7 +35,7 @@ dependencies = [ # Core generation dependencies, pinned for reproducible builds. "accelerate", "bitsandbytes; sys_platform!='darwin'", - "compel==2.1.1", + "compel @ git+https://github.com/invoke-ai/compel.git@main", "diffusers[torch]==0.36.0", "gguf", "mediapipe==0.10.14", # needed for "mediapipeface" controlnet model @@ -49,7 +49,7 @@ dependencies = [ "torch~=2.7.0", # torch and related dependencies are loosely pinned, will respect requirement of `diffusers[torch]` "torchsde", # diffusers needs this for SDE solvers, but it is not an explicit dep of diffusers "torchvision", - "transformers>=5.1.0", + "transformers>=5.9.0", # Core application dependencies, pinned for reproducible builds. 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Read it from there with a getattr fallback so the inv_freq buffer is computed from the configured base (1e6 / 256) instead of raising AttributeError. Applies to both the safetensors and GGUF Qwen3 encoder paths. Co-Authored-By: Claude Opus 4.8 --- .../model_manager/load/model_loaders/z_image.py | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/invokeai/backend/model_manager/load/model_loaders/z_image.py b/invokeai/backend/model_manager/load/model_loaders/z_image.py index aadced8f569..f712c6ebcd9 100644 --- a/invokeai/backend/model_manager/load/model_loaders/z_image.py +++ b/invokeai/backend/model_manager/load/model_loaders/z_image.py @@ -750,7 +750,13 @@ def _load_from_singlefile( # For rotary embeddings, this is inv_freq which is computed from config if buffer_name == "inv_freq": # Compute inv_freq from config (same logic as Qwen3RotaryEmbedding.__init__) - base = qwen_config.rope_theta + # NB: transformers 5.x moved rope_theta into the rope_parameters/rope_scaling dict + rope_params = ( + getattr(qwen_config, "rope_parameters", None) + or getattr(qwen_config, "rope_scaling", None) + or {} + ) + base = rope_params.get("rope_theta") or getattr(qwen_config, "rope_theta", 1000000.0) inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim)) parent.register_buffer(buffer_name, inv_freq.to(model_dtype), persistent=False) else: @@ -966,7 +972,13 @@ def _load_from_gguf( if buffer_name == "inv_freq": # Compute inv_freq from config - keep on CPU, cache system will move to GPU as needed - base = qwen_config.rope_theta + # NB: transformers 5.x moved rope_theta into the rope_parameters/rope_scaling dict + rope_params = ( + getattr(qwen_config, "rope_parameters", None) + or getattr(qwen_config, "rope_scaling", None) + or {} + ) + base = rope_params.get("rope_theta") or getattr(qwen_config, "rope_theta", 1000000.0) inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim)) parent.register_buffer(buffer_name, inv_freq.to(dtype=compute_dtype), persistent=False) else: From 69b36bad1cf671f8c134ca4cf04e65309fb6f1e7 Mon Sep 17 00:00:00 2001 From: Your Name Date: Fri, 29 May 2026 01:51:54 -0400 Subject: [PATCH 05/33] fix(model_manager): replace removed hf_hub get_token_permission with whoami huggingface_hub 1.x removed get_token_permission(). HFTokenHelper.get_status() now validates the token via whoami(), which returns user info for a valid token and raises HfHubHTTPError for an invalid one. Preserves the original three-way status: VALID on success, INVALID on HfHubHTTPError (e.g. 401), UNKNOWN on any other error (e.g. network failure). Co-Authored-By: Claude Opus 4.8 --- invokeai/app/api/routers/model_manager.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/invokeai/app/api/routers/model_manager.py b/invokeai/app/api/routers/model_manager.py index 81d4a927e83..7e5b26ee8ec 100644 --- a/invokeai/app/api/routers/model_manager.py +++ b/invokeai/app/api/routers/model_manager.py @@ -14,6 +14,7 @@ from fastapi import Body, Path, Query, Response, UploadFile from fastapi.responses import FileResponse, HTMLResponse from fastapi.routing import APIRouter +from huggingface_hub.errors import HfHubHTTPError from PIL import Image from pydantic import AnyHttpUrl, BaseModel, ConfigDict, Field from starlette.exceptions import HTTPException @@ -1048,12 +1049,17 @@ def get_status(cls) -> HFTokenStatus: if token is None: # No token set return HFTokenStatus.INVALID - if huggingface_hub.get_token_permission(token): - # Valid token! - return HFTokenStatus.VALID - # Token exists but has no permissions (shouldn't normally happen) + # get_token_permission() was removed in huggingface_hub 1.x. whoami() validates + # the token against the Hub: it returns user info for a valid token and raises + # HfHubHTTPError (e.g. 401) for an invalid one. + huggingface_hub.whoami(token=token) + # Valid token! + return HFTokenStatus.VALID + except HfHubHTTPError: + # Token is present but rejected by the Hub -> invalid return HFTokenStatus.INVALID except Exception: + # Network error or other unexpected failure -> unknown return HFTokenStatus.UNKNOWN @classmethod From 9fa57924e5e86ceac44d07e41d720b649011a908 Mon Sep 17 00:00:00 2001 From: Your Name Date: Fri, 29 May 2026 02:27:54 -0400 Subject: [PATCH 06/33] chore(deps): regenerate uv.lock after upstream merge Co-Authored-By: Claude Opus 4.8 --- uv.lock | 396 ++++++++++++++++---------------------------------------- 1 file changed, 109 insertions(+), 287 deletions(-) diff --git a/uv.lock b/uv.lock index 7166e367730..7e12a563b3d 100644 --- a/uv.lock +++ b/uv.lock @@ -223,15 +223,24 @@ wheels = [ ] [[package]] -name = "backrefs" -version = "5.9" +name = "bcrypt" +version = "3.2.2" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/eb/a7/312f673df6a79003279e1f55619abbe7daebbb87c17c976ddc0345c04c7b/backrefs-5.9.tar.gz", hash = "sha256:808548cb708d66b82ee231f962cb36faaf4f2baab032f2fbb783e9c2fdddaa59", size = 5765857, upload-time = "2025-06-22T19:34:13.97Z" } +dependencies = [ + { name = "cffi" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/e8/36/edc85ab295ceff724506252b774155eff8a238f13730c8b13badd33ef866/bcrypt-3.2.2.tar.gz", hash = "sha256:433c410c2177057705da2a9f2cd01dd157493b2a7ac14c8593a16b3dab6b6bfb", size = 42455, upload-time = "2022-05-01T17:58:52.348Z" } wheels = [ - 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Keep our v5 assertion (T5Tokenizer only) plus upstream's new t5_device logic, and drop the now-dead T5TokenizerFast monkeypatch in the test (the name no longer exists in the module). Co-Authored-By: Claude Opus 4.8 --- invokeai/app/invocations/sd3_text_encoder.py | 4 ---- tests/app/invocations/test_sd3_text_encoder.py | 1 - 2 files changed, 5 deletions(-) diff --git a/invokeai/app/invocations/sd3_text_encoder.py b/invokeai/app/invocations/sd3_text_encoder.py index d3236ea2f31..b9763e5a214 100644 --- a/invokeai/app/invocations/sd3_text_encoder.py +++ b/invokeai/app/invocations/sd3_text_encoder.py @@ -102,12 +102,8 @@ def _t5_encode(self, context: InvocationContext, max_seq_len: int) -> torch.Tens ): context.util.signal_progress("Running T5 encoder") assert isinstance(t5_text_encoder, T5EncoderModel) -<<<<<<< HEAD assert isinstance(t5_tokenizer, T5Tokenizer) -======= - assert isinstance(t5_tokenizer, (T5Tokenizer, T5TokenizerFast)) t5_device = get_effective_device(t5_text_encoder) ->>>>>>> upstream/main text_inputs = t5_tokenizer( prompt, diff --git a/tests/app/invocations/test_sd3_text_encoder.py b/tests/app/invocations/test_sd3_text_encoder.py index 560dcba43b0..b8c47a8373e 100644 --- a/tests/app/invocations/test_sd3_text_encoder.py +++ b/tests/app/invocations/test_sd3_text_encoder.py @@ -140,7 +140,6 @@ def test_sd3_t5_encode_uses_effective_device(monkeypatch): monkeypatch.setattr(f"{module_path}.T5EncoderModel", FakeSd3T5Encoder) monkeypatch.setattr(f"{module_path}.T5Tokenizer", FakeT5Tokenizer) - monkeypatch.setattr(f"{module_path}.T5TokenizerFast", FakeT5Tokenizer) invocation = Sd3TextEncoderInvocation.model_construct( clip_l=SimpleNamespace(text_encoder=SimpleNamespace(), tokenizer=SimpleNamespace(), loras=[]), From 6c7aedbcd3c3085f6840efd13832efefd56429c2 Mon Sep 17 00:00:00 2001 From: Your Name Date: Fri, 29 May 2026 03:34:06 -0400 Subject: [PATCH 08/33] style: ruff fixes on merge-resolved files - flux_text_encoder.py: drop unused typing.Union (F401) left by v5 import merge - huggingface.py: ruff format (wrap append(SimpleNamespace(...))) Co-Authored-By: Claude Opus 4.8 --- invokeai/app/invocations/flux_text_encoder.py | 2 +- .../model_manager/metadata/fetch/huggingface.py | 12 +++++++----- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/invokeai/app/invocations/flux_text_encoder.py b/invokeai/app/invocations/flux_text_encoder.py index 0d7a3f965a0..ea5841764db 100644 --- a/invokeai/app/invocations/flux_text_encoder.py +++ b/invokeai/app/invocations/flux_text_encoder.py @@ -1,5 +1,5 @@ from contextlib import ExitStack -from typing import Iterator, Literal, Optional, Tuple, Union +from typing import Iterator, Literal, Optional, Tuple import torch from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer diff --git a/invokeai/backend/model_manager/metadata/fetch/huggingface.py b/invokeai/backend/model_manager/metadata/fetch/huggingface.py index 0734a4e11af..08924bf182c 100644 --- a/invokeai/backend/model_manager/metadata/fetch/huggingface.py +++ b/invokeai/backend/model_manager/metadata/fetch/huggingface.py @@ -73,11 +73,13 @@ def _model_info_via_session(self, repo_id: str, variant: Optional[ModelRepoVaria # Convert siblings dicts to SimpleNamespace objects matching HfApi.model_info() shape siblings = [] for s in data.get("siblings", []): - siblings.append(SimpleNamespace( - rfilename=s.get("rfilename"), - size=s.get("size") or (s.get("lfs", {}) or {}).get("size"), - lfs=s.get("lfs"), - )) + siblings.append( + SimpleNamespace( + rfilename=s.get("rfilename"), + size=s.get("size") or (s.get("lfs", {}) or {}).get("size"), + lfs=s.get("lfs"), + ) + ) return SimpleNamespace(id=data["id"], siblings=siblings) def from_id(self, id: str, variant: Optional[ModelRepoVariant] = None) -> AnyModelRepoMetadata: From d5d71a6df5fd54d291c37264ea15388ed6914ec7 Mon Sep 17 00:00:00 2001 From: Your Name Date: Fri, 29 May 2026 05:14:07 -0400 Subject: [PATCH 09/33] chore(deps): pin transformers <5.6 (diffusers single-file CLIP incompat) transformers 5.6 flattened CLIPTextModel (removed the self.text_model wrapper, hoisted embeddings/encoder/final_layer_norm to the top level). diffusers' single-file checkpoint loader (create_diffusers_clip_model_from_ldm) still assumes the nested layout, so loading SD1.5 .safetensors checkpoints fails on 5.6+ with 'CLIPTextModel object has no attribute text_model' and, once that read is shimmed, 'Cannot copy out of meta tensor' (weights never populate the flattened model). Pin to >=5.5,<5.6 (last pre-flattening release) which keeps both the single-file and from_pretrained paths working. The invoke-ai/compel fork accepts any 5.x. Co-Authored-By: Claude Opus 4.8 --- pyproject.toml | 2 +- uv.lock | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index d5a3ef43dad..693bc2103d8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -49,7 +49,7 @@ dependencies = [ "torch~=2.7.0", # torch and related dependencies are loosely pinned, will respect requirement of `diffusers[torch]` "torchsde", # diffusers needs this for SDE solvers, but it is not an explicit dep of diffusers "torchvision", - "transformers>=5.9.0", + "transformers>=5.5,<5.6", # Core application dependencies, pinned for reproducible builds. "fastapi-events", diff --git a/uv.lock b/uv.lock index 7e12a563b3d..66f3cf89e98 100644 --- a/uv.lock +++ b/uv.lock @@ -1301,7 +1301,7 @@ requires-dist = [ { name = "torchvision", marker = "extra == 'cpu'", specifier = "==0.22.1+cpu", index = "https://download.pytorch.org/whl/cpu", conflict = { package = "invokeai", extra = "cpu" } }, { name = "torchvision", marker = "extra == 'cuda'", specifier = "==0.22.1+cu128", index = "https://download.pytorch.org/whl/cu128", conflict = { package = "invokeai", extra = "cuda" } }, { name = "torchvision", marker = "extra == 'rocm'", specifier = "==0.22.1+rocm6.3", index = "https://download.pytorch.org/whl/rocm6.3", conflict = { package = "invokeai", extra = "rocm" } }, - { name = "transformers", specifier = ">=5.9.0" }, + { name = "transformers", specifier = ">=5.5,<5.6" }, { name = "twine", marker = "extra == 'dist'" }, { name = "uvicorn", extras = ["standard"] }, { name = "xformers", marker = "sys_platform != 'darwin' and extra == 'xformers'", specifier = ">=0.0.28.post1" }, @@ -4326,7 +4326,7 @@ wheels = [ [[package]] name = "transformers" -version = "5.9.0" +version = "5.5.4" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "huggingface-hub" }, @@ -4339,9 +4339,9 @@ dependencies = [ { name = "tqdm" }, { name = "typer" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/51/58/7f843608f2e8421f86bb97060b54649be6239ec612b82bf9d41e65c26c00/transformers-5.9.0.tar.gz", hash = "sha256:25997cb8fa6053533171634b6162d7df54346530ec2aa9b42bb834e63668c842", size = 8642240, upload-time = "2026-05-20T14:50:49.278Z" } +sdist = { url = "https://files.pythonhosted.org/packages/a5/1e/1e244ab2ab50a863e6b52cc55761910567fa532b69a6740f6e99c5fdbd98/transformers-5.5.4.tar.gz", hash = "sha256:2e67cadba81fc7608cc07c4dd54f524820bc3d95b1cabd0ef3db7733c4f8b82e", size = 8227649, upload-time = "2026-04-13T16:55:55.181Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/02/ca/2eaa5359f2ccb8c2e1656bc26305ad0cf438aa392ce4b29ae67a315c186e/transformers-5.9.0-py3-none-any.whl", hash = "sha256:1d19509bcff7028ebc6b277d71caa712e8353778463d38764237d14b42b52788", size = 10787648, upload-time = "2026-05-20T14:50:45.337Z" }, + { url = "https://files.pythonhosted.org/packages/29/fb/162a66789c65e5afa3b051309240c26bf37fbc8fea285b4546ae747995a2/transformers-5.5.4-py3-none-any.whl", hash = "sha256:0bd6281b82966fe5a7a16f553ea517a9db1dee6284d7cb224dfd88fc0dd1c167", size = 10236696, upload-time = "2026-04-13T16:55:51.497Z" }, ] [[package]] From 5ba6e167b164b94745a17aef94ccb4461a8082c4 Mon Sep 17 00:00:00 2001 From: Your Name Date: Sat, 30 May 2026 15:02:08 -0400 Subject: [PATCH 10/33] @ chore(deps): replace compel fork with official compel 2.4.0 compel 2.4.0 (released 2026-05-30) merges the transformers-5 support that the invoke-ai fork carried (both descend from upstream PR #129), plus the maintainer-reviewed padding rework and added diffusers/T5 smoke coverage. Switch from the git fork to the PyPI release. - pyproject: compel git+main -> compel>=2.4.0,<3 - uv.lock: compel 2.3.1 (git 8f404b45) -> 2.4.0 (pypi) - transformers stays 5.5.4 (satisfies compel >=5,<6 and our <5.6 pin) Co-Authored-By: Claude Opus 4.8 @ --- pyproject.toml | 2 +- uv.lock | 10 +++++++--- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 693bc2103d8..1433ed3d22a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,7 +35,7 @@ dependencies = [ # Core generation dependencies, pinned for reproducible builds. "accelerate", "bitsandbytes; sys_platform!='darwin'", - "compel @ git+https://github.com/invoke-ai/compel.git@main", + "compel>=2.4.0,<3", "diffusers[torch]==0.37.0", "gguf", "mediapipe==0.10.14", # needed for "mediapipeface" controlnet model diff --git a/uv.lock b/uv.lock index 66f3cf89e98..09c4c0b82e0 100644 --- a/uv.lock +++ b/uv.lock @@ -537,8 +537,8 @@ wheels = [ [[package]] name = "compel" -version = "2.3.1" -source = { git = "https://github.com/invoke-ai/compel.git?rev=main#8f404b45ef620f37c454bc93cf35db1ddcb348e7" } +version = "2.4.0" +source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "diffusers" }, { name = "notebook" }, @@ -549,6 +549,10 @@ dependencies = [ { name = "torch", version = "2.7.1+rocm6.3", source = { registry = "https://download.pytorch.org/whl/rocm6.3" }, marker = "(extra == 'extra-8-invokeai-cpu' and extra == 'extra-8-invokeai-cuda') or (extra != 'extra-8-invokeai-cuda' and extra == 'extra-8-invokeai-rocm') or (extra != 'extra-8-invokeai-cpu' and extra == 'extra-8-invokeai-rocm')" }, { name = "transformers" }, ] +sdist = { url = "https://files.pythonhosted.org/packages/d3/7b/172a6c7fcba0f669c16e3ba3dc86e80fbdaa5b2a5a7ef60807ec1d43d5c4/compel-2.4.0.tar.gz", hash = "sha256:9d935d35bb4d16fa21d4b3b529d8b8f9dc784d67b44e42f46cd48c9dd321adc2", size = 54125, upload-time = "2026-05-30T16:07:48.477Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/76/3d/16a60bb74ad3430e99bd7db8c24a2810dcdc2dd60aec36c240ce1ceeabc7/compel-2.4.0-py3-none-any.whl", hash = "sha256:27a0ce5b540fba227c23ad1943f90128a9ee6266eda760e8cfb29fb9d041ac18", size = 36941, upload-time = "2026-05-30T16:07:47.184Z" }, +] [[package]] name = "contourpy" @@ -1237,7 +1241,7 @@ requires-dist = [ { name = "bcrypt", specifier = "<4.0.0" }, { name = "bitsandbytes", marker = "sys_platform != 'darwin'" }, { name = "blake3" }, - { name = "compel", git = "https://github.com/invoke-ai/compel.git?rev=main" }, + { name = "compel", specifier = ">=2.4.0,<3" }, { name = "deprecated" }, { name = "diffusers", extras = ["torch"], specifier = "==0.37.0" }, { name = "dnspython" }, From 1c6783c60c0cbb3fed8b65fbeae85924d7409468 Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Thu, 25 Jun 2026 03:59:08 +0200 Subject: [PATCH 11/33] feat(ideogram4): backend + model-manager registration for Ideogram 4 Vendor the Apache-2.0 Ideogram 4 reference model (DiT, FLUX2-style VAE, logit-normal flow-match scheduler, nf4/fp8 quant loading) into invokeai/backend/ideogram4/, plus InvokeAI glue (Qwen3-VL text encoding, packed-input build, dual-branch Euler denoise loop). Register the model: BaseModelType.Ideogram4, Main_Diffusers_Ideogram4_Config (detected via the Ideogram4Pipeline class name in model_index.json), and the Ideogram4DiffusersModel loader that loads both transformers as one Ideogram4TransformerPair submodel plus the Qwen3-VL encoder and VAE. Text-to-image only. --- invokeai/backend/ideogram4/NOTICE.md | 24 + invokeai/backend/ideogram4/__init__.py | 43 ++ invokeai/backend/ideogram4/autoencoder.py | 410 ++++++++++++++++++ invokeai/backend/ideogram4/constants.py | 11 + invokeai/backend/ideogram4/denoise.py | 127 ++++++ invokeai/backend/ideogram4/latent_norm.py | 272 ++++++++++++ .../backend/ideogram4/modeling_ideogram4.py | 379 ++++++++++++++++ .../backend/ideogram4/quantized_loading.py | 278 ++++++++++++ invokeai/backend/ideogram4/sampler_configs.py | 29 ++ invokeai/backend/ideogram4/sampling_utils.py | 132 ++++++ invokeai/backend/ideogram4/scheduler.py | 70 +++ invokeai/backend/ideogram4/text_encoding.py | 89 ++++ .../backend/ideogram4/transformer_pair.py | 25 ++ .../backend/model_manager/configs/factory.py | 2 + .../backend/model_manager/configs/main.py | 35 ++ .../load/model_loaders/ideogram4.py | 178 ++++++++ invokeai/backend/model_manager/taxonomy.py | 2 + 17 files changed, 2106 insertions(+) create mode 100644 invokeai/backend/ideogram4/NOTICE.md create mode 100644 invokeai/backend/ideogram4/__init__.py create mode 100644 invokeai/backend/ideogram4/autoencoder.py create mode 100644 invokeai/backend/ideogram4/constants.py create mode 100644 invokeai/backend/ideogram4/denoise.py create mode 100644 invokeai/backend/ideogram4/latent_norm.py create mode 100644 invokeai/backend/ideogram4/modeling_ideogram4.py create mode 100644 invokeai/backend/ideogram4/quantized_loading.py create mode 100644 invokeai/backend/ideogram4/sampler_configs.py create mode 100644 invokeai/backend/ideogram4/sampling_utils.py create mode 100644 invokeai/backend/ideogram4/scheduler.py create mode 100644 invokeai/backend/ideogram4/text_encoding.py create mode 100644 invokeai/backend/ideogram4/transformer_pair.py create mode 100644 invokeai/backend/model_manager/load/model_loaders/ideogram4.py diff --git a/invokeai/backend/ideogram4/NOTICE.md b/invokeai/backend/ideogram4/NOTICE.md new file mode 100644 index 00000000000..fa3907d6895 --- /dev/null +++ b/invokeai/backend/ideogram4/NOTICE.md @@ -0,0 +1,24 @@ +# Ideogram 4 vendored inference code + +The following modules in this package are adapted from the Ideogram 4 reference +implementation at https://github.com/ideogram-oss/ideogram4 (the `ideogram4` +Python package), which is licensed under the Apache License, Version 2.0: + +- `modeling_ideogram4.py` +- `autoencoder.py` +- `latent_norm.py` +- `scheduler.py` +- `sampler_configs.py` +- `constants.py` +- `quantized_loading.py` + +Copyright © Ideogram, Inc. Licensed under the Apache License, Version 2.0; a copy +is available at http://www.apache.org/licenses/LICENSE-2.0. + +Modifications by the InvokeAI project: intra-package import paths were rewritten +to `invokeai.backend.ideogram4.*`. The remaining modules in this package +(`conditioning.py`, `sampling_utils.py`, `denoise.py`, etc.) are original InvokeAI +code that wraps the vendored model for use in InvokeAI invocations. + +The Ideogram 4 model **weights** are NOT covered by this Apache license; they are +distributed under the separate "Ideogram Non-Commercial Model Agreement". diff --git a/invokeai/backend/ideogram4/__init__.py b/invokeai/backend/ideogram4/__init__.py new file mode 100644 index 00000000000..bd2331e669f --- /dev/null +++ b/invokeai/backend/ideogram4/__init__.py @@ -0,0 +1,43 @@ +"""Ideogram 4 backend. + +The model modules (``modeling_ideogram4``, ``autoencoder``, ``latent_norm``, +``scheduler``, ``sampler_configs``, ``constants``, ``quantized_loading``) are +adapted from the Apache-2.0 Ideogram 4 reference implementation +(https://github.com/ideogram-oss/ideogram4). See ``NOTICE.md``. The remaining +modules wrap that model for InvokeAI invocations. + +``quantized_loading`` is intentionally not re-exported here so that importing this +package does not eagerly import ``bitsandbytes``; import it directly where needed. +""" + +from invokeai.backend.ideogram4.denoise import run_ideogram4_denoise +from invokeai.backend.ideogram4.modeling_ideogram4 import Ideogram4Config, Ideogram4Transformer +from invokeai.backend.ideogram4.sampler_configs import PRESETS +from invokeai.backend.ideogram4.sampling_utils import ( + AE_SCALE_FACTOR, + LATENT_DIM, + PATCH_SIZE, + PIXELS_PER_IMAGE_TOKEN, + build_denoise_inputs, + pack_latents_to_grid, + unpatchify_and_denormalize, + validate_dimensions, +) +from invokeai.backend.ideogram4.text_encoding import MAX_TEXT_TOKENS, encode_qwen3vl_prompt + +__all__ = [ + "Ideogram4Config", + "Ideogram4Transformer", + "PRESETS", + "run_ideogram4_denoise", + "encode_qwen3vl_prompt", + "MAX_TEXT_TOKENS", + "build_denoise_inputs", + "pack_latents_to_grid", + "unpatchify_and_denormalize", + "validate_dimensions", + "AE_SCALE_FACTOR", + "LATENT_DIM", + "PATCH_SIZE", + "PIXELS_PER_IMAGE_TOKEN", +] diff --git a/invokeai/backend/ideogram4/autoencoder.py b/invokeai/backend/ideogram4/autoencoder.py new file mode 100644 index 00000000000..ba17ec939e2 --- /dev/null +++ b/invokeai/backend/ideogram4/autoencoder.py @@ -0,0 +1,410 @@ +"""Flux2 KL autoencoder.""" + +from __future__ import annotations + +import math +import re +from dataclasses import dataclass, field + +import torch +from einops import rearrange +from torch import Tensor, nn + + +@dataclass +class AutoEncoderParams: + resolution: int = 256 + in_channels: int = 3 + ch: int = 128 + out_ch: int = 3 + ch_mult: list[int] = field(default_factory=lambda: [1, 2, 4, 4]) + num_res_blocks: int = 2 + z_channels: int = 32 + + +def swish(x: Tensor) -> Tensor: + return x * torch.sigmoid(x) + + +class AttnBlock(nn.Module): + def __init__(self, in_channels: int): + super().__init__() + self.in_channels = in_channels + + self.norm = nn.GroupNorm( + num_groups=32, num_channels=in_channels, eps=1e-6, affine=True + ) + + self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1) + + def attention(self, h_: Tensor) -> Tensor: + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + b, c, h, w = q.shape + q = rearrange(q, "b c h w -> b 1 (h w) c").contiguous() + k = rearrange(k, "b c h w -> b 1 (h w) c").contiguous() + v = rearrange(v, "b c h w -> b 1 (h w) c").contiguous() + h_ = nn.functional.scaled_dot_product_attention(q, k, v) + + return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b) + + def forward(self, x: Tensor) -> Tensor: + return x + self.proj_out(self.attention(x)) + + +class ResnetBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + + self.norm1 = nn.GroupNorm( + num_groups=32, num_channels=in_channels, eps=1e-6, affine=True + ) + self.conv1 = nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + self.norm2 = nn.GroupNorm( + num_groups=32, num_channels=out_channels, eps=1e-6, affine=True + ) + self.conv2 = nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if self.in_channels != self.out_channels: + self.nin_shortcut = nn.Conv2d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x): + h = x + h = self.norm1(h) + h = swish(h) + h = self.conv1(h) + + h = self.norm2(h) + h = swish(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + x = self.nin_shortcut(x) + + return x + h + + +class Downsample(nn.Module): + def __init__(self, in_channels: int): + super().__init__() + # no asymmetric padding in torch conv, must do it ourselves + self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) + + def forward(self, x: Tensor): + pad = (0, 1, 0, 1) + x = nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + return x + + +class Upsample(nn.Module): + def __init__(self, in_channels: int): + super().__init__() + self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x: Tensor): + x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + x = self.conv(x) + return x + + +class Encoder(nn.Module): + def __init__( + self, + resolution: int, + in_channels: int, + ch: int, + ch_mult: list[int], + num_res_blocks: int, + z_channels: int, + ): + super().__init__() + self.quant_conv = torch.nn.Conv2d(2 * z_channels, 2 * z_channels, 1) + self.ch = ch + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + # downsampling + self.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + block_in = self.ch + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for _ in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out)) + block_in = block_out + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in) + + # end + self.norm_out = nn.GroupNorm( + num_groups=32, num_channels=block_in, eps=1e-6, affine=True + ) + self.conv_out = nn.Conv2d( + block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x: Tensor) -> Tensor: + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1]) # type: ignore[index, operator] + if len(self.down[i_level].attn) > 0: # type: ignore[arg-type] + h = self.down[i_level].attn[i_block](h) # type: ignore[index, operator] + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) # type: ignore[operator] + + # middle + h = hs[-1] + h = self.mid.block_1(h) # type: ignore[operator] + h = self.mid.attn_1(h) # type: ignore[operator] + h = self.mid.block_2(h) # type: ignore[operator] + # end + h = self.norm_out(h) + h = swish(h) + h = self.conv_out(h) + h = self.quant_conv(h) + return h + + +class Decoder(nn.Module): + def __init__( + self, + ch: int, + out_ch: int, + ch_mult: list[int], + num_res_blocks: int, + in_channels: int, + resolution: int, + z_channels: int, + ): + super().__init__() + self.post_quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1) + self.ch = ch + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.ffactor = 2 ** (self.num_resolutions - 1) + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + + # z to block_in + self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for _ in range(self.num_res_blocks + 1): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out)) + block_in = block_out + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = nn.GroupNorm( + num_groups=32, num_channels=block_in, eps=1e-6, affine=True + ) + self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) + + def forward(self, z: Tensor) -> Tensor: + z = self.post_quant_conv(z) + + # get dtype for proper tracing + upscale_dtype = next(self.up.parameters()).dtype + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h) # type: ignore[operator] + h = self.mid.attn_1(h) # type: ignore[operator] + h = self.mid.block_2(h) # type: ignore[operator] + + # cast to proper dtype + h = h.to(upscale_dtype) + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h) # type: ignore[index, operator] + if len(self.up[i_level].attn) > 0: # type: ignore[arg-type] + h = self.up[i_level].attn[i_block](h) # type: ignore[index, operator] + if i_level != 0: + h = self.up[i_level].upsample(h) # type: ignore[operator] + + # end + h = self.norm_out(h) + h = swish(h) + h = self.conv_out(h) + return h + + +class AutoEncoder(nn.Module): + def __init__(self, params: AutoEncoderParams): + super().__init__() + self.params = params + self.encoder = Encoder( + resolution=params.resolution, + in_channels=params.in_channels, + ch=params.ch, + ch_mult=params.ch_mult, + num_res_blocks=params.num_res_blocks, + z_channels=params.z_channels, + ) + self.decoder = Decoder( + resolution=params.resolution, + in_channels=params.in_channels, + ch=params.ch, + out_ch=params.out_ch, + ch_mult=params.ch_mult, + num_res_blocks=params.num_res_blocks, + z_channels=params.z_channels, + ) + + self.bn_eps = 1e-4 + self.bn_momentum = 0.1 + self.ps = [2, 2] + self.bn = torch.nn.BatchNorm2d( + math.prod(self.ps) * params.z_channels, + eps=self.bn_eps, + momentum=self.bn_momentum, + affine=False, + track_running_stats=True, + ) + + +_NUM_RESOLUTIONS = 4 + + +def convert_diffusers_state_dict(src: dict[str, Tensor]) -> dict[str, Tensor]: + out: dict[str, Tensor] = {} + attn_substrings = (".mid.attn_1.",) + for src_key, tensor in src.items(): + dst_key = _rewrite_diffusers_key(src_key) + if dst_key is None: + raise KeyError(f"Unrecognized diffusers VAE state-dict key: {src_key}") + if ( + any(s in dst_key for s in attn_substrings) + and dst_key.endswith(".weight") + and tensor.ndim == 2 + ): + tensor = tensor.unsqueeze(-1).unsqueeze(-1) + out[dst_key] = tensor + return out + + +def _rewrite_diffusers_key(key: str) -> str | None: + if key.startswith("bn."): + return key + + if key.startswith("quant_conv."): + return key.replace("quant_conv.", "encoder.quant_conv.", 1) + if key.startswith("post_quant_conv."): + return key.replace("post_quant_conv.", "decoder.post_quant_conv.", 1) + + if key == "encoder.conv_norm_out.weight": + return "encoder.norm_out.weight" + if key == "encoder.conv_norm_out.bias": + return "encoder.norm_out.bias" + if key == "decoder.conv_norm_out.weight": + return "decoder.norm_out.weight" + if key == "decoder.conv_norm_out.bias": + return "decoder.norm_out.bias" + + m = re.match(r"^(encoder|decoder)\.mid_block\.resnets\.(\d+)\.(.+)$", key) + if m: + side, idx, rest = m.group(1), int(m.group(2)), m.group(3) + rest = rest.replace("conv_shortcut", "nin_shortcut") + return f"{side}.mid.block_{idx + 1}.{rest}" + m = re.match(r"^(encoder|decoder)\.mid_block\.attentions\.0\.(.+)$", key) + if m: + side, rest = m.group(1), m.group(2) + rest = ( + rest.replace("group_norm.", "norm.") + .replace("to_q.", "q.") + .replace("to_k.", "k.") + .replace("to_v.", "v.") + .replace("to_out.0.", "proj_out.") + ) + return f"{side}.mid.attn_1.{rest}" + + m = re.match(r"^encoder\.down_blocks\.(\d+)\.resnets\.(\d+)\.(.+)$", key) + if m: + level, res_idx, rest = m.group(1), m.group(2), m.group(3) + rest = rest.replace("conv_shortcut", "nin_shortcut") + return f"encoder.down.{level}.block.{res_idx}.{rest}" + m = re.match(r"^encoder\.down_blocks\.(\d+)\.downsamplers\.0\.conv\.(.+)$", key) + if m: + return f"encoder.down.{m.group(1)}.downsample.conv.{m.group(2)}" + + m = re.match(r"^decoder\.up_blocks\.(\d+)\.resnets\.(\d+)\.(.+)$", key) + if m: + diffusers_idx = int(m.group(1)) + res_idx = m.group(2) + rest = m.group(3).replace("conv_shortcut", "nin_shortcut") + return f"decoder.up.{_NUM_RESOLUTIONS - 1 - diffusers_idx}.block.{res_idx}.{rest}" + m = re.match(r"^decoder\.up_blocks\.(\d+)\.upsamplers\.0\.conv\.(.+)$", key) + if m: + diffusers_idx = int(m.group(1)) + return ( + f"decoder.up.{_NUM_RESOLUTIONS - 1 - diffusers_idx}.upsample.conv.{m.group(2)}" + ) + + if key.startswith( + ("encoder.conv_in.", "encoder.conv_out.", "decoder.conv_in.", "decoder.conv_out.") + ): + return key + + return None diff --git a/invokeai/backend/ideogram4/constants.py b/invokeai/backend/ideogram4/constants.py new file mode 100644 index 00000000000..442929f2692 --- /dev/null +++ b/invokeai/backend/ideogram4/constants.py @@ -0,0 +1,11 @@ +SEQUENCE_PADDING_INDICATOR = -1 + +OUTPUT_IMAGE_INDICATOR = 2 +LLM_TOKEN_INDICATOR = 3 + +# Image grid coordinates start at this offset so they never collide with text token indices +# (text positions start at 0 and never exceed max_text_tokens, which is well below this). +IMAGE_POSITION_OFFSET = 65536 + +# Layers of Qwen3-VL whose hidden states are concatenated and fed to the transformer. +QWEN3_VL_ACTIVATION_LAYERS = (0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 35) diff --git a/invokeai/backend/ideogram4/denoise.py b/invokeai/backend/ideogram4/denoise.py new file mode 100644 index 00000000000..c39ab4ed692 --- /dev/null +++ b/invokeai/backend/ideogram4/denoise.py @@ -0,0 +1,127 @@ +"""Ideogram 4 denoising loop. + +Ports ``Ideogram4Pipeline.__call__``'s sampling loop, decoupled from model loading +and text encoding. Runs the Euler flow-matching loop with dual-branch asymmetric +CFG: the conditional transformer over the packed ``[text][image]`` sequence and the +unconditional transformer over image-only tokens with zeroed conditioning. +""" + +from __future__ import annotations + +from typing import Callable, Optional, Sequence + +import torch + +from invokeai.backend.ideogram4.modeling_ideogram4 import Ideogram4Transformer +from invokeai.backend.ideogram4.sampling_utils import ( + LATENT_DIM, + build_denoise_inputs, + pack_latents_to_grid, +) +from invokeai.backend.ideogram4.scheduler import get_schedule_for_resolution, make_step_intervals + +# Called after each completed step with (step_index, total_steps, latents). +StepCallback = Callable[[int, int, torch.Tensor], None] + + +@torch.no_grad() +def run_ideogram4_denoise( + *, + conditional_transformer: Ideogram4Transformer, + unconditional_transformer: Ideogram4Transformer, + llm_features: torch.Tensor, + height: int, + width: int, + num_steps: int, + mu: float, + std: float, + guidance_schedule: Optional[Sequence[float]] = None, + guidance_scale: float = 7.0, + seed: Optional[int] = None, + device: torch.device, + step_callback: Optional[StepCallback] = None, +) -> torch.Tensor: + """Sample latents for a single image. + + Args: + conditional_transformer / unconditional_transformer: the two DiT branches. + llm_features: ``(num_text_tokens, 53248)`` text conditioning on ``device``. + guidance_schedule: per-step guidance weights in loop-INDEX order (index 0 is + the last/polish step), length ``num_steps``. Falls back to a constant + ``guidance_scale`` when ``None``. + + Returns: + Packed latents ``(1, LATENT_DIM, grid_h, grid_w)``. + """ + num_text_tokens = int(llm_features.shape[0]) + llm_dim = int(llm_features.shape[-1]) + + inputs = build_denoise_inputs(num_text_tokens, height, width, device) + num_image_tokens = inputs["num_image_tokens"] + grid_h, grid_w = inputs["grid_h"], inputs["grid_w"] + + schedule = get_schedule_for_resolution((height, width), known_mean=mu, std=std) + step_intervals = make_step_intervals(num_steps).to(device) + + if guidance_schedule is not None: + gw_per_step = torch.as_tensor(guidance_schedule, dtype=torch.float32, device=device) + if gw_per_step.shape != (num_steps,): + raise ValueError( + f"guidance_schedule must have length {num_steps}, got {tuple(gw_per_step.shape)}" + ) + else: + gw_per_step = torch.full((num_steps,), float(guidance_scale), dtype=torch.float32, device=device) + + # Conditional branch: text features followed by zeros for the image tokens. + llm_features_full = torch.zeros( + 1, num_text_tokens + num_image_tokens, llm_dim, dtype=llm_features.dtype, device=device + ) + llm_features_full[0, :num_text_tokens] = llm_features.to(device) + + # Unconditional (negative) branch is image-only with zeroed conditioning. + neg_position_ids = inputs["position_ids"][:, num_text_tokens:] + neg_segment_ids = inputs["segment_ids"][:, num_text_tokens:] + neg_indicator = inputs["indicator"][:, num_text_tokens:] + neg_llm_features = torch.zeros(1, num_image_tokens, llm_dim, dtype=llm_features.dtype, device=device) + + generator = torch.Generator(device=device) + if seed is not None: + generator.manual_seed(seed) + z = torch.randn( + 1, num_image_tokens, LATENT_DIM, dtype=torch.float32, device=device, generator=generator + ) + text_z_padding = torch.zeros(1, num_text_tokens, LATENT_DIM, dtype=torch.float32, device=device) + + for i in range(num_steps - 1, -1, -1): + t_val = float(schedule(step_intervals[i + 1].unsqueeze(0)).item()) + s_val = float(schedule(step_intervals[i].unsqueeze(0)).item()) + t = torch.full((1,), t_val, dtype=torch.float32, device=device) + + pos_z = torch.cat([text_z_padding, z], dim=1) + pos_out = conditional_transformer( + llm_features=llm_features_full, + x=pos_z, + t=t, + position_ids=inputs["position_ids"], + segment_ids=inputs["segment_ids"], + indicator=inputs["indicator"], + ) + pos_v = pos_out[:, num_text_tokens:] + + neg_v = unconditional_transformer( + llm_features=neg_llm_features, + x=z, + t=t, + position_ids=neg_position_ids, + segment_ids=neg_segment_ids, + indicator=neg_indicator, + ) + + gw_i = gw_per_step[i] + v = gw_i * pos_v + (1.0 - gw_i) * neg_v + z = z + v * (s_val - t_val) + + if step_callback is not None: + step_callback(num_steps - i, num_steps, z) + + return pack_latents_to_grid(z, grid_h, grid_w) diff --git a/invokeai/backend/ideogram4/latent_norm.py b/invokeai/backend/ideogram4/latent_norm.py new file mode 100644 index 00000000000..eb013e9926a --- /dev/null +++ b/invokeai/backend/ideogram4/latent_norm.py @@ -0,0 +1,272 @@ +from __future__ import annotations + +import torch + +LATENT_SHIFT: tuple[float, ...] = ( + 0.01984364, + 0.10149707, + 0.29689495, + 0.27188619, + -0.21445648, + -0.15979549, + 0.05021099, + -0.15083604, + -0.15360136, + -0.20131799, + 0.01922352, + 0.0622626, + 0.10140969, + -0.06739428, + 0.3758261, + -0.233712, + 0.35164491, + -0.02590912, + -0.0271935, + -0.10833897, + -0.1476848, + -0.01130957, + -0.2298372, + 0.23526423, + -0.10893522, + 0.11957631, + 0.04047799, + 0.3134589, + -0.17225064, + -0.18646109, + -0.34691978, + -0.03571246, + 0.02583857, + 0.10190072, + 0.28402294, + 0.26952152, + -0.21634675, + -0.17938656, + 0.04358909, + -0.15007621, + -0.1548502, + -0.18971131, + 0.02710861, + 0.05609494, + 0.10697846, + -0.06854968, + 0.38167698, + -0.24269937, + 0.35705471, + -0.03063305, + -0.02946109, + -0.11244286, + -0.14336038, + -0.01362137, + -0.21863696, + 0.23228983, + -0.11739769, + 0.11693044, + 0.02563311, + 0.31356594, + -0.17420591, + -0.19006285, + -0.34905377, + -0.04025005, + 0.01924137, + 0.07652984, + 0.2995608, + 0.2628057, + -0.22011674, + -0.12715361, + 0.04879879, + -0.14075719, + -0.15935895, + -0.2123584, + 0.01974813, + 0.05523547, + 0.10011992, + -0.06428964, + 0.37781868, + -0.21491644, + 0.34254215, + -0.03153528, + -0.0310082, + -0.10761415, + -0.14730405, + -0.02475182, + -0.2285588, + 0.2515081, + -0.10445128, + 0.12446, + 0.07062869, + 0.30880162, + -0.18016875, + -0.18869164, + -0.34533499, + -0.0129177, + 0.02578168, + 0.07993659, + 0.28642181, + 0.26038408, + -0.22459419, + -0.14820155, + 0.04059549, + -0.14043529, + -0.16111187, + -0.2020305, + 0.02602069, + 0.04852717, + 0.10432153, + -0.06309942, + 0.38402443, + -0.22397003, + 0.34814481, + -0.03774432, + -0.03381438, + -0.11245691, + -0.14128767, + -0.02853208, + -0.21752016, + 0.24872463, + -0.11399775, + 0.1222687, + 0.05620835, + 0.309178, + -0.18065738, + -0.19401479, + -0.34495114, + -0.01760592, +) + +LATENT_SCALE: tuple[float, ...] = ( + 1.63933691, + 1.70204478, + 1.73642566, + 1.90004803, + 1.6675316, + 1.69059584, + 1.56853198, + 1.62314944, + 1.89106626, + 1.58086668, + 1.60822129, + 1.60962993, + 1.63322129, + 1.56074359, + 1.73419528, + 1.7919265, + 1.64040632, + 1.66802808, + 1.60390303, + 1.75480492, + 1.63187587, + 1.64334594, + 1.61722884, + 1.60146046, + 1.63459219, + 1.55291476, + 1.68771497, + 1.68415657, + 1.78966054, + 1.66631641, + 1.65626686, + 1.65976433, + 1.63487607, + 1.69513249, + 1.72933756, + 1.91310663, + 1.67035057, + 1.72286863, + 1.56719251, + 1.61934825, + 1.88628859, + 1.56911539, + 1.59455129, + 1.60829869, + 1.62470611, + 1.56052853, + 1.73677003, + 1.77563606, + 1.63732541, + 1.66370527, + 1.59508952, + 1.75153949, + 1.63029275, + 1.64517667, + 1.61659342, + 1.59722044, + 1.64103121, + 1.5408531, + 1.68610394, + 1.67772755, + 1.78998563, + 1.66621713, + 1.65458955, + 1.66041308, + 1.64710857, + 1.68163503, + 1.74000294, + 1.92784786, + 1.67411194, + 1.67395548, + 1.57406532, + 1.62199356, + 1.87618195, + 1.5584375, + 1.57438785, + 1.61711053, + 1.63094305, + 1.55644029, + 1.73124302, + 1.80666627, + 1.6463621, + 1.65932006, + 1.60816188, + 1.75682671, + 1.64695873, + 1.63121722, + 1.61380832, + 1.60478651, + 1.63396035, + 1.53505068, + 1.65534289, + 1.67132281, + 1.80317197, + 1.6767314, + 1.65700938, + 1.68426259, + 1.65339716, + 1.67540638, + 1.73298504, + 1.94067348, + 1.67893609, + 1.70635117, + 1.5730906, + 1.61928553, + 1.87148809, + 1.56244866, + 1.56697152, + 1.61584394, + 1.62759496, + 1.55480378, + 1.73484107, + 1.79055143, + 1.64688773, + 1.66121492, + 1.60135887, + 1.75254572, + 1.64798332, + 1.62989921, + 1.61381592, + 1.60792883, + 1.63939668, + 1.53075757, + 1.65371318, + 1.66801185, + 1.80029087, + 1.67591476, + 1.65655173, + 1.68533454, +) + + +def get_latent_norm() -> tuple[torch.Tensor, torch.Tensor]: + shift = torch.tensor(LATENT_SHIFT, dtype=torch.float32) + scale = torch.tensor(LATENT_SCALE, dtype=torch.float32) + assert shift.shape == (128,) and scale.shape == (128,) + return shift, scale diff --git a/invokeai/backend/ideogram4/modeling_ideogram4.py b/invokeai/backend/ideogram4/modeling_ideogram4.py new file mode 100644 index 00000000000..11086fbd795 --- /dev/null +++ b/invokeai/backend/ideogram4/modeling_ideogram4.py @@ -0,0 +1,379 @@ +"""Ideogram4 transformer backbone. + +The transformer consumes Qwen3-VL embeddings and flow-matching noise tokens to +produce velocity predictions on image latents. +""" + +from __future__ import annotations + +import math +from dataclasses import dataclass + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from invokeai.backend.ideogram4.constants import ( + LLM_TOKEN_INDICATOR, + OUTPUT_IMAGE_INDICATOR, + QWEN3_VL_ACTIVATION_LAYERS, +) + + +@dataclass +class Ideogram4Config: + emb_dim: int = 4608 + num_layers: int = 34 + num_heads: int = 18 + intermediate_size: int = 12288 + adanln_dim: int = 512 + + # Latent dimension after patchification: ae_channels (32) * patch_size**2 (4) = 128. + in_channels: int = 128 + + # Hidden size of Qwen3-VL-8B-Instruct multiplied by the number of layers we extract + # Qwen3-VL hidden size = 4096 + llm_features_dim: int = 4096 * len(QWEN3_VL_ACTIVATION_LAYERS) + + rope_theta: int = 5_000_000 + mrope_section: tuple[int, ...] = (24, 20, 20) + + norm_eps: float = 1e-5 + + +def _rotate_half(x: torch.Tensor) -> torch.Tensor: + half = x.shape[-1] // 2 + x1 = x[..., :half] + x2 = x[..., half:] + return torch.cat((-x2, x1), dim=-1) + + +def _apply_rotary_pos_emb( + q: torch.Tensor, + k: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor]: + # q, k: (B, num_heads, L, head_dim); cos/sin: (B, L, head_dim). + cos = cos.unsqueeze(1) + sin = sin.unsqueeze(1) + q_embed = (q * cos) + (_rotate_half(q) * sin) + k_embed = (k * cos) + (_rotate_half(k) * sin) + return q_embed, k_embed + + +class Ideogram4MRoPE(nn.Module): + inv_freq: torch.Tensor + + def __init__( + self, + head_dim: int, + base: int, + mrope_section: tuple[int, ...], + ) -> None: + super().__init__() + inv_freq = 1.0 / ( + base ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim) + ) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.mrope_section = tuple(mrope_section) + self.head_dim = head_dim + + @torch.no_grad() + def forward(self, position_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + # position_ids: (B, L, 3) of int. + assert position_ids.ndim == 3 and position_ids.shape[-1] == 3 + batch_size, seq_len, _ = position_ids.shape + + # (3, B, inv_freq_size, L) + pos = position_ids.permute(2, 0, 1).to(dtype=torch.float32) # type: ignore[arg-type] + inv_freq = self.inv_freq.to(dtype=torch.float32)[None, None, :, None].expand( + 3, batch_size, -1, 1 + ) # type: ignore[index] + freqs = inv_freq @ pos.unsqueeze(2) + freqs = freqs.transpose(2, 3) # (3, B, L, inv_freq_size) + + # interleaved mrope: pull H freqs into idx 1 mod 3, W freqs into idx 2 mod 3. + freqs_t = freqs[0].clone() + for axis, offset in ((1, 1), (2, 2)): + length = self.mrope_section[axis] * 3 + idx = torch.arange(offset, length, 3, device=freqs_t.device) + freqs_t[..., idx] = freqs[axis][..., idx] + + emb = torch.cat((freqs_t, freqs_t), dim=-1) + return emb.cos(), emb.sin() + + +class Ideogram4RMSNorm(nn.Module): + def __init__(self, dim: int, eps: float = 1e-6) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(dim)) + self.eps = eps + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return F.rms_norm(x, self.weight.shape, self.weight, self.eps) + + +class Ideogram4Attention(nn.Module): + def __init__(self, hidden_size: int, num_heads: int, eps: float = 1e-5) -> None: + super().__init__() + assert hidden_size % num_heads == 0 + self.hidden_size = hidden_size + self.num_heads = num_heads + self.head_dim = hidden_size // num_heads + + self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=False) + self.norm_q = Ideogram4RMSNorm(self.head_dim, eps=eps) + self.norm_k = Ideogram4RMSNorm(self.head_dim, eps=eps) + self.o = nn.Linear(hidden_size, hidden_size, bias=False) + + def forward( + self, + x: torch.Tensor, + segment_ids: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, + ) -> torch.Tensor: + batch_size, seq_len, _ = x.shape + + qkv = self.qkv(x) + qkv = qkv.view(batch_size, seq_len, 3, self.num_heads, self.head_dim) + q, k, v = qkv.unbind(dim=2) + + q = self.norm_q(q) + k = self.norm_k(k) + + # SDPA expects (B, num_heads, L, head_dim). + q = q.transpose(1, 2) + k = k.transpose(1, 2) + v = v.transpose(1, 2) + + q, k = _apply_rotary_pos_emb(q, k, cos, sin) + + # Block-diagonal mask from segment ids: (B, 1, L, L), True = attend. + attn_mask = (segment_ids.unsqueeze(2) == segment_ids.unsqueeze(1)).unsqueeze(1) + + out = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + out = out.transpose(1, 2).reshape(batch_size, seq_len, self.hidden_size) + return self.o(out) + + +class Ideogram4MLP(nn.Module): + def __init__(self, dim: int, hidden_dim: int) -> None: + super().__init__() + self.w1 = nn.Linear(dim, hidden_dim, bias=False) + self.w2 = nn.Linear(hidden_dim, dim, bias=False) + self.w3 = nn.Linear(dim, hidden_dim, bias=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.w2(F.silu(self.w1(x)) * self.w3(x)) + + +class Ideogram4TransformerBlock(nn.Module): + def __init__( + self, + hidden_size: int, + intermediate_size: int, + num_heads: int, + norm_eps: float, + adanln_dim: int, + ) -> None: + super().__init__() + self.attention = Ideogram4Attention(hidden_size, num_heads, eps=1e-5) + self.feed_forward = Ideogram4MLP(hidden_size, intermediate_size) + + self.attention_norm1 = Ideogram4RMSNorm(hidden_size, eps=norm_eps) + self.ffn_norm1 = Ideogram4RMSNorm(hidden_size, eps=norm_eps) + self.attention_norm2 = Ideogram4RMSNorm(hidden_size, eps=norm_eps) + self.ffn_norm2 = Ideogram4RMSNorm(hidden_size, eps=norm_eps) + + self.adaln_modulation = nn.Linear(adanln_dim, 4 * hidden_size, bias=True) + + def forward( + self, + x: torch.Tensor, + segment_ids: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, + adaln_input: torch.Tensor, + ) -> torch.Tensor: + mod = self.adaln_modulation(adaln_input) + scale_msa, gate_msa, scale_mlp, gate_mlp = mod.chunk(4, dim=-1) + gate_msa = torch.tanh(gate_msa) + gate_mlp = torch.tanh(gate_mlp) + scale_msa = 1.0 + scale_msa + scale_mlp = 1.0 + scale_mlp + + attn_out = self.attention( + self.attention_norm1(x) * scale_msa, + segment_ids=segment_ids, + cos=cos, + sin=sin, + ) + x = x + gate_msa * self.attention_norm2(attn_out) + x = x + gate_mlp * self.ffn_norm2(self.feed_forward(self.ffn_norm1(x) * scale_mlp)) + return x + + +def _sinusoidal_embedding( + t: torch.Tensor, dim: int, scale: float = 1e4 +) -> torch.Tensor: + t = t.to(torch.float32) + half = dim // 2 + freq = math.log(scale) / (half - 1) + freq = torch.exp(torch.arange(half, dtype=torch.float32, device=t.device) * -freq) # type: ignore[assignment] + emb = t.unsqueeze(-1) * freq + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) + if dim % 2 == 1: + emb = F.pad(emb, (0, 1)) + return emb + + +class Ideogram4EmbedScalar(nn.Module): + def __init__(self, dim: int, input_range: tuple[float, float]) -> None: + super().__init__() + self.dim = dim + self.range_min, self.range_max = input_range + assert self.range_max > self.range_min + self.mlp_in = nn.Linear(dim, dim, bias=True) + self.mlp_out = nn.Linear(dim, dim, bias=True) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # x is shape (..., 1) or (...,) holding a scalar per token. + x = x.to(torch.float32) + scaled = 1e4 * (x - self.range_min) / (self.range_max - self.range_min) + emb = _sinusoidal_embedding(scaled, self.dim) + emb = emb.to( + getattr(self.mlp_in, "compute_dtype", None) or self.mlp_in.weight.dtype + ) + emb = F.silu(self.mlp_in(emb)) + return self.mlp_out(emb) + + +class Ideogram4FinalLayer(nn.Module): + def __init__(self, hidden_size: int, out_channels: int, adanln_dim: int) -> None: + super().__init__() + self.norm_final = nn.LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False) + self.linear = nn.Linear(hidden_size, out_channels, bias=True) + self.adaln_modulation = nn.Linear(adanln_dim, hidden_size, bias=True) + + def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + scale = 1.0 + self.adaln_modulation(F.silu(c)) + return self.linear(self.norm_final(x) * scale) + + +class Ideogram4Transformer(nn.Module): + """Ideogram 4 flow-matching transformer.""" + + def __init__(self, config: Ideogram4Config) -> None: + super().__init__() + self.config = config + + head_dim = config.emb_dim // config.num_heads + + self.input_proj = nn.Linear(config.in_channels, config.emb_dim, bias=True) + self.llm_cond_norm = Ideogram4RMSNorm(config.llm_features_dim, eps=1e-6) + self.llm_cond_proj = nn.Linear(config.llm_features_dim, config.emb_dim, bias=True) + self.t_embedding = Ideogram4EmbedScalar(config.emb_dim, input_range=(0.0, 1.0)) + self.adaln_proj = nn.Linear(config.emb_dim, config.adanln_dim, bias=True) + + self.embed_image_indicator = nn.Embedding(2, config.emb_dim) + + self.rotary_emb = Ideogram4MRoPE( + head_dim=head_dim, + base=config.rope_theta, + mrope_section=config.mrope_section, + ) + + self.layers = nn.ModuleList( + [ + Ideogram4TransformerBlock( + hidden_size=config.emb_dim, + intermediate_size=config.intermediate_size, + num_heads=config.num_heads, + norm_eps=config.norm_eps, + adanln_dim=config.adanln_dim, + ) + for _ in range(config.num_layers) + ] + ) + + self.final_layer = Ideogram4FinalLayer( + hidden_size=config.emb_dim, + out_channels=config.in_channels, + adanln_dim=config.adanln_dim, + ) + + @property + def device(self) -> torch.device: + return next(self.parameters()).device + + def forward( + self, + *, + llm_features: torch.Tensor, + x: torch.Tensor, + t: torch.Tensor, + position_ids: torch.Tensor, + segment_ids: torch.Tensor, + indicator: torch.Tensor, + ) -> torch.Tensor: + """Velocity prediction. + + Args: + llm_features: (B, L, llm_features_dim) Qwen3-VL conditioning features. + x: (B, L, in_channels) noise tokens. + t: (B,) or (B, L) flow-matching time in [0, 1]. + position_ids: (B, L, 3) (t, h, w) positions for MRoPE. + segment_ids: (B, L) sample id within a packed batch. + indicator: (B, L) per-token role: LLM_TOKEN_INDICATOR or OUTPUT_IMAGE_INDICATOR. + + Returns: + (B, L, in_channels) velocity prediction in float32. Only the positions + with ``indicator == OUTPUT_IMAGE_INDICATOR`` are meaningful. + """ + batch_size, seq_len, in_channels = x.shape + assert in_channels == self.config.in_channels + + param_dtype = ( + getattr(self.input_proj, "compute_dtype", None) or self.input_proj.weight.dtype + ) + x = x.to(param_dtype) + t = t.to(param_dtype) + llm_features = llm_features.to(param_dtype) + + indicator = indicator.to(torch.long) + llm_token_mask = (indicator == LLM_TOKEN_INDICATOR).to(x.dtype).unsqueeze(-1) + output_image_mask = (indicator == OUTPUT_IMAGE_INDICATOR).to(x.dtype).unsqueeze(-1) + + llm_features = llm_features * llm_token_mask + x = x * output_image_mask + + x = self.input_proj(x) * output_image_mask + + # Keep shape (B, 1, ...) when t is per-sample so downstream adaln_modulation + # projections don't pay for L identical copies. + t_cond = self.t_embedding(t) + if t.dim() == 1: + t_cond = t_cond.unsqueeze(1) + adaln_input = F.silu(self.adaln_proj(t_cond)) + + llm_features = self.llm_cond_norm(llm_features) + llm_features = self.llm_cond_proj(llm_features) * llm_token_mask + + h = x + llm_features + + image_indicator_embedding = self.embed_image_indicator( + (indicator == OUTPUT_IMAGE_INDICATOR).to(torch.long) + ) + h = h + image_indicator_embedding + + cos, sin = self.rotary_emb(position_ids) + cos = cos.to(h.dtype) + sin = sin.to(h.dtype) + + for layer in self.layers: + h = layer(h, segment_ids=segment_ids, cos=cos, sin=sin, adaln_input=adaln_input) + + out = self.final_layer(h, c=adaln_input) + return out.to(torch.float32) diff --git a/invokeai/backend/ideogram4/quantized_loading.py b/invokeai/backend/ideogram4/quantized_loading.py new file mode 100644 index 00000000000..99d1ce26094 --- /dev/null +++ b/invokeai/backend/ideogram4/quantized_loading.py @@ -0,0 +1,278 @@ +from __future__ import annotations + +import warnings + +import bitsandbytes as bnb +import torch +import torch.nn as nn +import torch.nn.functional as F + + +_BNB_SIBLING_SUFFIXES = ( + ".absmax", + ".quant_map", + ".nested_absmax", + ".nested_quant_map", +) + +# Largest magnitude representable by the e4m3 float8 format. Per-row weight +# scales map each row's max abs value onto this so we use the full range. +FP8_E4M3_MAX = 448.0 +FP8_WEIGHT_DTYPE = torch.float8_e4m3fn +FP8_SCALE_SUFFIX = ".weight_scale" +# Marker written into the text encoder's config.json so the loader knows to take +# the custom weight-only FP8 path instead of transformers' from_pretrained. +FP8_TEXT_ENCODER_CONFIG_FLAG = "ideogram_fp8_weight_only" + + +def is_bnb4bit_state_dict(state_dict: dict[str, torch.Tensor]) -> bool: + """True if any key looks like a bnb 4-bit quant_state sibling.""" + return any(".quant_state.bitsandbytes__" in k for k in state_dict) + + +def swap_linears_to_bnb4bit( + module: nn.Module, + compute_dtype: torch.dtype, + *, + quant_type: str = "nf4", + compress_statistics: bool = False, +) -> None: + for name, child in list(module.named_children()): + if isinstance(child, nn.Linear): + new_linear = bnb.nn.Linear4bit( + child.in_features, + child.out_features, + bias=child.bias is not None, + compute_dtype=compute_dtype, + compress_statistics=compress_statistics, + quant_type=quant_type, + ) + setattr(module, name, new_linear) + else: + swap_linears_to_bnb4bit( + child, + compute_dtype, + quant_type=quant_type, + compress_statistics=compress_statistics, + ) + + +def load_bnb4bit_state_dict( + model: nn.Module, + state_dict: dict[str, torch.Tensor], + device: torch.device, + dtype: torch.dtype, +) -> None: + consumed: set[str] = set() + for full_name, tensor in state_dict.items(): + if ".quant_state." in full_name or full_name.endswith(_BNB_SIBLING_SUFFIXES): + continue + parent_path, _, param_name = full_name.rpartition(".") + parent = model.get_submodule(parent_path) if parent_path else model + current = parent._parameters.get(param_name) + if not isinstance(current, bnb.nn.Params4bit): + continue + prefix = full_name + "." + quantized_stats = {k: v for k, v in state_dict.items() if k.startswith(prefix)} + # bnb's from_prequantized pops keys it consumes from the dict, so snapshot + # the names first. + consumed.add(full_name) + consumed.update(quantized_stats.keys()) + parent._parameters[param_name] = bnb.nn.Params4bit.from_prequantized( + data=tensor, + quantized_stats=quantized_stats, + requires_grad=False, + device=device, + ) + + remaining = {k: v for k, v in state_dict.items() if k not in consumed} + for k in list(remaining): + if remaining[k].is_floating_point(): + remaining[k] = remaining[k].to(device=device, dtype=dtype) + else: + remaining[k] = remaining[k].to(device=device) + + missing, unexpected = model.load_state_dict(remaining, strict=False) + # Quantized weights are loaded via from_prequantized above, so they appear in + # `missing` from load_state_dict's perspective — filter those out. + real_missing = [m for m in missing if m not in consumed] + if real_missing: + raise RuntimeError(f"missing keys after quantized load: {real_missing[:10]}") + if unexpected: + raise RuntimeError(f"unexpected keys after quantized load: {unexpected[:10]}") + + for p in model.parameters(): + if isinstance(p, bnb.nn.Params4bit): + continue + if p.is_floating_point() and p.dtype != dtype: + p.data = p.data.to(dtype=dtype) + if p.device != device: + p.data = p.data.to(device=device) + for name, b in list(model.named_buffers()): + if b.is_floating_point() and b.dtype != dtype: + parent_path, _, leaf = name.rpartition(".") + parent = model.get_submodule(parent_path) if parent_path else model + parent.register_buffer( + leaf, + b.to(device=device, dtype=dtype), + persistent=leaf not in parent._non_persistent_buffers_set, + ) + elif b.device != device: + parent_path, _, leaf = name.rpartition(".") + parent = model.get_submodule(parent_path) if parent_path else model + parent.register_buffer( + leaf, + b.to(device=device), + persistent=leaf not in parent._non_persistent_buffers_set, + ) + + +# --------------------------------------------------------------------------- +# Weight-only FP8 (e4m3) +# +# Activations stay in the compute dtype (e.g. bfloat16); only Linear weights are +# stored as float8 with a per-output-channel (per-row) float32 scale. At forward +# time the weight is dequantized back to the compute dtype and a normal bf16 +# matmul runs, so this needs no FP8 tensor-core hardware and works on any device +# that can store float8 (CPU included). The win is ~2x smaller Linear weights. +# --------------------------------------------------------------------------- + + +def quantize_weight_to_fp8( + weight: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor]: + """Quantize a 2-D Linear weight to e4m3 float8 with per-row scales. + + Returns ``(weight_fp8, scale)`` where ``weight_fp8`` has shape ``(out, in)`` + in ``float8_e4m3fn`` and ``scale`` has shape ``(out,)`` in float32 such that + ``weight ≈ weight_fp8.to(dtype) * scale[:, None]``. + """ + w = weight.detach().to(torch.float32) + amax = w.abs().amax(dim=1, keepdim=True).clamp(min=1e-12) + scale = amax / FP8_E4M3_MAX + q = (w / scale).clamp(-FP8_E4M3_MAX, FP8_E4M3_MAX).to(FP8_WEIGHT_DTYPE) + return q, scale.squeeze(1).to(torch.float32) + + +def is_fp8_state_dict(state_dict: dict[str, torch.Tensor]) -> bool: + """True if the checkpoint carries weight-only FP8 Linear weights.""" + return any(k.endswith(FP8_SCALE_SUFFIX) for k in state_dict) or any( + v.dtype == FP8_WEIGHT_DTYPE for v in state_dict.values() + ) + + +class Fp8Linear(nn.Module): + """Linear layer holding an e4m3 float8 weight + per-row float32 scale. + + The weight and scale are registered as buffers (not parameters) so they load + via ``load_state_dict`` and are excluded from optimizer/grad machinery. The + dequantized matmul runs in ``compute_dtype``. + """ + + weight: torch.Tensor + weight_scale: torch.Tensor + bias: torch.Tensor | None + + def __init__( + self, + in_features: int, + out_features: int, + bias: bool, + compute_dtype: torch.dtype, + ) -> None: + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.compute_dtype = compute_dtype + self.register_buffer( + "weight", + torch.empty(out_features, in_features, dtype=FP8_WEIGHT_DTYPE), + ) + self.register_buffer("weight_scale", torch.empty(out_features, dtype=torch.float32)) + if bias: + self.register_buffer("bias", torch.empty(out_features, dtype=compute_dtype)) + else: + self.bias = None + + def forward(self, x: torch.Tensor) -> torch.Tensor: + w = self.weight.to(x.dtype) * self.weight_scale.to(x.dtype).unsqueeze(1) + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) + + +def swap_linears_to_fp8( + module: nn.Module, + state_dict: dict[str, torch.Tensor], + compute_dtype: torch.dtype, + *, + prefix: str = "", +) -> None: + """Replace each ``nn.Linear`` that has a saved FP8 scale with an ``Fp8Linear``. + + Gating on the presence of ``.weight_scale`` means only layers that were + actually quantized at save time are swapped; everything else loads normally in + the compute dtype. + """ + for name, child in list(module.named_children()): + child_prefix = f"{prefix}{name}" + if ( + isinstance(child, nn.Linear) and f"{child_prefix}{FP8_SCALE_SUFFIX}" in state_dict + ): + setattr( + module, + name, + Fp8Linear( + child.in_features, + child.out_features, + bias=child.bias is not None, + compute_dtype=compute_dtype, + ), + ) + else: + swap_linears_to_fp8(child, state_dict, compute_dtype, prefix=f"{child_prefix}.") + + +def load_fp8_state_dict( + model: nn.Module, + state_dict: dict[str, torch.Tensor], + device: torch.device, + dtype: torch.dtype, + *, + assign: bool = False, + strict: bool = True, +) -> None: + """Load a weight-only FP8 checkpoint into ``model``. + + ``model`` must already have its FP8 Linear layers swapped in (see + ``swap_linears_to_fp8``). FP8 weights are kept as float8, scales stay float32, + and every other floating tensor is cast to ``dtype``. + + ``assign=True`` replaces the module's tensors with the prepared ones rather than + copying into them. Use it when the model was built with ``from_config`` so the + non-quantized params take the loaded dtype directly and computed non-persistent + buffers (e.g. rotary caches) are left untouched. With ``assign=False`` (default), + the caller must have already put the unquantized params in ``dtype``. + + ``strict=False`` downgrades missing keys to a warning (e.g. tied weights that a + ``transformers`` model resolves itself); unexpected keys always raise. + """ + prepared: dict[str, torch.Tensor] = {} + for k, v in state_dict.items(): + if v.dtype == FP8_WEIGHT_DTYPE: + prepared[k] = v.to(device=device) + elif k.endswith(FP8_SCALE_SUFFIX): + prepared[k] = v.to(device=device, dtype=torch.float32) + elif v.is_floating_point(): + prepared[k] = v.to(device=device, dtype=dtype) + else: + prepared[k] = v.to(device=device) + + missing, unexpected = model.load_state_dict(prepared, strict=False, assign=assign) + if unexpected: + raise RuntimeError(f"unexpected keys after fp8 load: {unexpected[:10]}") + if missing: + if strict: + raise RuntimeError(f"missing keys after fp8 load: {missing[:10]}") + warnings.warn(f"missing keys after fp8 load: {missing[:10]}", stacklevel=2) + + model.to(device) diff --git a/invokeai/backend/ideogram4/sampler_configs.py b/invokeai/backend/ideogram4/sampler_configs.py new file mode 100644 index 00000000000..f3204b8c596 --- /dev/null +++ b/invokeai/backend/ideogram4/sampler_configs.py @@ -0,0 +1,29 @@ +"""Named sampler configurations for Ideogram 4 inference.""" + +from __future__ import annotations + +from invokeai.backend.ideogram4.scheduler import SamplerParameters + +# guidance_schedule is in loop-INDEX order: index 0 is the LAST (polish) step. +# Each preset does the first N_main sampling steps at gw=7, then N_cleanup +# polish steps at gw=3. +PRESETS: dict[str, SamplerParameters] = { + "V4_QUALITY_48": SamplerParameters( + num_steps=48, + guidance_schedule=(3.0,) * 3 + (7.0,) * 45, + mu=0.0, + std=1.5, + ), + "V4_DEFAULT_20": SamplerParameters( + num_steps=20, + guidance_schedule=(3.0,) * 2 + (7.0,) * 18, + mu=0.0, + std=1.75, + ), + "V4_TURBO_12": SamplerParameters( + num_steps=12, + guidance_schedule=(3.0,) * 1 + (7.0,) * 11, + mu=0.5, + std=1.75, + ), +} diff --git a/invokeai/backend/ideogram4/sampling_utils.py b/invokeai/backend/ideogram4/sampling_utils.py new file mode 100644 index 00000000000..173014ea6ee --- /dev/null +++ b/invokeai/backend/ideogram4/sampling_utils.py @@ -0,0 +1,132 @@ +"""Sampling helpers for Ideogram 4: packed-sequence construction and latent unpacking. + +These wrap the vendored reference model (``modeling_ideogram4`` etc.) for use in +InvokeAI invocations. They mirror the logic of ``Ideogram4Pipeline._build_inputs`` +and ``Ideogram4Pipeline._decode`` from the reference implementation, specialised +to the single-image (batch size 1) case InvokeAI generates, where there is no +left-padding so the packed layout is simply ``[text tokens][image tokens]``. +""" + +from __future__ import annotations + +from typing import TypedDict + +import torch + +from invokeai.backend.ideogram4.constants import ( + IMAGE_POSITION_OFFSET, + LLM_TOKEN_INDICATOR, + OUTPUT_IMAGE_INDICATOR, +) + +# Latent patch size (each transformer image token covers a patch_size x patch_size +# block of VAE latents) and the VAE's spatial downscale factor. A single image +# token therefore covers ``PATCH_SIZE * AE_SCALE_FACTOR`` pixels per side. +PATCH_SIZE = 2 +AE_SCALE_FACTOR = 8 +PIXELS_PER_IMAGE_TOKEN = PATCH_SIZE * AE_SCALE_FACTOR # 16 + +# Packed-latent channel count: ae z_channels (32) * patch_size**2 (4) = 128. +LATENT_DIM = 128 + + +class Ideogram4DenoiseInputs(TypedDict): + """The packed-sequence tensors fed to the transformer during denoising.""" + + position_ids: torch.Tensor # (1, L, 3) int64 — (t, h, w) positions for MRoPE + segment_ids: torch.Tensor # (1, L) int64 — sample id within the packed batch + indicator: torch.Tensor # (1, L) int64 — LLM_TOKEN_INDICATOR or OUTPUT_IMAGE_INDICATOR + num_text_tokens: int + num_image_tokens: int + grid_h: int + grid_w: int + + +def validate_dimensions(height: int, width: int) -> None: + """Ensure the requested resolution is compatible with the patch/VAE grid.""" + if height % PIXELS_PER_IMAGE_TOKEN != 0 or width % PIXELS_PER_IMAGE_TOKEN != 0: + raise ValueError( + f"height and width must be divisible by {PIXELS_PER_IMAGE_TOKEN}, got {height}x{width}" + ) + + +def build_denoise_inputs( + num_text_tokens: int, + height: int, + width: int, + device: torch.device, +) -> Ideogram4DenoiseInputs: + """Build the packed ``[text][image]`` position/segment/indicator tensors for one image. + + Mirrors ``Ideogram4Pipeline._build_inputs`` for batch size 1 (no padding). + """ + validate_dimensions(height, width) + grid_h = height // PIXELS_PER_IMAGE_TOKEN + grid_w = width // PIXELS_PER_IMAGE_TOKEN + num_image_tokens = grid_h * grid_w + total_seq_len = num_text_tokens + num_image_tokens + + # Image grid positions (t=0, h, w), offset so they never collide with text positions. + h_idx = torch.arange(grid_h).view(-1, 1).expand(grid_h, grid_w).reshape(-1) + w_idx = torch.arange(grid_w).view(1, -1).expand(grid_h, grid_w).reshape(-1) + t_idx = torch.zeros_like(h_idx) + image_pos = torch.stack([t_idx, h_idx, w_idx], dim=1) + IMAGE_POSITION_OFFSET + + position_ids = torch.zeros(1, total_seq_len, 3, dtype=torch.long) + text_pos = torch.arange(num_text_tokens) + text_pos_3d = torch.stack([text_pos, text_pos, text_pos], dim=1) + position_ids[0, :num_text_tokens] = text_pos_3d + position_ids[0, num_text_tokens:] = image_pos + + # Single sample, no padding -> every position belongs to segment 1. + segment_ids = torch.ones(1, total_seq_len, dtype=torch.long) + + indicator = torch.zeros(1, total_seq_len, dtype=torch.long) + indicator[0, :num_text_tokens] = LLM_TOKEN_INDICATOR + indicator[0, num_text_tokens:] = OUTPUT_IMAGE_INDICATOR + + return Ideogram4DenoiseInputs( + position_ids=position_ids.to(device), + segment_ids=segment_ids.to(device), + indicator=indicator.to(device), + num_text_tokens=num_text_tokens, + num_image_tokens=num_image_tokens, + grid_h=grid_h, + grid_w=grid_w, + ) + + +def pack_latents_to_grid(z: torch.Tensor, grid_h: int, grid_w: int) -> torch.Tensor: + """Reshape sampled latents ``(1, grid_h*grid_w, LATENT_DIM)`` to ``(1, LATENT_DIM, grid_h, grid_w)``. + + Stores the packed latent in a channels-first 4-D tensor so the grid dimensions + survive in the latent shape (the L2I node recovers them). + """ + batch_size = z.shape[0] + z = z.reshape(batch_size, grid_h, grid_w, LATENT_DIM) + return z.permute(0, 3, 1, 2).contiguous() + + +def unpatchify_and_denormalize( + packed: torch.Tensor, + latent_shift: torch.Tensor, + latent_scale: torch.Tensor, +) -> torch.Tensor: + """Convert a packed latent ``(1, LATENT_DIM, grid_h, grid_w)`` to a VAE latent ``(1, 32, H/8, W/8)``. + + Applies the per-channel latent denormalization (``z * scale + shift``) in the + packed space, then unpatchifies, exactly as ``Ideogram4Pipeline._decode`` does. + """ + batch_size, channels, grid_h, grid_w = packed.shape + if channels != LATENT_DIM: + raise ValueError(f"expected {LATENT_DIM} packed channels, got {channels}") + + # (B, grid_h, grid_w, LATENT_DIM) + z = packed.permute(0, 2, 3, 1) + z = z * latent_scale.to(z.device, z.dtype) + latent_shift.to(z.device, z.dtype) + + ae_channels = LATENT_DIM // (PATCH_SIZE * PATCH_SIZE) # 32 + z = z.reshape(batch_size, grid_h, grid_w, PATCH_SIZE, PATCH_SIZE, ae_channels) + z = z.permute(0, 5, 1, 3, 2, 4).contiguous() + z = z.reshape(batch_size, ae_channels, grid_h * PATCH_SIZE, grid_w * PATCH_SIZE) + return z diff --git a/invokeai/backend/ideogram4/scheduler.py b/invokeai/backend/ideogram4/scheduler.py new file mode 100644 index 00000000000..d84b46be0a9 --- /dev/null +++ b/invokeai/backend/ideogram4/scheduler.py @@ -0,0 +1,70 @@ +"""Logit-normal schedule and Euler flow-matching sampler.""" + +from __future__ import annotations + +import math +from dataclasses import dataclass + +import torch + + +@dataclass(frozen=True) +class LogitNormalSchedule: + mean: float + std: float = 1.0 + logsnr_min: float = -15.0 + logsnr_max: float = 18.0 + + def __call__(self, t: torch.Tensor) -> torch.Tensor: + t = t.to(torch.float64) + z = torch.special.ndtri(t) + y = self.mean + self.std * z + t_ = torch.special.expit(y) + t_ = 1 - t_ + t_min = 1.0 / (1 + math.exp(0.5 * self.logsnr_max)) + t_max = 1.0 / (1 + math.exp(0.5 * self.logsnr_min)) + return t_.clamp(t_min, t_max).to(torch.float32) + + +def get_schedule_for_resolution( + image_resolution: tuple[int, int], + known_resolution: tuple[int, int] = (512, 512), + known_mean: float = 1.0, + std: float = 1.0, +) -> LogitNormalSchedule: + """Resolution-aware schedule used at eval time.""" + num_pixels = image_resolution[0] * image_resolution[1] + known_pixels = known_resolution[0] * known_resolution[1] + mean = known_mean + 0.5 * math.log(num_pixels / known_pixels) + return LogitNormalSchedule(mean=mean, std=std) + + +def make_step_intervals(num_steps: int) -> torch.Tensor: + """Default linear step schedule used by the v4 eval config.""" + return torch.linspace(0.0, 1.0, num_steps + 1, dtype=torch.float32) + + +@dataclass(frozen=True, kw_only=True) +class SamplerParameters: + """Bundle of sampling hyperparameters for a named preset. + + ``guidance_schedule`` is in LOOP-INDEX order: index 0 is the LAST sampling + step (final polish), index ``num_steps - 1`` is the FIRST sampling step. + ``mu`` and ``std`` are the mean and stddev of the logit-normal noise + schedule passed to ``get_schedule_for_resolution`` (as ``known_mean`` and + ``std`` respectively). + + See ``ideogram4.sampler_configs.PRESETS`` for the named preset registry. + """ + + num_steps: int + guidance_schedule: tuple[float, ...] + mu: float + std: float = 1.0 + + def __post_init__(self) -> None: + if len(self.guidance_schedule) != self.num_steps: + raise ValueError( + f"guidance_schedule has length {len(self.guidance_schedule)}, " + f"expected num_steps={self.num_steps}" + ) diff --git a/invokeai/backend/ideogram4/text_encoding.py b/invokeai/backend/ideogram4/text_encoding.py new file mode 100644 index 00000000000..45cc7e3e7cf --- /dev/null +++ b/invokeai/backend/ideogram4/text_encoding.py @@ -0,0 +1,89 @@ +"""Qwen3-VL text encoding for Ideogram 4. + +Ideogram 4 conditions on a concatenation of hidden states taken from 13 specific +layers of the Qwen3-VL language model (see ``QWEN3_VL_ACTIVATION_LAYERS``), giving +a ``(seq_len, 4096 * 13) == (seq_len, 53248)`` feature tensor. + +The reference pipeline runs the encoder over the full packed ``[text][image]`` +sequence, but the text-token hidden states are independent of the image tokens +(attention is causal and gated to LLM-token positions), so we encode the text +tokens only. The denoise node assembles the full packed sequence. +""" + +from __future__ import annotations + +import torch + +from invokeai.backend.ideogram4.constants import QWEN3_VL_ACTIVATION_LAYERS + +# Matches Ideogram4PipelineConfig.max_text_tokens. +MAX_TEXT_TOKENS = 2048 + + +def encode_qwen3vl_prompt( + prompt: str, + tokenizer, + text_encoder, + *, + max_text_tokens: int = MAX_TEXT_TOKENS, +) -> torch.Tensor: + """Encode a single prompt into Ideogram 4 conditioning features. + + Returns a ``(num_text_tokens, 53248)`` float32 tensor (on the encoder's device; + the caller is responsible for moving it to CPU for storage). + """ + # Importing here keeps module import cheap and tolerant of transformers versions + # that lay out the masking utilities differently. + from transformers.masking_utils import create_causal_mask + + device = next(text_encoder.parameters()).device + + # Chat-format and tokenize, matching Ideogram4Pipeline._tokenize. + messages = [{"role": "user", "content": [{"type": "text", "text": prompt}]}] + text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) + encoded = tokenizer(text, return_tensors="pt", add_special_tokens=False) + token_ids = encoded["input_ids"].to(device) # (1, L) + num_text_tokens = int(token_ids.shape[1]) + if num_text_tokens > max_text_tokens: + raise ValueError( + f"prompt has {num_text_tokens} tokens, exceeds max_text_tokens={max_text_tokens}" + ) + + # Text-only sequence: every position is a real LLM token. + attention_mask = torch.ones((1, num_text_tokens), dtype=torch.long, device=device) + pos_2d = torch.arange(num_text_tokens, device=device)[None, :] # (1, L) + + language_model = text_encoder.language_model + inputs_embeds = language_model.embed_tokens(token_ids) + + position_ids_4d = pos_2d[None, ...].expand(4, 1, num_text_tokens) + text_position_ids = position_ids_4d[0] # (1, L) + mrope_position_ids = position_ids_4d[1:] # (3, 1, L) + + causal_mask = create_causal_mask( + config=language_model.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=None, + position_ids=text_position_ids, + ) + position_embeddings = language_model.rotary_emb(inputs_embeds, mrope_position_ids) + + tap_set = set(QWEN3_VL_ACTIVATION_LAYERS) + captured: dict[int, torch.Tensor] = {} + hidden_states = inputs_embeds + for layer_idx, decoder_layer in enumerate(language_model.layers): + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=text_position_ids, + past_key_values=None, + position_embeddings=position_embeddings, + ) + if layer_idx in tap_set: + captured[layer_idx] = hidden_states + + selected = [captured[i] for i in QWEN3_VL_ACTIVATION_LAYERS] + stacked = torch.stack(selected, dim=0) # (num_taps, 1, L, H) + stacked = stacked.permute(1, 2, 3, 0).reshape(1, num_text_tokens, -1) # (1, L, H*num_taps) + return stacked[0].to(torch.float32) # (L, 53248) diff --git a/invokeai/backend/ideogram4/transformer_pair.py b/invokeai/backend/ideogram4/transformer_pair.py new file mode 100644 index 00000000000..509149fd61e --- /dev/null +++ b/invokeai/backend/ideogram4/transformer_pair.py @@ -0,0 +1,25 @@ +"""Container holding Ideogram 4's two transformer branches as a single submodel. + +Ideogram 4 uses dual-branch asymmetric CFG with two *separate* weight sets +(``transformer/`` and ``unconditional_transformer/`` on disk). InvokeAI's model +cache keys a cached entity by (model, submodel_type) and there is no +"unconditional transformer" submodel type, so we load both branches into one +``nn.Module`` returned for ``SubModelType.Transformer``. This keeps both branches +co-resident through the denoise loop (each step runs both), which is required for +acceptable performance and is what makes the nf4 build fit in 24 GB. +""" + +from __future__ import annotations + +import torch + +from invokeai.backend.ideogram4.modeling_ideogram4 import Ideogram4Transformer + + +class Ideogram4TransformerPair(torch.nn.Module): + """Holds the conditional and unconditional Ideogram 4 transformers.""" + + def __init__(self, conditional: Ideogram4Transformer, unconditional: Ideogram4Transformer) -> None: + super().__init__() + self.conditional = conditional + self.unconditional = unconditional diff --git a/invokeai/backend/model_manager/configs/factory.py b/invokeai/backend/model_manager/configs/factory.py index 985cb982d30..b9a70ae2692 100644 --- a/invokeai/backend/model_manager/configs/factory.py +++ b/invokeai/backend/model_manager/configs/factory.py @@ -72,6 +72,7 @@ Main_Diffusers_CogView4_Config, Main_Diffusers_Flux2_Config, Main_Diffusers_FLUX_Config, + Main_Diffusers_Ideogram4_Config, Main_Diffusers_QwenImage_Config, Main_Diffusers_SD1_Config, Main_Diffusers_SD2_Config, @@ -174,6 +175,7 @@ Annotated[Main_Diffusers_CogView4_Config, Main_Diffusers_CogView4_Config.get_tag()], Annotated[Main_Diffusers_QwenImage_Config, Main_Diffusers_QwenImage_Config.get_tag()], Annotated[Main_Diffusers_ZImage_Config, Main_Diffusers_ZImage_Config.get_tag()], + Annotated[Main_Diffusers_Ideogram4_Config, Main_Diffusers_Ideogram4_Config.get_tag()], # Main (Pipeline) - checkpoint format # IMPORTANT: FLUX.2 must be checked BEFORE FLUX.1 because FLUX.2 has specific validation # that will reject FLUX.1 models, but FLUX.1 validation may incorrectly match FLUX.2 models diff --git a/invokeai/backend/model_manager/configs/main.py b/invokeai/backend/model_manager/configs/main.py index e1e408a3483..0fae8af2855 100644 --- a/invokeai/backend/model_manager/configs/main.py +++ b/invokeai/backend/model_manager/configs/main.py @@ -83,6 +83,10 @@ def from_base( return cls(steps=9, cfg_scale=1.0, width=1024, height=1024) case BaseModelType.Anima: return cls(steps=35, cfg_scale=4.5, width=1024, height=1024) + case BaseModelType.Ideogram4: + # Ideogram 4 uses sampler presets (default V4_QUALITY_48 = 48 steps) and a + # dual-branch guidance schedule; these are sensible UI defaults. + return cls(steps=48, cfg_scale=7.0, width=1024, height=1024) case BaseModelType.Flux2: # Different defaults based on variant if variant in (Flux2VariantType.Klein4BBase, Flux2VariantType.Klein9BBase): @@ -1283,6 +1287,37 @@ def _validate_looks_like_gguf_quantized(cls, mod: ModelOnDisk) -> None: raise NotAMatchError("state dict does not look like GGUF quantized") +class Main_Diffusers_Ideogram4_Config(Diffusers_Config_Base, Main_Config_Base, Config_Base): + """Model config for Ideogram 4 diffusers models (nf4 / fp8 quantized). + + The on-disk layout is a diffusers pipeline folder bundling two transformers + (transformer/ + unconditional_transformer/), a Qwen3-VL text_encoder/ + tokenizer/, + and a FLUX.2-style vae/. Quantization (nf4 vs fp8) lives inside the component folders + and is detected by the loader, not here. + """ + + base: Literal[BaseModelType.Ideogram4] = Field(BaseModelType.Ideogram4) + + @classmethod + def from_model_on_disk(cls, mod: ModelOnDisk, override_fields: dict[str, Any]) -> Self: + raise_if_not_dir(mod) + + raise_for_override_fields(cls, override_fields) + + # The Ideogram4Pipeline class name in model_index.json uniquely identifies this base. + raise_for_class_name( + common_config_paths(mod.path), + {"Ideogram4Pipeline"}, + ) + + repo_variant = override_fields.pop("repo_variant", None) or cls._get_repo_variant_or_raise(mod) + + return cls( + **override_fields, + repo_variant=repo_variant, + ) + + class Main_Diffusers_QwenImage_Config(Diffusers_Config_Base, Main_Config_Base, Config_Base): """Model config for Qwen Image diffusers models (both txt2img and edit).""" diff --git a/invokeai/backend/model_manager/load/model_loaders/ideogram4.py b/invokeai/backend/model_manager/load/model_loaders/ideogram4.py new file mode 100644 index 00000000000..dd35fb47060 --- /dev/null +++ b/invokeai/backend/model_manager/load/model_loaders/ideogram4.py @@ -0,0 +1,178 @@ +"""Model loading for Ideogram 4 in InvokeAI. + +The on-disk model is a diffusers pipeline folder bundling: + - transformer/ (Ideogram4Transformer, nf4 or fp8 quantized) + - unconditional_transformer/ (Ideogram4Transformer, nf4 or fp8 quantized) + - text_encoder/ + tokenizer/ (Qwen3-VL, nf4 or fp8) + - vae/ (FLUX.2-style AutoencoderKL; loaded via the vendored AutoEncoder) + +The transformer is our vendored ``Ideogram4Transformer`` (not a diffusers class), so we +build it explicitly and load the prequantized state dict — mirroring how InvokeAI loads +FLUX nf4. Both transformer branches are returned as a single ``Ideogram4TransformerPair``. +""" + +import json +from pathlib import Path +from typing import Any, Optional + +import accelerate +import torch +from safetensors.torch import load_file + +from invokeai.backend.model_manager.configs.factory import AnyModelConfig +from invokeai.backend.model_manager.configs.main import Main_Diffusers_Ideogram4_Config +from invokeai.backend.model_manager.load.load_default import ModelLoader +from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry +from invokeai.backend.model_manager.taxonomy import ( + AnyModel, + BaseModelType, + ModelFormat, + ModelType, + SubModelType, +) +from invokeai.backend.util.devices import TorchDevice + + +def _load_local_state_dict(folder: Path, basename: str) -> dict[str, torch.Tensor]: + """Load a (possibly sharded) safetensors checkpoint from a local diffusers component folder.""" + index_path = folder / f"{basename}.safetensors.index.json" + if index_path.exists(): + with open(index_path) as f: + weight_map: dict[str, str] = json.load(f)["weight_map"] + sd: dict[str, torch.Tensor] = {} + for shard in sorted(set(weight_map.values())): + sd.update(load_file(folder / shard)) + return sd + return load_file(folder / f"{basename}.safetensors") + + +@ModelLoaderRegistry.register(base=BaseModelType.Ideogram4, type=ModelType.Main, format=ModelFormat.Diffusers) +class Ideogram4DiffusersModel(ModelLoader): + """Loads Ideogram 4 main models (nf4 / fp8) bundled in diffusers layout.""" + + def _load_model( + self, + config: AnyModelConfig, + submodel_type: Optional[SubModelType] = None, + ) -> AnyModel: + if not isinstance(config, Main_Diffusers_Ideogram4_Config): + raise ValueError(f"Expected Main_Diffusers_Ideogram4_Config, got {type(config).__name__}.") + if submodel_type is None: + raise Exception("A submodel type must be provided when loading Ideogram 4 main pipelines.") + + model_path = Path(config.path) + + match submodel_type: + case SubModelType.Transformer: + return self._load_transformer_pair(model_path) + case SubModelType.TextEncoder: + return self._load_text_encoder(model_path) + case SubModelType.Tokenizer: + from transformers import AutoTokenizer + + return AutoTokenizer.from_pretrained(model_path / "tokenizer", local_files_only=True) + case SubModelType.VAE: + return self._load_vae(model_path) + + raise ValueError( + f"Unsupported submodel for Ideogram 4: {submodel_type.value if submodel_type else 'None'}. " + "Supported: Transformer, TextEncoder, Tokenizer, VAE." + ) + + def _load_transformer_pair(self, model_path: Path) -> AnyModel: + from invokeai.backend.ideogram4.transformer_pair import Ideogram4TransformerPair + + conditional = self._load_one_transformer(model_path / "transformer") + unconditional = self._load_one_transformer(model_path / "unconditional_transformer") + return Ideogram4TransformerPair(conditional=conditional, unconditional=unconditional) + + def _load_one_transformer(self, folder: Path) -> torch.nn.Module: + from invokeai.backend.ideogram4.modeling_ideogram4 import Ideogram4Config, Ideogram4Transformer + from invokeai.backend.ideogram4.quantized_loading import ( + is_bnb4bit_state_dict, + is_fp8_state_dict, + load_fp8_state_dict, + swap_linears_to_fp8, + ) + from invokeai.backend.quantization.bnb_nf4 import quantize_model_nf4 + + target_device = TorchDevice.choose_torch_device() + compute_dtype = TorchDevice.choose_bfloat16_safe_dtype(target_device) + + sd = _load_local_state_dict(folder, "diffusion_pytorch_model") + self._ram_cache.make_room(sum(t.nelement() * t.element_size() for t in sd.values())) + + if is_bnb4bit_state_dict(sd): + # nf4: build the model with InvokeLinearNF4 layers (compress_statistics=False, matching + # the on-disk single-quant format), then load the prequantized state dict. The model + # stays on CPU/meta until the cache moves it to the GPU. + with accelerate.init_empty_weights(): + model: torch.nn.Module = Ideogram4Transformer(Ideogram4Config()) + model = quantize_model_nf4(model, modules_to_not_convert=set(), compute_dtype=compute_dtype) + model.load_state_dict(sd, strict=True, assign=True) + return model + + if is_fp8_state_dict(sd): + # Weight-only fp8 (e4m3): dequantizes to compute dtype at forward time; runs on any device. + model = Ideogram4Transformer(Ideogram4Config()) + model.to(compute_dtype) + swap_linears_to_fp8(model, sd, compute_dtype=compute_dtype) + load_fp8_state_dict(model, sd, device=torch.device("cpu"), dtype=compute_dtype) + model.eval() + return model + + # Unquantized fallback. + with accelerate.init_empty_weights(): + model = Ideogram4Transformer(Ideogram4Config()) + model.load_state_dict(sd, strict=True, assign=True) + return model.to(compute_dtype) + + def _load_text_encoder(self, model_path: Path) -> AnyModel: + from transformers import Qwen3VLModel + + encoder_path = model_path / "text_encoder" + target_device = TorchDevice.choose_torch_device() + model_dtype = TorchDevice.choose_bfloat16_safe_dtype(target_device) + + cfg = json.loads((encoder_path / "config.json").read_text(encoding="utf-8")) + if cfg.get("ideogram_fp8_weight_only", False): + # The fp8 text encoder uses Ideogram's custom weight-only fp8 layout, which transformers' + # from_pretrained cannot read. Supporting it requires the vendored _load_fp8_text_encoder + # path; deferred until the fp8 build is targeted (nf4 is the 24 GB path). + raise NotImplementedError( + "Ideogram 4 fp8 text encoder loading is not yet implemented; use the nf4 build." + ) + + if "quantization_config" in cfg: + # bitsandbytes (nf4): transformers applies the quantization from config.json. bnb 4-bit + # requires a CUDA device, so place it there directly via device_map. + return Qwen3VLModel.from_pretrained( + encoder_path, + torch_dtype=model_dtype, + device_map={"": target_device}, + local_files_only=True, + ) + + return Qwen3VLModel.from_pretrained( + encoder_path, + torch_dtype=model_dtype, + low_cpu_mem_usage=True, + local_files_only=True, + ) + + def _load_vae(self, model_path: Path) -> AnyModel: + from invokeai.backend.ideogram4.autoencoder import ( + AutoEncoder, + AutoEncoderParams, + convert_diffusers_state_dict, + ) + + target_device = TorchDevice.choose_torch_device() + model_dtype = TorchDevice.choose_bfloat16_safe_dtype(target_device) + + sd = load_file(model_path / "vae" / "diffusion_pytorch_model.safetensors") + sd = convert_diffusers_state_dict(sd) + ae = AutoEncoder(AutoEncoderParams()) + ae.load_state_dict(sd) + ae.eval() + return ae.to(model_dtype) diff --git a/invokeai/backend/model_manager/taxonomy.py b/invokeai/backend/model_manager/taxonomy.py index a2e4e58bdc4..6c91e48edbc 100644 --- a/invokeai/backend/model_manager/taxonomy.py +++ b/invokeai/backend/model_manager/taxonomy.py @@ -52,6 +52,8 @@ class BaseModelType(str, Enum): """Indicates the model is associated with CogView 4 model architecture.""" ZImage = "z-image" """Indicates the model is associated with Z-Image model architecture, including Z-Image-Turbo.""" + Ideogram4 = "ideogram-4" + """Indicates the model is associated with the Ideogram 4 text-to-image model architecture.""" External = "external" """Indicates the model is hosted by an external provider.""" QwenImage = "qwen-image" From 52e034088e22a70ad0496f88a0096dddb6794d5f Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Thu, 25 Jun 2026 04:41:59 +0200 Subject: [PATCH 12/33] =?UTF-8?q?feat(ideogram4):=20Ideogram=204=20backend?= =?UTF-8?q?=20=E2=80=94=20model=20manager,=20invocations,=20nf4=20loading?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit End-to-end text-to-image backend for Ideogram 4, validated through the real session runner. Vendors the Apache-2.0 reference model (DiT, FLUX2-style VAE, logit-normal flow-match scheduler) into invokeai/backend/ideogram4/ with InvokeAI glue. Registers BaseModelType.Ideogram4, Main_Diffusers_Ideogram4_Config, and the Ideogram4DiffusersModel loader (two transformers as one Ideogram4TransformerPair; Qwen3-VL encoder + VAE). Both transformers and the encoder load via InvokeLinearNF4 so they work with the partial-load cache. Adds Ideogram4ConditioningInfo/Field/Output and the model_loader/text_encoder/denoise/l2i invocations. Text-to-image only. --- invokeai/app/api/dependencies.py | 2 + invokeai/app/invocations/fields.py | 6 ++ invokeai/app/invocations/ideogram4_denoise.py | 87 +++++++++++++++++++ .../invocations/ideogram4_latents_to_image.py | 66 ++++++++++++++ .../app/invocations/ideogram4_model_loader.py | 64 ++++++++++++++ .../app/invocations/ideogram4_text_encoder.py | 60 +++++++++++++ invokeai/app/invocations/metadata.py | 1 + invokeai/app/invocations/primitives.py | 12 +++ .../load/model_loaders/ideogram4.py | 55 +++++++----- .../diffusion/conditioning_data.py | 16 ++++ 10 files changed, 348 insertions(+), 21 deletions(-) create mode 100644 invokeai/app/invocations/ideogram4_denoise.py create mode 100644 invokeai/app/invocations/ideogram4_latents_to_image.py create mode 100644 invokeai/app/invocations/ideogram4_model_loader.py create mode 100644 invokeai/app/invocations/ideogram4_text_encoder.py diff --git a/invokeai/app/api/dependencies.py b/invokeai/app/api/dependencies.py index e7468c1bca4..84cc7a574fa 100644 --- a/invokeai/app/api/dependencies.py +++ b/invokeai/app/api/dependencies.py @@ -59,6 +59,7 @@ CogView4ConditioningInfo, ConditioningFieldData, FLUXConditioningInfo, + Ideogram4ConditioningInfo, QwenImageConditioningInfo, SD3ConditioningInfo, SDXLConditioningInfo, @@ -150,6 +151,7 @@ def initialize( SD3ConditioningInfo, CogView4ConditioningInfo, ZImageConditioningInfo, + Ideogram4ConditioningInfo, QwenImageConditioningInfo, AnimaConditioningInfo, ], diff --git a/invokeai/app/invocations/fields.py b/invokeai/app/invocations/fields.py index e53aeb417b2..8745e509535 100644 --- a/invokeai/app/invocations/fields.py +++ b/invokeai/app/invocations/fields.py @@ -343,6 +343,12 @@ class ZImageConditioningField(BaseModel): ) +class Ideogram4ConditioningField(BaseModel): + """An Ideogram 4 conditioning tensor primitive value""" + + conditioning_name: str = Field(description="The name of conditioning tensor") + + class QwenImageConditioningField(BaseModel): """A Qwen Image Edit conditioning tensor primitive value""" diff --git a/invokeai/app/invocations/ideogram4_denoise.py b/invokeai/app/invocations/ideogram4_denoise.py new file mode 100644 index 00000000000..1ebe4686a34 --- /dev/null +++ b/invokeai/app/invocations/ideogram4_denoise.py @@ -0,0 +1,87 @@ +from typing import Literal + +import torch + +from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation +from invokeai.app.invocations.fields import ( + FieldDescriptions, + Ideogram4ConditioningField, + Input, + InputField, +) +from invokeai.app.invocations.model import TransformerField +from invokeai.app.invocations.primitives import LatentsOutput +from invokeai.app.services.shared.invocation_context import InvocationContext +from invokeai.backend.ideogram4 import run_ideogram4_denoise +from invokeai.backend.ideogram4.sampler_configs import PRESETS +from invokeai.backend.ideogram4.transformer_pair import Ideogram4TransformerPair +from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Ideogram4ConditioningInfo +from invokeai.backend.util.devices import TorchDevice + +# Named sampler presets bundle step count, guidance schedule (with polish tail), and the +# logit-normal schedule mean/std. V4_QUALITY_48 is the reference default. +IDEOGRAM4_SAMPLER_PRESETS = Literal["V4_QUALITY_48", "V4_DEFAULT_20", "V4_TURBO_12"] + + +@invocation( + "ideogram4_denoise", + title="Denoise - Ideogram 4", + tags=["image", "ideogram4"], + category="latents", + version="1.0.0", + classification=Classification.Prototype, +) +class Ideogram4DenoiseInvocation(BaseInvocation): + """Runs the Ideogram 4 dual-branch flow-matching denoising loop (text-to-image).""" + + transformer: TransformerField = InputField( + description=FieldDescriptions.transformer, input=Input.Connection, title="Transformer" + ) + positive_conditioning: Ideogram4ConditioningField = InputField( + description=FieldDescriptions.positive_cond, input=Input.Connection + ) + sampler_preset: IDEOGRAM4_SAMPLER_PRESETS = InputField( + default="V4_QUALITY_48", + description="Sampler preset (steps + guidance schedule + schedule mean/std).", + title="Sampler Preset", + ) + width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.") + height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.") + seed: int = InputField(default=0, description="Randomness seed for reproducibility.") + + @torch.no_grad() + def invoke(self, context: InvocationContext) -> LatentsOutput: + device = TorchDevice.choose_torch_device() + preset = PRESETS[self.sampler_preset] + + # Load conditioning (the stacked Qwen3-VL features). + cond_data = context.conditioning.load(self.positive_conditioning.conditioning_name) + assert len(cond_data.conditionings) == 1 + info = cond_data.conditionings[0] + assert isinstance(info, Ideogram4ConditioningInfo) + llm_features = info.prompt_embeds.to(device=device, dtype=torch.float32) + + def step_callback(step: int, total: int, _latents: torch.Tensor) -> None: + context.util.signal_progress("Running Ideogram 4 denoising", step / total) + + transformer_info = context.models.load(self.transformer.transformer) + with transformer_info.model_on_device() as (_, transformers): + assert isinstance(transformers, Ideogram4TransformerPair) + packed = run_ideogram4_denoise( + conditional_transformer=transformers.conditional, + unconditional_transformer=transformers.unconditional, + llm_features=llm_features, + height=self.height, + width=self.width, + num_steps=preset.num_steps, + mu=preset.mu, + std=preset.std, + guidance_schedule=preset.guidance_schedule, + seed=self.seed, + device=device, + step_callback=step_callback, + ) + + packed = packed.detach().to("cpu") + name = context.tensors.save(tensor=packed) + return LatentsOutput.build(latents_name=name, latents=packed, seed=None) diff --git a/invokeai/app/invocations/ideogram4_latents_to_image.py b/invokeai/app/invocations/ideogram4_latents_to_image.py new file mode 100644 index 00000000000..2b8120b6c1b --- /dev/null +++ b/invokeai/app/invocations/ideogram4_latents_to_image.py @@ -0,0 +1,66 @@ +import torch +from einops import rearrange +from PIL import Image + +from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation +from invokeai.app.invocations.fields import ( + FieldDescriptions, + Input, + InputField, + LatentsField, + WithBoard, + WithMetadata, +) +from invokeai.app.invocations.model import VAEField +from invokeai.app.invocations.primitives import ImageOutput +from invokeai.app.services.shared.invocation_context import InvocationContext +from invokeai.backend.ideogram4.autoencoder import AutoEncoder +from invokeai.backend.ideogram4.latent_norm import get_latent_norm +from invokeai.backend.ideogram4.sampling_utils import unpatchify_and_denormalize +from invokeai.backend.util.devices import TorchDevice + + +@invocation( + "ideogram4_l2i", + title="Latents to Image - Ideogram 4", + tags=["latents", "image", "vae", "l2i", "ideogram4"], + category="latents", + version="1.0.0", + classification=Classification.Prototype, +) +class Ideogram4LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard): + """Decodes Ideogram 4 packed latents to an image with the FLUX.2-style VAE.""" + + latents: LatentsField = InputField(description=FieldDescriptions.latents, input=Input.Connection) + vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection) + + @torch.no_grad() + def invoke(self, context: InvocationContext) -> ImageOutput: + # Packed latents from denoise: (1, 128, grid_h, grid_w). + latents = context.tensors.load(self.latents.latents_name) + device = TorchDevice.choose_torch_device() + + vae_info = context.models.load(self.vae.vae) + latent_shift, latent_scale = get_latent_norm() + + with vae_info.model_on_device() as (_, vae): + assert isinstance(vae, AutoEncoder), ( + f"Expected Ideogram 4 AutoEncoder, got {type(vae).__name__}." + ) + context.util.signal_progress("Running VAE") + vae_dtype = next(vae.parameters()).dtype + + # Denormalize + unpatchify to a standard (1, 32, H/8, W/8) latent. + z = unpatchify_and_denormalize( + latents.float().to(device), latent_shift.to(device), latent_scale.to(device) + ) + TorchDevice.empty_cache() + decoded = vae.decoder(z.to(vae_dtype)) + + img = decoded.float().clamp(-1.0, 1.0) + img = rearrange(img[0], "c h w -> h w c") + img_pil = Image.fromarray((127.5 * (img + 1.0)).byte().cpu().numpy()) + + TorchDevice.empty_cache() + image_dto = context.images.save(image=img_pil) + return ImageOutput.build(image_dto) diff --git a/invokeai/app/invocations/ideogram4_model_loader.py b/invokeai/app/invocations/ideogram4_model_loader.py new file mode 100644 index 00000000000..45927214080 --- /dev/null +++ b/invokeai/app/invocations/ideogram4_model_loader.py @@ -0,0 +1,64 @@ +from invokeai.app.invocations.baseinvocation import ( + BaseInvocation, + BaseInvocationOutput, + Classification, + invocation, + invocation_output, +) +from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField +from invokeai.app.invocations.model import ( + ModelIdentifierField, + Qwen3EncoderField, + TransformerField, + VAEField, +) +from invokeai.app.services.shared.invocation_context import InvocationContext +from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType, SubModelType + + +@invocation_output("ideogram4_model_loader_output") +class Ideogram4ModelLoaderOutput(BaseInvocationOutput): + """Ideogram 4 model loader output.""" + + transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer") + qwen3_encoder: Qwen3EncoderField = OutputField( + description=FieldDescriptions.qwen3_encoder, title="Qwen3-VL Encoder" + ) + vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE") + + +@invocation( + "ideogram4_model_loader", + title="Main Model - Ideogram 4", + tags=["model", "ideogram4"], + category="model", + version="1.0.0", + classification=Classification.Prototype, +) +class Ideogram4ModelLoaderInvocation(BaseInvocation): + """Loads an Ideogram 4 model, outputting its submodels. + + Ideogram 4 is distributed as a single bundled diffusers folder, so the transformer + (both branches), the Qwen3-VL text encoder + tokenizer, and the VAE are all loaded + from the one selected model. + """ + + model: ModelIdentifierField = InputField( + description="The Ideogram 4 model to load.", + input=Input.Direct, + ui_model_base=BaseModelType.Ideogram4, + ui_model_type=ModelType.Main, + title="Model", + ) + + def invoke(self, context: InvocationContext) -> Ideogram4ModelLoaderOutput: + transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer}) + text_encoder = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder}) + tokenizer = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer}) + vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE}) + + return Ideogram4ModelLoaderOutput( + transformer=TransformerField(transformer=transformer, loras=[]), + qwen3_encoder=Qwen3EncoderField(tokenizer=tokenizer, text_encoder=text_encoder), + vae=VAEField(vae=vae), + ) diff --git a/invokeai/app/invocations/ideogram4_text_encoder.py b/invokeai/app/invocations/ideogram4_text_encoder.py new file mode 100644 index 00000000000..ca2a809f1bb --- /dev/null +++ b/invokeai/app/invocations/ideogram4_text_encoder.py @@ -0,0 +1,60 @@ +from contextlib import ExitStack + +import torch + +from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation +from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, UIComponent +from invokeai.app.invocations.model import Qwen3EncoderField +from invokeai.app.invocations.primitives import Ideogram4ConditioningOutput +from invokeai.app.services.shared.invocation_context import InvocationContext +from invokeai.backend.ideogram4.text_encoding import encode_qwen3vl_prompt +from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ( + ConditioningFieldData, + Ideogram4ConditioningInfo, +) + + +@invocation( + "ideogram4_text_encoder", + title="Prompt - Ideogram 4", + tags=["prompt", "conditioning", "ideogram4"], + category="conditioning", + version="1.0.0", + classification=Classification.Prototype, +) +class Ideogram4TextEncoderInvocation(BaseInvocation): + """Encodes a prompt for Ideogram 4 using the Qwen3-VL encoder. + + The prompt is normally a structured JSON caption (see the Ideogram 4 prompting guide); + plain text also works but yields lower-quality results. + """ + + prompt: str = InputField( + description="The prompt to encode. A structured JSON caption is recommended.", + ui_component=UIComponent.Textarea, + ) + qwen3_encoder: Qwen3EncoderField = InputField( + title="Qwen3-VL Encoder", + description=FieldDescriptions.qwen3_encoder, + input=Input.Connection, + ) + + @torch.no_grad() + def invoke(self, context: InvocationContext) -> Ideogram4ConditioningOutput: + text_encoder_info = context.models.load(self.qwen3_encoder.text_encoder) + tokenizer_info = context.models.load(self.qwen3_encoder.tokenizer) + + with ExitStack() as exit_stack: + (_, text_encoder) = exit_stack.enter_context(text_encoder_info.model_on_device()) + (_, tokenizer) = exit_stack.enter_context(tokenizer_info.model_on_device()) + + context.util.signal_progress("Running Qwen3-VL text encoder") + prompt_embeds = encode_qwen3vl_prompt(self.prompt, tokenizer, text_encoder) + + # Move to CPU for storage to save VRAM. + prompt_embeds = prompt_embeds.detach().to("cpu") + conditioning_data = ConditioningFieldData( + conditionings=[Ideogram4ConditioningInfo(prompt_embeds=prompt_embeds)] + ) + conditioning_name = context.conditioning.save(conditioning_data) + return Ideogram4ConditioningOutput.build(conditioning_name) diff --git a/invokeai/app/invocations/metadata.py b/invokeai/app/invocations/metadata.py index da24d8802bb..046e65954d3 100644 --- a/invokeai/app/invocations/metadata.py +++ b/invokeai/app/invocations/metadata.py @@ -166,6 +166,7 @@ def invoke(self, context: InvocationContext) -> MetadataOutput: "z_image_img2img", "z_image_inpaint", "z_image_outpaint", + "ideogram4_txt2img", "qwen_image_txt2img", "qwen_image_img2img", "qwen_image_inpaint", diff --git a/invokeai/app/invocations/primitives.py b/invokeai/app/invocations/primitives.py index 7ec6c3dc149..5137b5e010d 100644 --- a/invokeai/app/invocations/primitives.py +++ b/invokeai/app/invocations/primitives.py @@ -29,6 +29,7 @@ SD3ConditioningField, TensorField, UIComponent, + Ideogram4ConditioningField, ZImageConditioningField, ) from invokeai.app.services.images.images_common import ImageDTO @@ -475,6 +476,17 @@ def build(cls, conditioning_name: str) -> "ZImageConditioningOutput": return cls(conditioning=ZImageConditioningField(conditioning_name=conditioning_name)) +@invocation_output("ideogram4_conditioning_output") +class Ideogram4ConditioningOutput(BaseInvocationOutput): + """Base class for nodes that output an Ideogram 4 text conditioning tensor.""" + + conditioning: Ideogram4ConditioningField = OutputField(description=FieldDescriptions.cond) + + @classmethod + def build(cls, conditioning_name: str) -> "Ideogram4ConditioningOutput": + return cls(conditioning=Ideogram4ConditioningField(conditioning_name=conditioning_name)) + + @invocation_output("qwen_image_conditioning_output") class QwenImageConditioningOutput(BaseInvocationOutput): """Base class for nodes that output a Qwen Image Edit conditioning tensor.""" diff --git a/invokeai/backend/model_manager/load/model_loaders/ideogram4.py b/invokeai/backend/model_manager/load/model_loaders/ideogram4.py index dd35fb47060..a764b6e6f6c 100644 --- a/invokeai/backend/model_manager/load/model_loaders/ideogram4.py +++ b/invokeai/backend/model_manager/load/model_loaders/ideogram4.py @@ -128,38 +128,51 @@ def _load_one_transformer(self, folder: Path) -> torch.nn.Module: return model.to(compute_dtype) def _load_text_encoder(self, model_path: Path) -> AnyModel: - from transformers import Qwen3VLModel + import accelerate + from transformers import AutoConfig, AutoModel + + from invokeai.backend.quantization.bnb_nf4 import quantize_model_nf4 encoder_path = model_path / "text_encoder" target_device = TorchDevice.choose_torch_device() - model_dtype = TorchDevice.choose_bfloat16_safe_dtype(target_device) + compute_dtype = TorchDevice.choose_bfloat16_safe_dtype(target_device) - cfg = json.loads((encoder_path / "config.json").read_text(encoding="utf-8")) - if cfg.get("ideogram_fp8_weight_only", False): - # The fp8 text encoder uses Ideogram's custom weight-only fp8 layout, which transformers' - # from_pretrained cannot read. Supporting it requires the vendored _load_fp8_text_encoder - # path; deferred until the fp8 build is targeted (nf4 is the 24 GB path). + raw_cfg = json.loads((encoder_path / "config.json").read_text(encoding="utf-8")) + if raw_cfg.get("ideogram_fp8_weight_only", False): + # The fp8 text encoder uses Ideogram's custom weight-only fp8 layout; supporting it + # requires the vendored _load_fp8_text_encoder path. Deferred (nf4 is the 24 GB path). raise NotImplementedError( "Ideogram 4 fp8 text encoder loading is not yet implemented; use the nf4 build." ) - if "quantization_config" in cfg: - # bitsandbytes (nf4): transformers applies the quantization from config.json. bnb 4-bit - # requires a CUDA device, so place it there directly via device_map. - return Qwen3VLModel.from_pretrained( - encoder_path, - torch_dtype=model_dtype, - device_map={"": target_device}, - local_files_only=True, - ) + # Build the bare architecture from config, then quantize with InvokeAI's InvokeLinearNF4 and + # load the prequantized weights. We must NOT use transformers' native bitsandbytes loading + # (from_pretrained with a quantization_config) because the resulting bnb Linear4bit layers are + # not compatible with InvokeAI's partial-loading model cache. This mirrors how the FLUX T5 bnb + # encoder is loaded. + cfg = AutoConfig.from_pretrained(encoder_path, local_files_only=True) + # Drop the quantization_config so from_config builds a plain (unquantized) architecture. + if hasattr(cfg, "quantization_config"): + cfg.quantization_config = None + + sd = _load_local_state_dict(encoder_path, "model") + self._ram_cache.make_room(sum(t.nelement() * t.element_size() for t in sd.values())) - return Qwen3VLModel.from_pretrained( - encoder_path, - torch_dtype=model_dtype, - low_cpu_mem_usage=True, - local_files_only=True, + is_bnb_nf4 = "quantization_config" in raw_cfg and bool( + raw_cfg["quantization_config"].get("load_in_4bit") ) + with accelerate.init_empty_weights(): + model: torch.nn.Module = AutoModel.from_config(cfg) + if is_bnb_nf4: + model = quantize_model_nf4(model, modules_to_not_convert=set(), compute_dtype=compute_dtype) + + model.load_state_dict(sd, strict=False, assign=True) + if not is_bnb_nf4: + model = model.to(compute_dtype) + model.eval() + return model + def _load_vae(self, model_path: Path) -> AnyModel: from invokeai.backend.ideogram4.autoencoder import ( AutoEncoder, diff --git a/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py b/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py index 6a9959f1e87..0efec4dcbc0 100644 --- a/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py +++ b/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py @@ -88,6 +88,21 @@ def to(self, device: torch.device | None = None, dtype: torch.dtype | None = Non return self +@dataclass +class Ideogram4ConditioningInfo: + """Ideogram 4 text conditioning from the Qwen3-VL encoder. + + prompt_embeds is the concatenation of hidden states from 13 Qwen3-VL layers. + Shape: (seq_len, 53248) where 53248 = 4096 * 13. + """ + + prompt_embeds: torch.Tensor + + def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None): + self.prompt_embeds = self.prompt_embeds.to(device=device, dtype=dtype) + return self + + @dataclass class QwenImageConditioningInfo: """Qwen Image Edit conditioning information from Qwen2.5-VL encoder.""" @@ -142,6 +157,7 @@ class ConditioningFieldData: | List[SD3ConditioningInfo] | List[CogView4ConditioningInfo] | List[ZImageConditioningInfo] + | List[Ideogram4ConditioningInfo] | List[QwenImageConditioningInfo] | List[AnimaConditioningInfo] ) From fe360df82e1e8236b6d766ad0e92effe28e71655 Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Thu, 25 Jun 2026 05:29:25 +0200 Subject: [PATCH 13/33] =?UTF-8?q?feat(ideogram4):=20frontend=20=E2=80=94?= =?UTF-8?q?=20Regions=E2=86=92JSON=20prompt,=20graph=20builder,=20UI?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Wires Ideogram 4 into the canvas/generate UI. buildIdeogram4Prompt assembles the structured JSON caption from the global prompt + Canvas Regional Guidance layers (each region → an obj element with a 0–1000 bbox + desc), with raw-JSON passthrough and a plain-text fallback when there are no regions. Adds buildIdeogram4Graph (text-to-image only, no negative prompt) and the enqueue switch. Structured captions use a static string node + a decoy positive-prompt node so the linear batch can't clobber the assembled JSON; plain text uses the real node so dynamic prompts/batching still work. Registers the 'ideogram-4' base (enums, color, names, model picker, grid size 16), a sampler-preset param (V4_QUALITY_48/V4_DEFAULT_20/V4_TURBO_12) replacing the steps/CFG controls, ParamIdeogram4SamplerPreset, and metadata recall. Regenerates schema.ts. --- invokeai/frontend/web/public/locales/en.json | 1 + .../controlLayers/store/paramsSlice.ts | 11 + .../src/features/controlLayers/store/types.ts | 4 + .../web/src/features/metadata/parsing.tsx | 28 ++ .../web/src/features/modelManagerV2/models.ts | 3 + .../web/src/features/nodes/types/common.ts | 6 + .../graph/generation/buildIdeogram4Graph.ts | 152 ++++++ .../generation/buildIdeogram4Prompt.test.ts | 94 ++++ .../graph/generation/buildIdeogram4Prompt.ts | 137 ++++++ .../src/features/nodes/util/graph/types.ts | 1 + .../Core/ParamIdeogram4SamplerPreset.tsx | 42 ++ .../parameters/components/ModelPicker.tsx | 13 +- .../parameters/types/parameterSchemas.ts | 7 + .../parameters/util/optimalDimension.ts | 2 + .../features/queue/hooks/useEnqueueCanvas.ts | 3 + .../queue/hooks/useEnqueueGenerate.ts | 3 + .../GenerationSettingsAccordion.tsx | 7 +- .../frontend/web/src/services/api/schema.ts | 445 ++++++++++++++++-- 18 files changed, 924 insertions(+), 35 deletions(-) create mode 100644 invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts create mode 100644 invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.test.ts create mode 100644 invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts create mode 100644 invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx diff --git a/invokeai/frontend/web/public/locales/en.json b/invokeai/frontend/web/public/locales/en.json index 75367a502db..b644311dd47 100644 --- a/invokeai/frontend/web/public/locales/en.json +++ b/invokeai/frontend/web/public/locales/en.json @@ -1043,6 +1043,7 @@ "qwen3Source": "Qwen3 Source", "recallParameters": "Recall Parameters", "recallParameter": "Recall {{label}}", + "ideogram4SamplerPreset": "Sampler Preset", "scheduler": "Scheduler", "seamlessXAxis": "Seamless X Axis", "seamlessYAxis": "Seamless Y Axis", diff --git a/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts b/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts index c4c90cf98e7..9925d5dc2fc 100644 --- a/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts +++ b/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts @@ -39,6 +39,7 @@ import type { ParameterControlLoRAModel, ParameterFluxDypePreset, ParameterGuidance, + ParameterIdeogram4SamplerPreset, ParameterModel, ParameterNegativePrompt, ParameterPositivePrompt, @@ -98,6 +99,9 @@ const slice = createSlice({ setZImageShift: (state, action: PayloadAction) => { state.zImageShift = action.payload; }, + setIdeogram4SamplerPreset: (state, action: PayloadAction) => { + state.ideogram4SamplerPreset = action.payload; + }, setZImageSeedVarianceEnabled: (state, action: PayloadAction) => { state.zImageSeedVarianceEnabled = action.payload; }, @@ -645,6 +649,7 @@ export const { setFluxDypeExponent, setZImageScheduler, setZImageShift, + setIdeogram4SamplerPreset, setZImageSeedVarianceEnabled, setZImageSeedVarianceStrength, setZImageSeedVarianceRandomizePercent, @@ -758,6 +763,7 @@ export const selectIsFLUX = createParamsSelector((params) => params.model?.base export const selectIsSD3 = createParamsSelector((params) => params.model?.base === 'sd-3'); export const selectIsCogView4 = createParamsSelector((params) => params.model?.base === 'cogview4'); export const selectIsZImage = createParamsSelector((params) => params.model?.base === 'z-image'); +export const selectIsIdeogram4 = createParamsSelector((params) => params.model?.base === 'ideogram-4'); export const selectIsAnima = createParamsSelector((params) => params.model?.base === 'anima'); export const selectIsFlux2 = createParamsSelector((params) => params.model?.base === 'flux2'); export const selectIsExternal = createParamsSelector((params) => params.model?.base === 'external'); @@ -882,6 +888,10 @@ export const selectModelSupportsSteps = createSelector(selectModel, (model) => { if (model.base === 'external') { return false; } + if (model.base === 'ideogram-4') { + // Ideogram 4 bundles step count into its sampler preset, so there is no standalone steps control. + return false; + } return true; }); export const selectModelSupportsDimensions = createSelector(selectModel, selectModelConfig, (model, modelConfig) => { @@ -906,6 +916,7 @@ export const selectFluxDypeScale = createParamsSelector((params) => params.fluxD export const selectFluxDypeExponent = createParamsSelector((params) => params.fluxDypeExponent); export const selectZImageScheduler = createParamsSelector((params) => params.zImageScheduler); export const selectZImageShift = createParamsSelector((params) => params.zImageShift); +export const selectIdeogram4SamplerPreset = createParamsSelector((params) => params.ideogram4SamplerPreset); export const selectZImageSeedVarianceEnabled = createParamsSelector((params) => params.zImageSeedVarianceEnabled); export const selectZImageSeedVarianceStrength = createParamsSelector((params) => params.zImageSeedVarianceStrength); export const selectZImageSeedVarianceRandomizePercent = createParamsSelector( diff --git a/invokeai/frontend/web/src/features/controlLayers/store/types.ts b/invokeai/frontend/web/src/features/controlLayers/store/types.ts index 09ce177ab0f..6147ad381d2 100644 --- a/invokeai/frontend/web/src/features/controlLayers/store/types.ts +++ b/invokeai/frontend/web/src/features/controlLayers/store/types.ts @@ -15,6 +15,7 @@ import { zParameterFluxDypeScale, zParameterFluxScheduler, zParameterGuidance, + zParameterIdeogram4SamplerPreset, zParameterImageDimension, zParameterMaskBlurMethod, zParameterModel, @@ -804,6 +805,8 @@ export const zParamsState = z.object({ fluxDypeExponent: zParameterFluxDypeExponent, zImageScheduler: zParameterZImageScheduler, zImageShift: z.number().min(0).max(3).nullable(), + // Default makes this resilient to rehydration of persisted state saved before this field existed. + ideogram4SamplerPreset: zParameterIdeogram4SamplerPreset.default('V4_QUALITY_48'), upscaleScheduler: zParameterScheduler, upscaleCfgScale: zParameterCFGScale, seed: zParameterSeed, @@ -892,6 +895,7 @@ export const getInitialParamsState = (): ParamsState => ({ fluxDypeExponent: 2.0, zImageScheduler: 'euler', zImageShift: null, + ideogram4SamplerPreset: 'V4_QUALITY_48', upscaleScheduler: 'kdpm_2', upscaleCfgScale: 2, seed: 0, diff --git a/invokeai/frontend/web/src/features/metadata/parsing.tsx b/invokeai/frontend/web/src/features/metadata/parsing.tsx index d643c838b2c..e8da3946b6f 100644 --- a/invokeai/frontend/web/src/features/metadata/parsing.tsx +++ b/invokeai/frontend/web/src/features/metadata/parsing.tsx @@ -39,6 +39,7 @@ import { setFluxDypeScale, setFluxScheduler, setGuidance, + setIdeogram4SamplerPreset, setImg2imgStrength, setRefinerCFGScale, setRefinerNegativeAestheticScore, @@ -78,6 +79,7 @@ import type { ParameterFluxDypeScale, ParameterGuidance, ParameterHeight, + ParameterIdeogram4SamplerPreset, ParameterModel, ParameterNegativePrompt, ParameterPositivePrompt, @@ -103,6 +105,7 @@ import { zParameterFluxDypePreset, zParameterFluxDypeScale, zParameterGuidance, + zParameterIdeogram4SamplerPreset, zParameterImageDimension, zParameterNegativePrompt, zParameterPositivePrompt, @@ -878,6 +881,30 @@ const ZImageShift: SingleMetadataHandler = { }; //#endregion ZImageShift +//#region Ideogram4SamplerPreset +const Ideogram4SamplerPreset: SingleMetadataHandler = { + [SingleMetadataKey]: true, + type: 'Ideogram4SamplerPreset', + parse: (metadata, _store) => { + const raw = getProperty(metadata, 'ideogram4_sampler_preset'); + const parsed = zParameterIdeogram4SamplerPreset.parse(raw); + return Promise.resolve(parsed); + }, + recall: (value, store) => { + // Only recall onto an Ideogram 4 model so we don't set this (otherwise hidden) field for other bases. + if (selectBase(store.getState()) !== 'ideogram-4') { + return; + } + store.dispatch(setIdeogram4SamplerPreset(value)); + }, + i18nKey: 'parameters.ideogram4SamplerPreset', + LabelComponent: MetadataLabel, + ValueComponent: ({ value }: SingleMetadataValueProps) => ( + + ), +}; +//#endregion Ideogram4SamplerPreset + //#region RefinerModel const RefinerModel: SingleMetadataHandler = { [SingleMetadataKey]: true, @@ -1641,6 +1668,7 @@ export const ImageMetadataHandlers = { QwenImageQuantization, QwenImageShift, ZImageShift, + Ideogram4SamplerPreset, LoRAs, CanvasLayers, RefImages, diff --git a/invokeai/frontend/web/src/features/modelManagerV2/models.ts b/invokeai/frontend/web/src/features/modelManagerV2/models.ts index cf295c9af6a..a38cd35dd9a 100644 --- a/invokeai/frontend/web/src/features/modelManagerV2/models.ts +++ b/invokeai/frontend/web/src/features/modelManagerV2/models.ts @@ -163,6 +163,7 @@ export const MODEL_BASE_TO_COLOR: Record = { cogview4: 'red', 'qwen-image': 'orange', 'z-image': 'cyan', + 'ideogram-4': 'pink', external: 'orange', anima: 'invokePurple', unknown: 'red', @@ -210,6 +211,7 @@ export const MODEL_BASE_TO_LONG_NAME: Record = { cogview4: 'CogView4', 'qwen-image': 'Qwen Image', 'z-image': 'Z-Image', + 'ideogram-4': 'Ideogram 4', external: 'External', anima: 'Anima', unknown: 'Unknown', @@ -230,6 +232,7 @@ export const MODEL_BASE_TO_SHORT_NAME: Record = { cogview4: 'CogView4', 'qwen-image': 'QwenImg', 'z-image': 'Z-Image', + 'ideogram-4': 'Ideogram4', external: 'External', anima: 'Anima', unknown: 'Unknown', diff --git a/invokeai/frontend/web/src/features/nodes/types/common.ts b/invokeai/frontend/web/src/features/nodes/types/common.ts index fb2a1ce946a..21c7b254486 100644 --- a/invokeai/frontend/web/src/features/nodes/types/common.ts +++ b/invokeai/frontend/web/src/features/nodes/types/common.ts @@ -75,6 +75,10 @@ export const zZImageSchedulerField = z.enum(['euler', 'heun', 'lcm']); // Anima scheduler options (same flow-matching schedulers, defined separately to avoid coupling) export const zAnimaSchedulerField = z.enum(['euler', 'heun', 'dpmpp_2m', 'dpmpp_2m_sde', 'er_sde', 'lcm']); +// Ideogram 4 sampler presets. Each bundles step count, the per-step guidance schedule (with a polish +// tail), and the logit-normal schedule mean/std. V4_QUALITY_48 is the reference default. +export const zIdeogram4SamplerPresetField = z.enum(['V4_QUALITY_48', 'V4_DEFAULT_20', 'V4_TURBO_12']); + // Flux DyPE (Dynamic Position Extrapolation) preset options for high-resolution generation export const zFluxDypePresetField = z.enum(['off', 'manual', 'auto', 'area', '4k']); @@ -98,6 +102,7 @@ export const zBaseModelType = z.enum([ 'cogview4', 'qwen-image', 'z-image', + 'ideogram-4', 'external', 'anima', 'unknown', @@ -113,6 +118,7 @@ export const zMainModelBase = z.enum([ 'cogview4', 'qwen-image', 'z-image', + 'ideogram-4', 'anima', ]); type MainModelBase = z.infer; diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts new file mode 100644 index 00000000000..7f701c5a878 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts @@ -0,0 +1,152 @@ +import { objectEquals } from '@observ33r/object-equals'; +import { logger } from 'app/logging/logger'; +import { getPrefixedId } from 'features/controlLayers/konva/util'; +import { selectIdeogram4SamplerPreset, selectMainModelConfig } from 'features/controlLayers/store/paramsSlice'; +import { selectCanvasMetadata } from 'features/controlLayers/store/selectors'; +import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers'; +import { addNSFWChecker } from 'features/nodes/util/graph/generation/addNSFWChecker'; +import { addWatermarker } from 'features/nodes/util/graph/generation/addWatermarker'; +import { buildIdeogram4Prompt } from 'features/nodes/util/graph/generation/buildIdeogram4Prompt'; +import { Graph } from 'features/nodes/util/graph/generation/Graph'; +import { + getOriginalAndScaledSizesForTextToImage, + selectCanvasOutputFields, +} from 'features/nodes/util/graph/graphBuilderUtils'; +import type { GraphBuilderArg, GraphBuilderReturn, ImageOutputNodes } from 'features/nodes/util/graph/types'; +import { selectActiveTab } from 'features/ui/store/uiSelectors'; +import type { Invocation } from 'services/api/types'; +import { isNonRefinerMainModelConfig } from 'services/api/types'; +import { assert } from 'tsafe'; + +const log = logger('system'); + +/** + * Builds the graph for Ideogram 4 generation. Ideogram 4 is text-to-image only and prompted with a + * structured JSON caption (assembled from the global prompt + Canvas Regional Guidance layers; see + * buildIdeogram4Prompt). There is no negative prompt (the reference uses an asymmetric CFG with a + * zeroed unconditional branch), and no img2img/inpaint/outpaint or mask conditioning. + */ +export const buildIdeogram4Graph = async (arg: GraphBuilderArg): Promise => { + const { generationMode, state, manager } = arg; + + log.debug({ generationMode, manager: manager?.id }, 'Building Ideogram 4 graph'); + + assert(generationMode === 'txt2img', 'Ideogram 4 only supports text-to-image generation'); + + const model = selectMainModelConfig(state); + assert(model, 'No model selected'); + assert(model.base === 'ideogram-4', 'Selected model is not an Ideogram 4 model'); + + const samplerPreset = selectIdeogram4SamplerPreset(state); + + // Assemble the prompt: raw-JSON passthrough, a structured caption built from Regional Guidance + // layers, or plain text when there are no regions. + const { prompt, isStructured } = buildIdeogram4Prompt(state, manager); + + const g = new Graph(getPrefixedId('ideogram4_graph')); + + const modelLoader = g.addNode({ + type: 'ideogram4_model_loader', + id: getPrefixedId('ideogram4_model_loader'), + model, + }); + + const promptNode = g.addNode({ + id: getPrefixedId('ideogram4_prompt'), + type: 'string', + value: prompt, + }); + + const textEncoder = g.addNode({ + type: 'ideogram4_text_encoder', + id: getPrefixedId('ideogram4_text_encoder'), + }); + + const seed = g.addNode({ + id: getPrefixedId('seed'), + type: 'integer', + }); + + const denoise = g.addNode({ + type: 'ideogram4_denoise', + id: getPrefixedId('ideogram4_denoise'), + sampler_preset: samplerPreset, + }); + + const l2i = g.addNode({ + type: 'ideogram4_l2i', + id: getPrefixedId('ideogram4_l2i'), + }); + + g.addEdge(modelLoader, 'transformer', denoise, 'transformer'); + g.addEdge(modelLoader, 'qwen3_encoder', textEncoder, 'qwen3_encoder'); + g.addEdge(modelLoader, 'vae', l2i, 'vae'); + g.addEdge(promptNode, 'value', textEncoder, 'prompt'); + g.addEdge(textEncoder, 'conditioning', denoise, 'positive_conditioning'); + g.addEdge(seed, 'value', denoise, 'seed'); + g.addEdge(denoise, 'latents', l2i, 'latents'); + + // Text-to-image dimensions. Ideogram 4 requires multiples of 16 (enforced by the bbox grid size). + const { originalSize, scaledSize } = getOriginalAndScaledSizesForTextToImage(state); + denoise.width = scaledSize.width; + denoise.height = scaledSize.height; + + // The linear batch injects the raw positive prompt (and dynamic-prompt expansions) into the node we + // return as `positivePrompt`. For a structured caption we must NOT let it clobber the assembled JSON, + // so we return a decoy string node; for plain text we return the real prompt node so dynamic prompts + // and prompt batching work normally. + const positivePrompt: Invocation<'string'> = isStructured + ? g.addNode({ id: getPrefixedId('positive_prompt_decoy'), type: 'string' }) + : promptNode; + + const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig); + assert(modelConfig.base === 'ideogram-4'); + + g.upsertMetadata({ + model: Graph.getModelMetadataField(modelConfig), + ideogram4_sampler_preset: samplerPreset, + width: originalSize.width, + height: originalSize.height, + generation_mode: 'ideogram4_txt2img', + }); + // The assembled caption is static; store it for reproducibility when it differs from the raw prompt. + if (isStructured) { + g.upsertMetadata({ ideogram4_caption: prompt }); + } + g.addEdgeToMetadata(seed, 'value', 'seed'); + g.addEdgeToMetadata(positivePrompt, 'value', 'positive_prompt'); + + // Resize the output back to the original size if the canvas used a scaled bbox. + let canvasOutput: Invocation = l2i; + if (!objectEquals(scaledSize, originalSize)) { + const resizeImageToOriginalSize = g.addNode({ + id: getPrefixedId('resize_image_to_original_size'), + type: 'img_resize', + ...originalSize, + }); + g.addEdge(l2i, 'image', resizeImageToOriginalSize, 'image'); + canvasOutput = resizeImageToOriginalSize; + } + + if (state.system.shouldUseNSFWChecker) { + canvasOutput = addNSFWChecker(g, canvasOutput); + } + + if (state.system.shouldUseWatermarker) { + canvasOutput = addWatermarker(g, canvasOutput); + } + + g.updateNode(canvasOutput, selectCanvasOutputFields(state)); + + if (selectActiveTab(state) === 'canvas') { + g.upsertMetadata(selectCanvasMetadata(state)); + } + + g.setMetadataReceivingNode(canvasOutput); + + return { + g, + seed, + positivePrompt, + }; +}; diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.test.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.test.ts new file mode 100644 index 00000000000..0111c0856d3 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.test.ts @@ -0,0 +1,94 @@ +import type { Rect } from 'features/controlLayers/store/types'; +import { describe, expect, it } from 'vitest'; + +import type { Ideogram4RegionInput } from './buildIdeogram4Prompt'; +import { buildIdeogram4Caption, rectToIdeogram4Bbox } from './buildIdeogram4Prompt'; + +describe('rectToIdeogram4Bbox', () => { + const genBbox: Rect = { x: 0, y: 0, width: 1024, height: 1024 }; + + it('normalizes a region rect to [y_min, x_min, y_max, x_max] in 0-1000', () => { + // Region occupying the right half, vertically centered band. + const regionRect: Rect = { x: 512, y: 256, width: 512, height: 512 }; + expect(rectToIdeogram4Bbox(regionRect, genBbox)).toEqual([250, 500, 750, 1000]); + }); + + it('accounts for a generation bbox not anchored at the origin', () => { + const offsetBbox: Rect = { x: 100, y: 200, width: 1000, height: 500 }; + const regionRect: Rect = { x: 600, y: 200, width: 500, height: 250 }; + // x: (600-100)/1000=0.5 -> 500, (1100-100)/1000=1.0 -> 1000 + // y: (200-200)/500=0 -> 0, (450-200)/500=0.5 -> 500 + expect(rectToIdeogram4Bbox(regionRect, offsetBbox)).toEqual([0, 500, 500, 1000]); + }); + + it('clamps out-of-bounds regions to the 0-1000 range', () => { + const regionRect: Rect = { x: -200, y: -200, width: 2048, height: 2048 }; + expect(rectToIdeogram4Bbox(regionRect, genBbox)).toEqual([0, 0, 1000, 1000]); + }); + + it('returns zeros for a degenerate (zero-size) generation bbox', () => { + const regionRect: Rect = { x: 10, y: 10, width: 10, height: 10 }; + expect(rectToIdeogram4Bbox(regionRect, { x: 0, y: 0, width: 0, height: 0 })).toEqual([0, 0, 0, 0]); + }); +}); + +describe('buildIdeogram4Caption', () => { + const region = (prompt: string, bbox: Ideogram4RegionInput['bbox']): Ideogram4RegionInput => ({ prompt, bbox }); + + it('passes through a raw JSON object verbatim and marks it structured', () => { + const raw = '{"high_level_description":"a cat","compositional_deconstruction":{"background":"","elements":[]}}'; + expect(buildIdeogram4Caption(raw, [])).toEqual({ prompt: raw, isStructured: true }); + }); + + it('passes through raw JSON even when leading/trailing whitespace is present', () => { + const raw = ' {"a":1} '; + expect(buildIdeogram4Caption(raw, [])).toEqual({ prompt: raw, isStructured: true }); + }); + + it('returns plain text (not structured) when there are no regions', () => { + expect(buildIdeogram4Caption('a golden retriever on a skateboard', [])).toEqual({ + prompt: 'a golden retriever on a skateboard', + isStructured: false, + }); + }); + + it('trims the plain prompt', () => { + expect(buildIdeogram4Caption(' hello world ', [])).toEqual({ prompt: 'hello world', isStructured: false }); + }); + + it('assembles a structured caption from regions with correct key order', () => { + const result = buildIdeogram4Caption('A dog and a ball.', [ + region('a fluffy dog', [200, 300, 800, 900]), + region('a red ball', [250, 750, 750, 950]), + ]); + expect(result.isStructured).toBe(true); + // Key order must be exactly: high_level_description, compositional_deconstruction{background, elements}; + // each obj element: type, bbox, desc. JSON.stringify preserves insertion order, so assert on the raw string. + expect(result.prompt).toBe( + '{"high_level_description":"A dog and a ball.",' + + '"compositional_deconstruction":{"background":"",' + + '"elements":[' + + '{"type":"obj","bbox":[200,300,800,900],"desc":"a fluffy dog"},' + + '{"type":"obj","bbox":[250,750,750,950],"desc":"a red ball"}' + + ']}}' + ); + }); + + it('omits bbox for regions with no drawn content', () => { + const result = buildIdeogram4Caption('scene', [region('floating element', null)]); + expect(result.prompt).toContain('{"type":"obj","desc":"floating element"}'); + }); + + it('skips regions with empty prompts', () => { + const result = buildIdeogram4Caption('scene', [region(' ', [0, 0, 100, 100]), region('kept', [1, 2, 3, 4])]); + const parsed = JSON.parse(result.prompt); + expect(parsed.compositional_deconstruction.elements).toHaveLength(1); + expect(parsed.compositional_deconstruction.elements[0].desc).toBe('kept'); + }); + + it('preserves non-ASCII characters without escaping', () => { + const result = buildIdeogram4Caption('café', [region('a café ☕', [0, 0, 100, 100])]); + expect(result.prompt).toContain('café ☕'); + expect(result.prompt).not.toContain('\\u'); + }); +}); diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts new file mode 100644 index 00000000000..4e9b1efc224 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts @@ -0,0 +1,137 @@ +import type { RootState } from 'app/store/store'; +import type { CanvasManager } from 'features/controlLayers/konva/CanvasManager'; +import { selectPositivePrompt } from 'features/controlLayers/store/paramsSlice'; +import { selectCanvasSlice } from 'features/controlLayers/store/selectors'; +import type { Rect } from 'features/controlLayers/store/types'; + +/** + * Ideogram 4 is prompted with a structured JSON caption describing the scene as a list of regions, + * each with a bounding box and a description. This module assembles that caption from InvokeAI's + * Canvas Regional Guidance layers — the bbox numbers live inside the prompt string (Ideogram 4 does + * not use spatial attention masks), so this is pure string assembly with no backend mask handling. + * + * See the reference prompting guide for the schema; the key points used here: + * - bbox is `[y_min, x_min, y_max, x_max]`, normalized to 0–1000, origin at top-left. + * - Key order matters (the model was trained on a consistent order). `obj` elements use + * `type`, `bbox`, `desc`. We rely on JS object insertion order being preserved by JSON.stringify. + * - The reference serializes with compact separators and `ensure_ascii=False`; JSON.stringify + * already produces compact `,`/`:` separators and preserves non-ASCII characters. + */ + +/** Ideogram 4 normalizes spatial coordinates to a 0–1000 grid with the origin at the top-left. */ +const IDEOGRAM4_COORD_MAX = 1000; + +const clamp = (value: number, min: number, max: number): number => Math.min(max, Math.max(min, value)); + +export type Ideogram4Bbox = [number, number, number, number]; + +/** + * Converts a region's rect (canvas/layer coordinates — the same space as the generation bbox) into an + * Ideogram 4 bounding box `[y_min, x_min, y_max, x_max]`, normalized to 0–1000 relative to the + * generation bbox, clamped and rounded to integers. + */ +export const rectToIdeogram4Bbox = (regionRect: Rect, genBbox: Rect): Ideogram4Bbox => { + const norm = (value: number, origin: number, extent: number): number => + extent <= 0 ? 0 : clamp(Math.round(((value - origin) / extent) * IDEOGRAM4_COORD_MAX), 0, IDEOGRAM4_COORD_MAX); + const yMin = norm(regionRect.y, genBbox.y, genBbox.height); + const xMin = norm(regionRect.x, genBbox.x, genBbox.width); + const yMax = norm(regionRect.y + regionRect.height, genBbox.y, genBbox.height); + const xMax = norm(regionRect.x + regionRect.width, genBbox.x, genBbox.width); + return [yMin, xMin, yMax, xMax]; +}; + +export type Ideogram4RegionInput = { + /** The region's positive prompt — becomes the element's `desc`. */ + prompt: string; + /** The region's normalized bbox, or null when the region has no drawn content. */ + bbox: Ideogram4Bbox | null; +}; + +/** An `obj`-type element. Key order matches the training schema: `type`, `bbox`, `desc`. */ +type Ideogram4Element = { type: 'obj'; bbox: Ideogram4Bbox; desc: string } | { type: 'obj'; desc: string }; + +export type Ideogram4PromptResult = { + /** The final prompt string to feed to the text encoder. */ + prompt: string; + /** + * Whether the prompt is a structured caption (assembled JSON or raw-JSON passthrough). When true, + * the graph builder must NOT let the linear batch inject the raw positive prompt over it, otherwise + * the assembled caption would be clobbered by the plain prompt text. + */ + isStructured: boolean; +}; + +/** + * Assembles an Ideogram 4 prompt from a global prompt and a set of regions. + * + * - Raw-JSON passthrough: if the global prompt is already a JSON object (trimmed, starts with `{`), it + * is used verbatim and treated as structured. + * - With regions: a structured JSON caption is built — the global prompt becomes + * `high_level_description`, and each region becomes an `obj` element with its bbox + desc. + * - Without regions: the plain global prompt is returned (the model accepts plain text). This keeps + * dynamic prompts and prompt batching working, since the caller can let the batch inject it directly. + */ +export const buildIdeogram4Caption = (globalPrompt: string, regions: Ideogram4RegionInput[]): Ideogram4PromptResult => { + const trimmed = globalPrompt.trim(); + + // The user pasted a structured caption (or any JSON object) — use it verbatim. + if (trimmed.startsWith('{')) { + return { prompt: globalPrompt, isStructured: true }; + } + + const elements: Ideogram4Element[] = regions + .filter((region) => region.prompt.trim().length > 0) + .map((region) => + region.bbox ? { type: 'obj', bbox: region.bbox, desc: region.prompt } : { type: 'obj', desc: region.prompt } + ); + + // Nothing regional to encode — fall back to the plain prompt (documented to work). + if (elements.length === 0) { + return { prompt: trimmed, isStructured: false }; + } + + const caption = { + high_level_description: trimmed, + compositional_deconstruction: { + background: '', + elements, + }, + }; + return { prompt: JSON.stringify(caption), isStructured: true }; +}; + +/** + * Reads the global prompt and each enabled Regional Guidance layer (prompt + normalized bbox) from + * canvas state, then assembles the Ideogram 4 prompt. Regions with no drawn content contribute their + * description without a bbox. + */ +export const buildIdeogram4Prompt = (state: RootState, manager: CanvasManager | null): Ideogram4PromptResult => { + const globalPrompt = selectPositivePrompt(state); + + // No canvas manager (e.g. the Generate tab) → no regions to read. + if (manager === null) { + return buildIdeogram4Caption(globalPrompt, []); + } + + const canvas = selectCanvasSlice(state); + const genBbox = canvas.bbox.rect; + + const regions: Ideogram4RegionInput[] = []; + for (const region of canvas.regionalGuidance.entities) { + if (!region.isEnabled) { + continue; + } + const prompt = region.positivePrompt; + if (!prompt || prompt.trim().length === 0) { + continue; + } + const adapter = manager.adapters.regionMasks.get(region.id); + const bbox = + adapter && adapter.renderer.hasObjects() + ? rectToIdeogram4Bbox(adapter.transformer.getRelativeRect(), genBbox) + : null; + regions.push({ prompt, bbox }); + } + + return buildIdeogram4Caption(globalPrompt, regions); +}; diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/types.ts b/invokeai/frontend/web/src/features/nodes/util/graph/types.ts index d6a18f3f9c0..b3683082507 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graph/types.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graph/types.ts @@ -17,6 +17,7 @@ export type ImageOutputNodes = | 'cogview4_l2i' | 'qwen_image_l2i' | 'z_image_l2i' + | 'ideogram4_l2i' | 'anima_l2i'; export type LatentToImageNodes = diff --git a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx new file mode 100644 index 00000000000..a25de5beba3 --- /dev/null +++ b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx @@ -0,0 +1,42 @@ +import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library'; +import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library'; +import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; +import { selectIdeogram4SamplerPreset, setIdeogram4SamplerPreset } from 'features/controlLayers/store/paramsSlice'; +import { isParameterIdeogram4SamplerPreset } from 'features/parameters/types/parameterSchemas'; +import { memo, useCallback, useMemo } from 'react'; +import { useTranslation } from 'react-i18next'; + +// Each preset bundles a step count, the per-step guidance schedule (with a polish tail), and the +// logit-normal schedule mean/std. The primary quality/speed control for Ideogram 4. +const IDEOGRAM4_SAMPLER_PRESET_OPTIONS: ComboboxOption[] = [ + { value: 'V4_QUALITY_48', label: 'Quality (48 steps)' }, + { value: 'V4_DEFAULT_20', label: 'Default (20 steps)' }, + { value: 'V4_TURBO_12', label: 'Turbo (12 steps)' }, +]; + +const ParamIdeogram4SamplerPreset = () => { + const dispatch = useAppDispatch(); + const { t } = useTranslation(); + const samplerPreset = useAppSelector(selectIdeogram4SamplerPreset); + + const onChange = useCallback( + (v) => { + if (!isParameterIdeogram4SamplerPreset(v?.value)) { + return; + } + dispatch(setIdeogram4SamplerPreset(v.value)); + }, + [dispatch] + ); + + const value = useMemo(() => IDEOGRAM4_SAMPLER_PRESET_OPTIONS.find((o) => o.value === samplerPreset), [samplerPreset]); + + return ( + + {t('parameters.ideogram4SamplerPreset')} + + + ); +}; + +export default memo(ParamIdeogram4SamplerPreset); diff --git a/invokeai/frontend/web/src/features/parameters/components/ModelPicker.tsx b/invokeai/frontend/web/src/features/parameters/components/ModelPicker.tsx index 6ecbf1c4a64..149d864ba77 100644 --- a/invokeai/frontend/web/src/features/parameters/components/ModelPicker.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/ModelPicker.tsx @@ -251,7 +251,18 @@ export const ModelPicker = typedMemo( const _options: Group>[] = []; // Add groups in the original order - for (const groupId of ['api', 'flux', 'z-image', 'qwen-image', 'cogview4', 'sdxl', 'sd-3', 'sd-2', 'sd-1']) { + for (const groupId of [ + 'api', + 'flux', + 'z-image', + 'ideogram-4', + 'qwen-image', + 'cogview4', + 'sdxl', + 'sd-3', + 'sd-2', + 'sd-1', + ]) { const group = groups[groupId]; if (group) { // Sort options within each group so starred ones come first diff --git a/invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts b/invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts index eb2f1e6c15b..08e78488916 100644 --- a/invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts +++ b/invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts @@ -8,6 +8,7 @@ import { zFluxDypePresetField, zFluxDypeScaleField, zFluxSchedulerField, + zIdeogram4SamplerPresetField, zModelIdentifierField, zSchedulerField, zZImageSchedulerField, @@ -86,6 +87,12 @@ export const [zParameterAnimaScheduler, isParameterAnimaScheduler] = buildParame export type ParameterAnimaScheduler = z.infer; // #endregion +// #region Ideogram 4 Sampler Preset +export const [zParameterIdeogram4SamplerPreset, isParameterIdeogram4SamplerPreset] = + buildParameter(zIdeogram4SamplerPresetField); +export type ParameterIdeogram4SamplerPreset = z.infer; +// #endregion + // #region Flux DyPE Preset export const [zParameterFluxDypePreset, isParameterFluxDypePreset] = buildParameter(zFluxDypePresetField); export type ParameterFluxDypePreset = z.infer; diff --git a/invokeai/frontend/web/src/features/parameters/util/optimalDimension.ts b/invokeai/frontend/web/src/features/parameters/util/optimalDimension.ts index 2ac59a32e2b..2548f907ac5 100644 --- a/invokeai/frontend/web/src/features/parameters/util/optimalDimension.ts +++ b/invokeai/frontend/web/src/features/parameters/util/optimalDimension.ts @@ -20,6 +20,7 @@ export const getOptimalDimension = (base?: BaseModelType | null): number => { case 'cogview4': case 'qwen-image': case 'z-image': + case 'ideogram-4': case 'anima': default: return 1024; @@ -78,6 +79,7 @@ export const getGridSize = (base?: BaseModelType | null): number => { case 'sd-3': case 'qwen-image': case 'z-image': + case 'ideogram-4': return 16; case 'sd-1': case 'sd-2': diff --git a/invokeai/frontend/web/src/features/queue/hooks/useEnqueueCanvas.ts b/invokeai/frontend/web/src/features/queue/hooks/useEnqueueCanvas.ts index 1229371b6e8..aa8ddad612a 100644 --- a/invokeai/frontend/web/src/features/queue/hooks/useEnqueueCanvas.ts +++ b/invokeai/frontend/web/src/features/queue/hooks/useEnqueueCanvas.ts @@ -17,6 +17,7 @@ import { buildAnimaGraph } from 'features/nodes/util/graph/generation/buildAnima import { buildCogView4Graph } from 'features/nodes/util/graph/generation/buildCogView4Graph'; import { buildExternalGraph } from 'features/nodes/util/graph/generation/buildExternalGraph'; import { buildFLUXGraph } from 'features/nodes/util/graph/generation/buildFLUXGraph'; +import { buildIdeogram4Graph } from 'features/nodes/util/graph/generation/buildIdeogram4Graph'; import { buildQwenImageGraph } from 'features/nodes/util/graph/generation/buildQwenImageGraph'; import { buildSD1Graph } from 'features/nodes/util/graph/generation/buildSD1Graph'; import { buildSD3Graph } from 'features/nodes/util/graph/generation/buildSD3Graph'; @@ -69,6 +70,8 @@ const enqueueCanvas = async (store: AppStore, canvasManager: CanvasManager, prep return await buildQwenImageGraph(graphBuilderArg); case 'z-image': return await buildZImageGraph(graphBuilderArg); + case 'ideogram-4': + return await buildIdeogram4Graph(graphBuilderArg); case 'external': return await buildExternalGraph(graphBuilderArg); case 'anima': diff --git a/invokeai/frontend/web/src/features/queue/hooks/useEnqueueGenerate.ts b/invokeai/frontend/web/src/features/queue/hooks/useEnqueueGenerate.ts index 8b0c30d924f..de165a7a4ef 100644 --- a/invokeai/frontend/web/src/features/queue/hooks/useEnqueueGenerate.ts +++ b/invokeai/frontend/web/src/features/queue/hooks/useEnqueueGenerate.ts @@ -15,6 +15,7 @@ import { buildAnimaGraph } from 'features/nodes/util/graph/generation/buildAnima import { buildCogView4Graph } from 'features/nodes/util/graph/generation/buildCogView4Graph'; import { buildExternalGraph } from 'features/nodes/util/graph/generation/buildExternalGraph'; import { buildFLUXGraph } from 'features/nodes/util/graph/generation/buildFLUXGraph'; +import { buildIdeogram4Graph } from 'features/nodes/util/graph/generation/buildIdeogram4Graph'; import { buildQwenImageGraph } from 'features/nodes/util/graph/generation/buildQwenImageGraph'; import { buildSD1Graph } from 'features/nodes/util/graph/generation/buildSD1Graph'; import { buildSD3Graph } from 'features/nodes/util/graph/generation/buildSD3Graph'; @@ -62,6 +63,8 @@ const enqueueGenerate = async (store: AppStore, prepend: boolean) => { return await buildQwenImageGraph(graphBuilderArg); case 'z-image': return await buildZImageGraph(graphBuilderArg); + case 'ideogram-4': + return await buildIdeogram4Graph(graphBuilderArg); case 'external': return await buildExternalGraph(graphBuilderArg); case 'anima': diff --git a/invokeai/frontend/web/src/features/settingsAccordions/components/GenerationSettingsAccordion/GenerationSettingsAccordion.tsx b/invokeai/frontend/web/src/features/settingsAccordions/components/GenerationSettingsAccordion/GenerationSettingsAccordion.tsx index 220008a38b0..30cd28fc82c 100644 --- a/invokeai/frontend/web/src/features/settingsAccordions/components/GenerationSettingsAccordion/GenerationSettingsAccordion.tsx +++ b/invokeai/frontend/web/src/features/settingsAccordions/components/GenerationSettingsAccordion/GenerationSettingsAccordion.tsx @@ -11,6 +11,7 @@ import { selectIsExternal, selectIsFLUX, selectIsFlux2, + selectIsIdeogram4, selectIsQwenImage, selectIsSD3, selectIsZImage, @@ -26,6 +27,7 @@ import ParamFluxDypePreset from 'features/parameters/components/Core/ParamFluxDy import ParamFluxDypeScale from 'features/parameters/components/Core/ParamFluxDypeScale'; import ParamFluxScheduler from 'features/parameters/components/Core/ParamFluxScheduler'; import ParamGuidance from 'features/parameters/components/Core/ParamGuidance'; +import ParamIdeogram4SamplerPreset from 'features/parameters/components/Core/ParamIdeogram4SamplerPreset'; import ParamQwenImageShift from 'features/parameters/components/Core/ParamQwenImageShift'; import ParamScheduler from 'features/parameters/components/Core/ParamScheduler'; import ParamSteps from 'features/parameters/components/Core/ParamSteps'; @@ -52,6 +54,7 @@ export const GenerationSettingsAccordion = memo(() => { const isSD3 = useAppSelector(selectIsSD3); const isCogView4 = useAppSelector(selectIsCogView4); const isZImage = useAppSelector(selectIsZImage); + const isIdeogram4 = useAppSelector(selectIsIdeogram4); const isExternal = useAppSelector(selectIsExternal); const isQwenImage = useAppSelector(selectIsQwenImage); const isAnima = useAppSelector(selectIsAnima); @@ -103,17 +106,19 @@ export const GenerationSettingsAccordion = memo(() => { !isSD3 && !isCogView4 && !isZImage && + !isIdeogram4 && !isQwenImage && !isAnima && } {!isExternal && (isFLUX || isFlux2) && } {!isExternal && isZImage && } + {!isExternal && isIdeogram4 && } {!isExternal && isAnima && } {modelSupportsSteps && } {isExternal && modelSupportsGuidance && } {!isExternal && isFLUX && modelConfig && !isFluxFillMainModelModelConfig(modelConfig) && ( )} - {!isExternal && !isFLUX && !isFlux2 && } + {!isExternal && !isFLUX && !isFlux2 && !isIdeogram4 && } {!isExternal && isZImage && } {!isExternal && isQwenImage && } {!isExternal && isFLUX && } diff --git a/invokeai/frontend/web/src/services/api/schema.ts b/invokeai/frontend/web/src/services/api/schema.ts index 5726458dc3a..8b77ef7a95d 100644 --- a/invokeai/frontend/web/src/services/api/schema.ts +++ b/invokeai/frontend/web/src/services/api/schema.ts @@ -3549,7 +3549,7 @@ export type components = { */ type: "anima_text_encoder"; }; - AnyModelConfig: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; + AnyModelConfig: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; /** * AppVersion * @description App Version Response @@ -3701,7 +3701,7 @@ export type components = { * fallback/null value `BaseModelType.Any` for these models, instead of making the model base optional. * @enum {string} */ - BaseModelType: "any" | "sd-1" | "sd-2" | "sd-3" | "sdxl" | "sdxl-refiner" | "flux" | "flux2" | "cogview4" | "z-image" | "external" | "qwen-image" | "anima" | "unknown"; + BaseModelType: "any" | "sd-1" | "sd-2" | "sd-3" | "sdxl" | "sdxl-refiner" | "flux" | "flux2" | "cogview4" | "z-image" | "ideogram-4" | "external" | "qwen-image" | "anima" | "unknown"; /** Batch */ Batch: { /** @@ -4352,6 +4352,7 @@ export type components = { /** * Resize To * @description Dimensions to resize the image to, must be stringified tuple of 2 integers. Max total pixel count: 16777216 + * @example "[1024,1024]" */ resize_to?: string | null; /** @@ -7544,7 +7545,7 @@ export type components = { * @description The generation mode that output this image * @default null */ - generation_mode?: ("txt2img" | "img2img" | "inpaint" | "outpaint" | "sdxl_txt2img" | "sdxl_img2img" | "sdxl_inpaint" | "sdxl_outpaint" | "flux_txt2img" | "flux_img2img" | "flux_inpaint" | "flux_outpaint" | "flux2_txt2img" | "flux2_img2img" | "flux2_inpaint" | "flux2_outpaint" | "sd3_txt2img" | "sd3_img2img" | "sd3_inpaint" | "sd3_outpaint" | "cogview4_txt2img" | "cogview4_img2img" | "cogview4_inpaint" | "cogview4_outpaint" | "z_image_txt2img" | "z_image_img2img" | "z_image_inpaint" | "z_image_outpaint" | "qwen_image_txt2img" | "qwen_image_img2img" | "qwen_image_inpaint" | "qwen_image_outpaint" | "anima_txt2img" | "anima_img2img" | "anima_inpaint" | "anima_outpaint") | null; + generation_mode?: ("txt2img" | "img2img" | "inpaint" | "outpaint" | "sdxl_txt2img" | "sdxl_img2img" | "sdxl_inpaint" | "sdxl_outpaint" | "flux_txt2img" | "flux_img2img" | "flux_inpaint" | "flux_outpaint" | "flux2_txt2img" | "flux2_img2img" | "flux2_inpaint" | "flux2_outpaint" | "sd3_txt2img" | "sd3_img2img" | "sd3_inpaint" | "sd3_outpaint" | "cogview4_txt2img" | "cogview4_img2img" | "cogview4_inpaint" | "cogview4_outpaint" | "z_image_txt2img" | "z_image_img2img" | "z_image_inpaint" | "z_image_outpaint" | "ideogram4_txt2img" | "qwen_image_txt2img" | "qwen_image_img2img" | "qwen_image_inpaint" | "qwen_image_outpaint" | "anima_txt2img" | "anima_img2img" | "anima_inpaint" | "anima_outpaint") | null; /** * Positive Prompt * @description The positive prompt parameter @@ -12264,7 +12265,7 @@ export type components = { * @description The nodes in this graph */ nodes?: { - [key: string]: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"]; + [key: string]: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"]; }; /** * Edges @@ -12301,7 +12302,7 @@ export type components = { * @description The results of node executions */ results: { - [key: string]: components["schemas"]["AnimaConditioningOutput"] | components["schemas"]["AnimaLoRALoaderOutput"] | components["schemas"]["AnimaModelLoaderOutput"] | components["schemas"]["BooleanCollectionOutput"] | components["schemas"]["BooleanOutput"] | components["schemas"]["BoundingBoxCollectionOutput"] | components["schemas"]["BoundingBoxOutput"] | components["schemas"]["CLIPOutput"] | components["schemas"]["CLIPSkipInvocationOutput"] | components["schemas"]["CalculateImageTilesOutput"] | components["schemas"]["CogView4ConditioningOutput"] | components["schemas"]["CogView4ModelLoaderOutput"] | components["schemas"]["CollectInvocationOutput"] | components["schemas"]["ColorCollectionOutput"] | components["schemas"]["ColorOutput"] | components["schemas"]["ConditioningCollectionOutput"] | components["schemas"]["ConditioningOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["DenoiseMaskOutput"] | components["schemas"]["FaceMaskOutput"] | components["schemas"]["FaceOffOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["FloatGeneratorOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["Flux2KleinLoRALoaderOutput"] | components["schemas"]["Flux2KleinModelLoaderOutput"] | components["schemas"]["FluxConditioningCollectionOutput"] | components["schemas"]["FluxConditioningOutput"] | components["schemas"]["FluxControlLoRALoaderOutput"] | components["schemas"]["FluxControlNetOutput"] | components["schemas"]["FluxFillOutput"] | components["schemas"]["FluxKontextOutput"] | components["schemas"]["FluxLoRALoaderOutput"] | components["schemas"]["FluxModelLoaderOutput"] | components["schemas"]["FluxReduxOutput"] | components["schemas"]["GradientMaskOutput"] | components["schemas"]["IPAdapterOutput"] | components["schemas"]["IdealSizeOutput"] | components["schemas"]["IfInvocationOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ImageGeneratorOutput"] | components["schemas"]["ImageOutput"] | components["schemas"]["ImagePanelCoordinateOutput"] | components["schemas"]["IntegerCollectionOutput"] | components["schemas"]["IntegerGeneratorOutput"] | components["schemas"]["IntegerOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["LatentsCollectionOutput"] | components["schemas"]["LatentsMetaOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["LoRALoaderOutput"] | components["schemas"]["LoRASelectorOutput"] | components["schemas"]["MDControlListOutput"] | components["schemas"]["MDIPAdapterListOutput"] | components["schemas"]["MDT2IAdapterListOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["MetadataItemOutput"] | components["schemas"]["MetadataOutput"] | components["schemas"]["MetadataToLorasCollectionOutput"] | components["schemas"]["MetadataToModelOutput"] | components["schemas"]["MetadataToSDXLModelOutput"] | components["schemas"]["ModelIdentifierOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PBRMapsOutput"] | components["schemas"]["PairTileImageOutput"] | components["schemas"]["PromptTemplateOutput"] | components["schemas"]["QwenImageConditioningOutput"] | components["schemas"]["QwenImageLoRALoaderOutput"] | components["schemas"]["QwenImageModelLoaderOutput"] | components["schemas"]["SD3ConditioningOutput"] | components["schemas"]["SDXLLoRALoaderOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["SchedulerOutput"] | components["schemas"]["Sd3ModelLoaderOutput"] | components["schemas"]["SeamlessModeOutput"] | components["schemas"]["String2Output"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["StringGeneratorOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["StringPosNegOutput"] | components["schemas"]["T2IAdapterOutput"] | components["schemas"]["TileToPropertiesOutput"] | components["schemas"]["UNetOutput"] | components["schemas"]["VAEOutput"] | components["schemas"]["ZImageConditioningOutput"] | components["schemas"]["ZImageControlOutput"] | components["schemas"]["ZImageLoRALoaderOutput"] | components["schemas"]["ZImageModelLoaderOutput"]; + [key: string]: components["schemas"]["AnimaConditioningOutput"] | components["schemas"]["AnimaLoRALoaderOutput"] | components["schemas"]["AnimaModelLoaderOutput"] | components["schemas"]["BooleanCollectionOutput"] | components["schemas"]["BooleanOutput"] | components["schemas"]["BoundingBoxCollectionOutput"] | components["schemas"]["BoundingBoxOutput"] | components["schemas"]["CLIPOutput"] | components["schemas"]["CLIPSkipInvocationOutput"] | components["schemas"]["CalculateImageTilesOutput"] | components["schemas"]["CogView4ConditioningOutput"] | components["schemas"]["CogView4ModelLoaderOutput"] | components["schemas"]["CollectInvocationOutput"] | components["schemas"]["ColorCollectionOutput"] | components["schemas"]["ColorOutput"] | components["schemas"]["ConditioningCollectionOutput"] | components["schemas"]["ConditioningOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["DenoiseMaskOutput"] | components["schemas"]["FaceMaskOutput"] | components["schemas"]["FaceOffOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["FloatGeneratorOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["Flux2KleinLoRALoaderOutput"] | components["schemas"]["Flux2KleinModelLoaderOutput"] | components["schemas"]["FluxConditioningCollectionOutput"] | components["schemas"]["FluxConditioningOutput"] | components["schemas"]["FluxControlLoRALoaderOutput"] | components["schemas"]["FluxControlNetOutput"] | components["schemas"]["FluxFillOutput"] | components["schemas"]["FluxKontextOutput"] | components["schemas"]["FluxLoRALoaderOutput"] | components["schemas"]["FluxModelLoaderOutput"] | components["schemas"]["FluxReduxOutput"] | components["schemas"]["GradientMaskOutput"] | components["schemas"]["IPAdapterOutput"] | components["schemas"]["IdealSizeOutput"] | components["schemas"]["Ideogram4ConditioningOutput"] | components["schemas"]["Ideogram4ModelLoaderOutput"] | components["schemas"]["IfInvocationOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ImageGeneratorOutput"] | components["schemas"]["ImageOutput"] | components["schemas"]["ImagePanelCoordinateOutput"] | components["schemas"]["IntegerCollectionOutput"] | components["schemas"]["IntegerGeneratorOutput"] | components["schemas"]["IntegerOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["LatentsCollectionOutput"] | components["schemas"]["LatentsMetaOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["LoRALoaderOutput"] | components["schemas"]["LoRASelectorOutput"] | components["schemas"]["MDControlListOutput"] | components["schemas"]["MDIPAdapterListOutput"] | components["schemas"]["MDT2IAdapterListOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["MetadataItemOutput"] | components["schemas"]["MetadataOutput"] | components["schemas"]["MetadataToLorasCollectionOutput"] | components["schemas"]["MetadataToModelOutput"] | components["schemas"]["MetadataToSDXLModelOutput"] | components["schemas"]["ModelIdentifierOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PBRMapsOutput"] | components["schemas"]["PairTileImageOutput"] | components["schemas"]["PromptTemplateOutput"] | components["schemas"]["QwenImageConditioningOutput"] | components["schemas"]["QwenImageLoRALoaderOutput"] | components["schemas"]["QwenImageModelLoaderOutput"] | components["schemas"]["SD3ConditioningOutput"] | components["schemas"]["SDXLLoRALoaderOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["SchedulerOutput"] | components["schemas"]["Sd3ModelLoaderOutput"] | components["schemas"]["SeamlessModeOutput"] | components["schemas"]["String2Output"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["StringGeneratorOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["StringPosNegOutput"] | components["schemas"]["T2IAdapterOutput"] | components["schemas"]["TileToPropertiesOutput"] | components["schemas"]["UNetOutput"] | components["schemas"]["VAEOutput"] | components["schemas"]["ZImageConditioningOutput"] | components["schemas"]["ZImageControlOutput"] | components["schemas"]["ZImageLoRALoaderOutput"] | components["schemas"]["ZImageModelLoaderOutput"]; }; /** * Errors @@ -13380,6 +13381,254 @@ export type components = { */ type: "ideal_size_output"; }; + /** + * Ideogram4ConditioningField + * @description An Ideogram 4 conditioning tensor primitive value + */ + Ideogram4ConditioningField: { + /** + * Conditioning Name + * @description The name of conditioning tensor + */ + conditioning_name: string; + }; + /** + * Ideogram4ConditioningOutput + * @description Base class for nodes that output an Ideogram 4 text conditioning tensor. + */ + Ideogram4ConditioningOutput: { + /** @description Conditioning tensor */ + conditioning: components["schemas"]["Ideogram4ConditioningField"]; + /** + * type + * @default ideogram4_conditioning_output + * @constant + */ + type: "ideogram4_conditioning_output"; + }; + /** + * Denoise - Ideogram 4 + * @description Runs the Ideogram 4 dual-branch flow-matching denoising loop (text-to-image). + */ + Ideogram4DenoiseInvocation: { + /** + * Id + * @description The id of this instance of an invocation. Must be unique among all instances of invocations. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this is an intermediate invocation. + * @default false + */ + is_intermediate?: boolean; + /** + * Use Cache + * @description Whether or not to use the cache + * @default true + */ + use_cache?: boolean; + /** + * Transformer + * @description Transformer + * @default null + */ + transformer?: components["schemas"]["TransformerField"] | null; + /** + * @description Positive conditioning tensor + * @default null + */ + positive_conditioning?: components["schemas"]["Ideogram4ConditioningField"] | null; + /** + * Sampler Preset + * @description Sampler preset (steps + guidance schedule + schedule mean/std). + * @default V4_QUALITY_48 + * @enum {string} + */ + sampler_preset?: "V4_QUALITY_48" | "V4_DEFAULT_20" | "V4_TURBO_12"; + /** + * Width + * @description Width of the generated image. + * @default 1024 + */ + width?: number; + /** + * Height + * @description Height of the generated image. + * @default 1024 + */ + height?: number; + /** + * Seed + * @description Randomness seed for reproducibility. + * @default 0 + */ + seed?: number; + /** + * type + * @default ideogram4_denoise + * @constant + */ + type: "ideogram4_denoise"; + }; + /** + * Latents to Image - Ideogram 4 + * @description Decodes Ideogram 4 packed latents to an image with the FLUX.2-style VAE. + */ + Ideogram4LatentsToImageInvocation: { + /** + * @description The board to save the image to + * @default null + */ + board?: components["schemas"]["BoardField"] | null; + /** + * @description Optional metadata to be saved with the image + * @default null + */ + metadata?: components["schemas"]["MetadataField"] | null; + /** + * Id + * @description The id of this instance of an invocation. Must be unique among all instances of invocations. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this is an intermediate invocation. + * @default false + */ + is_intermediate?: boolean; + /** + * Use Cache + * @description Whether or not to use the cache + * @default true + */ + use_cache?: boolean; + /** + * @description Latents tensor + * @default null + */ + latents?: components["schemas"]["LatentsField"] | null; + /** + * @description VAE + * @default null + */ + vae?: components["schemas"]["VAEField"] | null; + /** + * type + * @default ideogram4_l2i + * @constant + */ + type: "ideogram4_l2i"; + }; + /** + * Main Model - Ideogram 4 + * @description Loads an Ideogram 4 model, outputting its submodels. + * + * Ideogram 4 is distributed as a single bundled diffusers folder, so the transformer + * (both branches), the Qwen3-VL text encoder + tokenizer, and the VAE are all loaded + * from the one selected model. + */ + Ideogram4ModelLoaderInvocation: { + /** + * Id + * @description The id of this instance of an invocation. Must be unique among all instances of invocations. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this is an intermediate invocation. + * @default false + */ + is_intermediate?: boolean; + /** + * Use Cache + * @description Whether or not to use the cache + * @default true + */ + use_cache?: boolean; + /** + * Model + * @description The Ideogram 4 model to load. + */ + model: components["schemas"]["ModelIdentifierField"]; + /** + * type + * @default ideogram4_model_loader + * @constant + */ + type: "ideogram4_model_loader"; + }; + /** + * Ideogram4ModelLoaderOutput + * @description Ideogram 4 model loader output. + */ + Ideogram4ModelLoaderOutput: { + /** + * Transformer + * @description Transformer + */ + transformer: components["schemas"]["TransformerField"]; + /** + * Qwen3-VL Encoder + * @description Qwen3 tokenizer and text encoder + */ + qwen3_encoder: components["schemas"]["Qwen3EncoderField"]; + /** + * VAE + * @description VAE + */ + vae: components["schemas"]["VAEField"]; + /** + * type + * @default ideogram4_model_loader_output + * @constant + */ + type: "ideogram4_model_loader_output"; + }; + /** + * Prompt - Ideogram 4 + * @description Encodes a prompt for Ideogram 4 using the Qwen3-VL encoder. + * + * The prompt is normally a structured JSON caption (see the Ideogram 4 prompting guide); + * plain text also works but yields lower-quality results. + */ + Ideogram4TextEncoderInvocation: { + /** + * Id + * @description The id of this instance of an invocation. Must be unique among all instances of invocations. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this is an intermediate invocation. + * @default false + */ + is_intermediate?: boolean; + /** + * Use Cache + * @description Whether or not to use the cache + * @default true + */ + use_cache?: boolean; + /** + * Prompt + * @description The prompt to encode. A structured JSON caption is recommended. + * @default null + */ + prompt?: string | null; + /** + * Qwen3-VL Encoder + * @description Qwen3 tokenizer and text encoder + * @default null + */ + qwen3_encoder?: components["schemas"]["Qwen3EncoderField"] | null; + /** + * type + * @default ideogram4_text_encoder + * @constant + */ + type: "ideogram4_text_encoder"; + }; /** * If * @description Selects between two optional inputs based on a boolean condition. @@ -15662,7 +15911,7 @@ export type components = { * Invocation * @description The ID of the invocation */ - invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"]; + invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"]; /** * Invocation Source Id * @description The ID of the prepared invocation's source node @@ -15672,7 +15921,7 @@ export type components = { * Result * @description The result of the invocation */ - result: components["schemas"]["AnimaConditioningOutput"] | components["schemas"]["AnimaLoRALoaderOutput"] | components["schemas"]["AnimaModelLoaderOutput"] | components["schemas"]["BooleanCollectionOutput"] | components["schemas"]["BooleanOutput"] | components["schemas"]["BoundingBoxCollectionOutput"] | components["schemas"]["BoundingBoxOutput"] | components["schemas"]["CLIPOutput"] | components["schemas"]["CLIPSkipInvocationOutput"] | components["schemas"]["CalculateImageTilesOutput"] | components["schemas"]["CogView4ConditioningOutput"] | components["schemas"]["CogView4ModelLoaderOutput"] | components["schemas"]["CollectInvocationOutput"] | components["schemas"]["ColorCollectionOutput"] | components["schemas"]["ColorOutput"] | components["schemas"]["ConditioningCollectionOutput"] | components["schemas"]["ConditioningOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["DenoiseMaskOutput"] | components["schemas"]["FaceMaskOutput"] | components["schemas"]["FaceOffOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["FloatGeneratorOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["Flux2KleinLoRALoaderOutput"] | components["schemas"]["Flux2KleinModelLoaderOutput"] | components["schemas"]["FluxConditioningCollectionOutput"] | components["schemas"]["FluxConditioningOutput"] | components["schemas"]["FluxControlLoRALoaderOutput"] | components["schemas"]["FluxControlNetOutput"] | components["schemas"]["FluxFillOutput"] | components["schemas"]["FluxKontextOutput"] | components["schemas"]["FluxLoRALoaderOutput"] | components["schemas"]["FluxModelLoaderOutput"] | components["schemas"]["FluxReduxOutput"] | components["schemas"]["GradientMaskOutput"] | components["schemas"]["IPAdapterOutput"] | components["schemas"]["IdealSizeOutput"] | components["schemas"]["IfInvocationOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ImageGeneratorOutput"] | components["schemas"]["ImageOutput"] | components["schemas"]["ImagePanelCoordinateOutput"] | components["schemas"]["IntegerCollectionOutput"] | components["schemas"]["IntegerGeneratorOutput"] | components["schemas"]["IntegerOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["LatentsCollectionOutput"] | components["schemas"]["LatentsMetaOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["LoRALoaderOutput"] | components["schemas"]["LoRASelectorOutput"] | components["schemas"]["MDControlListOutput"] | components["schemas"]["MDIPAdapterListOutput"] | components["schemas"]["MDT2IAdapterListOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["MetadataItemOutput"] | components["schemas"]["MetadataOutput"] | components["schemas"]["MetadataToLorasCollectionOutput"] | components["schemas"]["MetadataToModelOutput"] | components["schemas"]["MetadataToSDXLModelOutput"] | components["schemas"]["ModelIdentifierOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PBRMapsOutput"] | components["schemas"]["PairTileImageOutput"] | components["schemas"]["PromptTemplateOutput"] | components["schemas"]["QwenImageConditioningOutput"] | components["schemas"]["QwenImageLoRALoaderOutput"] | components["schemas"]["QwenImageModelLoaderOutput"] | components["schemas"]["SD3ConditioningOutput"] | components["schemas"]["SDXLLoRALoaderOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["SchedulerOutput"] | components["schemas"]["Sd3ModelLoaderOutput"] | components["schemas"]["SeamlessModeOutput"] | components["schemas"]["String2Output"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["StringGeneratorOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["StringPosNegOutput"] | components["schemas"]["T2IAdapterOutput"] | components["schemas"]["TileToPropertiesOutput"] | components["schemas"]["UNetOutput"] | components["schemas"]["VAEOutput"] | components["schemas"]["ZImageConditioningOutput"] | components["schemas"]["ZImageControlOutput"] | components["schemas"]["ZImageLoRALoaderOutput"] | components["schemas"]["ZImageModelLoaderOutput"]; + result: components["schemas"]["AnimaConditioningOutput"] | components["schemas"]["AnimaLoRALoaderOutput"] | components["schemas"]["AnimaModelLoaderOutput"] | components["schemas"]["BooleanCollectionOutput"] | components["schemas"]["BooleanOutput"] | components["schemas"]["BoundingBoxCollectionOutput"] | components["schemas"]["BoundingBoxOutput"] | components["schemas"]["CLIPOutput"] | components["schemas"]["CLIPSkipInvocationOutput"] | components["schemas"]["CalculateImageTilesOutput"] | components["schemas"]["CogView4ConditioningOutput"] | components["schemas"]["CogView4ModelLoaderOutput"] | components["schemas"]["CollectInvocationOutput"] | components["schemas"]["ColorCollectionOutput"] | components["schemas"]["ColorOutput"] | components["schemas"]["ConditioningCollectionOutput"] | components["schemas"]["ConditioningOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["DenoiseMaskOutput"] | components["schemas"]["FaceMaskOutput"] | components["schemas"]["FaceOffOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["FloatGeneratorOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["Flux2KleinLoRALoaderOutput"] | components["schemas"]["Flux2KleinModelLoaderOutput"] | components["schemas"]["FluxConditioningCollectionOutput"] | components["schemas"]["FluxConditioningOutput"] | components["schemas"]["FluxControlLoRALoaderOutput"] | components["schemas"]["FluxControlNetOutput"] | components["schemas"]["FluxFillOutput"] | components["schemas"]["FluxKontextOutput"] | components["schemas"]["FluxLoRALoaderOutput"] | components["schemas"]["FluxModelLoaderOutput"] | components["schemas"]["FluxReduxOutput"] | components["schemas"]["GradientMaskOutput"] | components["schemas"]["IPAdapterOutput"] | components["schemas"]["IdealSizeOutput"] | components["schemas"]["Ideogram4ConditioningOutput"] | components["schemas"]["Ideogram4ModelLoaderOutput"] | components["schemas"]["IfInvocationOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ImageGeneratorOutput"] | components["schemas"]["ImageOutput"] | components["schemas"]["ImagePanelCoordinateOutput"] | components["schemas"]["IntegerCollectionOutput"] | components["schemas"]["IntegerGeneratorOutput"] | components["schemas"]["IntegerOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["LatentsCollectionOutput"] | components["schemas"]["LatentsMetaOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["LoRALoaderOutput"] | components["schemas"]["LoRASelectorOutput"] | components["schemas"]["MDControlListOutput"] | components["schemas"]["MDIPAdapterListOutput"] | components["schemas"]["MDT2IAdapterListOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["MetadataItemOutput"] | components["schemas"]["MetadataOutput"] | components["schemas"]["MetadataToLorasCollectionOutput"] | components["schemas"]["MetadataToModelOutput"] | components["schemas"]["MetadataToSDXLModelOutput"] | components["schemas"]["ModelIdentifierOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PBRMapsOutput"] | components["schemas"]["PairTileImageOutput"] | components["schemas"]["PromptTemplateOutput"] | components["schemas"]["QwenImageConditioningOutput"] | components["schemas"]["QwenImageLoRALoaderOutput"] | components["schemas"]["QwenImageModelLoaderOutput"] | components["schemas"]["SD3ConditioningOutput"] | components["schemas"]["SDXLLoRALoaderOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["SchedulerOutput"] | components["schemas"]["Sd3ModelLoaderOutput"] | components["schemas"]["SeamlessModeOutput"] | components["schemas"]["String2Output"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["StringGeneratorOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["StringPosNegOutput"] | components["schemas"]["T2IAdapterOutput"] | components["schemas"]["TileToPropertiesOutput"] | components["schemas"]["UNetOutput"] | components["schemas"]["VAEOutput"] | components["schemas"]["ZImageConditioningOutput"] | components["schemas"]["ZImageControlOutput"] | components["schemas"]["ZImageLoRALoaderOutput"] | components["schemas"]["ZImageModelLoaderOutput"]; }; /** * InvocationErrorEvent @@ -15726,7 +15975,7 @@ export type components = { * Invocation * @description The ID of the invocation */ - invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"]; + invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"]; /** * Invocation Source Id * @description The ID of the prepared invocation's source node @@ -15842,6 +16091,10 @@ export type components = { heuristic_resize: components["schemas"]["ImageOutput"]; i2l: components["schemas"]["LatentsOutput"]; ideal_size: components["schemas"]["IdealSizeOutput"]; + ideogram4_denoise: components["schemas"]["LatentsOutput"]; + ideogram4_l2i: components["schemas"]["ImageOutput"]; + ideogram4_model_loader: components["schemas"]["Ideogram4ModelLoaderOutput"]; + ideogram4_text_encoder: components["schemas"]["Ideogram4ConditioningOutput"]; if: components["schemas"]["IfInvocationOutput"]; image: components["schemas"]["ImageOutput"]; image_batch: components["schemas"]["ImageOutput"]; @@ -16057,7 +16310,7 @@ export type components = { * Invocation * @description The ID of the invocation */ - invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"]; + invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"]; /** * Invocation Source Id * @description The ID of the prepared invocation's source node @@ -16132,7 +16385,7 @@ export type components = { * Invocation * @description The ID of the invocation */ - invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"]; + invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"]; /** * Invocation Source Id * @description The ID of the prepared invocation's source node @@ -20598,6 +20851,96 @@ export type components = { base: "flux2"; variant: components["schemas"]["Flux2VariantType"]; }; + /** + * Main_Diffusers_Ideogram4_Config + * @description Model config for Ideogram 4 diffusers models (nf4 / fp8 quantized). + * + * The on-disk layout is a diffusers pipeline folder bundling two transformers + * (transformer/ + unconditional_transformer/), a Qwen3-VL text_encoder/ + tokenizer/, + * and a FLUX.2-style vae/. Quantization (nf4 vs fp8) lives inside the component folders + * and is detected by the loader, not here. + */ + Main_Diffusers_Ideogram4_Config: { + /** + * Key + * @description A unique key for this model. + */ + key: string; + /** + * Hash + * @description The hash of the model file(s). + */ + hash: string; + /** + * Path + * @description Path to the model on the filesystem. Relative paths are relative to the Invoke root directory. + */ + path: string; + /** + * File Size + * @description The size of the model in bytes. + */ + file_size: number; + /** + * Name + * @description Name of the model. + */ + name: string; + /** + * Description + * @description Model description + */ + description: string | null; + /** + * Source + * @description The original source of the model (path, URL or repo_id). + */ + source: string; + /** @description The type of source */ + source_type: components["schemas"]["ModelSourceType"]; + /** + * Source Api Response + * @description The original API response from the source, as stringified JSON. + */ + source_api_response: string | null; + /** + * Source Url + * @description Optional URL for the model (e.g. download page or model page). + */ + source_url: string | null; + /** + * Cover Image + * @description Url for image to preview model + */ + cover_image: string | null; + /** + * Type + * @default main + * @constant + */ + type: "main"; + /** + * Trigger Phrases + * @description Set of trigger phrases for this model + */ + trigger_phrases: string[] | null; + /** @description Default settings for this model */ + default_settings: components["schemas"]["MainModelDefaultSettings"] | null; + /** + * Format + * @default diffusers + * @constant + */ + format: "diffusers"; + /** @default */ + repo_variant: components["schemas"]["ModelRepoVariant"]; + /** + * Base + * @default ideogram-4 + * @constant + */ + base: "ideogram-4"; + }; /** * Main_Diffusers_QwenImage_Config * @description Model config for Qwen Image diffusers models (both txt2img and edit). @@ -23379,7 +23722,7 @@ export type components = { * Config * @description The installed model's config */ - config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; + config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; }; /** * ModelInstallDownloadProgressEvent @@ -23545,7 +23888,7 @@ export type components = { * Config Out * @description After successful installation, this will hold the configuration object. */ - config_out?: (components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]) | null; + config_out?: (components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]) | null; /** * Inplace * @description Leave model in its current location; otherwise install under models directory @@ -23631,7 +23974,7 @@ export type components = { * Config * @description The model's config */ - config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; + config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; /** * @description The submodel type, if any * @default null @@ -23652,7 +23995,7 @@ export type components = { * Config * @description The model's config */ - config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; + config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; /** * @description The submodel type, if any * @default null @@ -23803,6 +24146,18 @@ export type components = { /** * Model Keys * @description List of model keys to fetch related models for + * @example [ + * "aa3b247f-90c9-4416-bfcd-aeaa57a5339e", + * "ac32b914-10ab-496e-a24a-3068724b9c35" + * ] + * @example [ + * "b1c2d3e4-f5a6-7890-abcd-ef1234567890", + * "12345678-90ab-cdef-1234-567890abcdef", + * "fedcba98-7654-3210-fedc-ba9876543210" + * ] + * @example [ + * "3bb7c0eb-b6c8-469c-ad8c-4d69c06075e4" + * ] */ model_keys: string[]; }; @@ -23811,11 +24166,23 @@ export type components = { /** * Model Key 1 * @description The key of the first model in the relationship + * @example aa3b247f-90c9-4416-bfcd-aeaa57a5339e + * @example ac32b914-10ab-496e-a24a-3068724b9c35 + * @example d944abfd-c7c3-42e2-a4ff-da640b29b8b4 + * @example b1c2d3e4-f5a6-7890-abcd-ef1234567890 + * @example 12345678-90ab-cdef-1234-567890abcdef + * @example fedcba98-7654-3210-fedc-ba9876543210 */ model_key_1: string; /** * Model Key 2 * @description The key of the second model in the relationship + * @example 3bb7c0eb-b6c8-469c-ad8c-4d69c06075e4 + * @example f0c3da4e-d9ff-42b5-a45c-23be75c887c9 + * @example 38170dd8-f1e5-431e-866c-2c81f1277fcc + * @example c57fea2d-7646-424c-b9ad-c0ba60fc68be + * @example 10f7807b-ab54-46a9-ab03-600e88c630a1 + * @example f6c1d267-cf87-4ee0-bee0-37e791eacab7 */ model_key_2: string; }; @@ -23849,7 +24216,7 @@ export type components = { */ ModelsList: { /** Models */ - models: (components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"])[]; + models: (components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"])[]; }; /** * Multiply Integers @@ -33479,7 +33846,7 @@ export interface operations { [name: string]: unknown; }; content: { - "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; + "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; }; }; /** @description Validation Error */ @@ -33511,7 +33878,7 @@ export interface operations { [name: string]: unknown; }; content: { - "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; + "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; }; }; /** @description Validation Error */ @@ -33543,7 +33910,8 @@ export interface operations { [name: string]: unknown; }; content: { - /** @example { + /** + * @example { * "path": "string", * "name": "string", * "base": "sd-1", @@ -33560,8 +33928,9 @@ export interface operations { * "prediction_type": "epsilon", * "repo_variant": "fp16", * "upcast_attention": false - * } */ - "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; + * } + */ + "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; }; }; /** @description Bad request */ @@ -33648,7 +34017,8 @@ export interface operations { [name: string]: unknown; }; content: { - /** @example { + /** + * @example { * "path": "string", * "name": "string", * "base": "sd-1", @@ -33665,8 +34035,9 @@ export interface operations { * "prediction_type": "epsilon", * "repo_variant": "fp16", * "upcast_attention": false - * } */ - "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; + * } + */ + "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; }; }; /** @description Bad request */ @@ -33719,7 +34090,8 @@ export interface operations { [name: string]: unknown; }; content: { - /** @example { + /** + * @example { * "path": "string", * "name": "string", * "base": "sd-1", @@ -33736,8 +34108,9 @@ export interface operations { * "prediction_type": "epsilon", * "repo_variant": "fp16", * "upcast_attention": false - * } */ - "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; + * } + */ + "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; }; }; /** @description Bad request */ @@ -34452,7 +34825,8 @@ export interface operations { [name: string]: unknown; }; content: { - /** @example { + /** + * @example { * "path": "string", * "name": "string", * "base": "sd-1", @@ -34469,8 +34843,9 @@ export interface operations { * "prediction_type": "epsilon", * "repo_variant": "fp16", * "upcast_attention": false - * } */ - "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; + * } + */ + "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; }; }; /** @description Bad request */ @@ -36068,11 +36443,13 @@ export interface operations { [name: string]: unknown; }; content: { - /** @example [ + /** + * @example [ * "15e9eb28-8cfe-47c9-b610-37907a79fc3c", * "71272e82-0e5f-46d5-bca9-9a61f4bd8a82", * "a5d7cd49-1b98-4534-a475-aeee4ccf5fa2" - * ] */ + * ] + */ "application/json": string[]; }; }; @@ -36211,7 +36588,8 @@ export interface operations { [name: string]: unknown; }; content: { - /** @example [ + /** + * @example [ * "ca562b14-995e-4a42-90c1-9528f1a5921d", * "cc0c2b8a-c62e-41d6-878e-cc74dde5ca8f", * "18ca7649-6a9e-47d5-bc17-41ab1e8cec81", @@ -36219,7 +36597,8 @@ export interface operations { * "c382eaa3-0e28-4ab0-9446-408667699aeb", * "71272e82-0e5f-46d5-bca9-9a61f4bd8a82", * "a5d7cd49-1b98-4534-a475-aeee4ccf5fa2" - * ] */ + * ] + */ "application/json": string[]; }; }; From 38223ce0e8ee49c89f781bb5c6011b780c027c1e Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Thu, 25 Jun 2026 06:23:57 +0200 Subject: [PATCH 14/33] feat(ideogram4): advanced sampler overrides + color palette MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Advanced accordion now shows only Ideogram 4-relevant controls. Adds optional overrides of the sampler preset — steps, guidance scale (overrides the main gw, preserves the preset's polish tail), and schedule shift (mu) — plus a color palette editor that injects style_description.color_palette into the auto-built JSON caption (uppercase #RRGGBB, max 16, ignored for raw-JSON prompts). All are nullable (null = use preset), recallable from metadata, and the irrelevant controls (VAE, CLIP skip, CFG rescale, seamless, color compensation) are hidden for Ideogram 4. Backend denoise gains steps/guidance_scale/mu fields; schema.ts regenerated. --- invokeai/app/invocations/ideogram4_denoise.py | 56 ++++++++- invokeai/frontend/web/public/locales/en.json | 5 + .../controlLayers/store/paramsSlice.ts | 20 +++ .../src/features/controlLayers/store/types.ts | 12 +- .../web/src/features/metadata/parsing.tsx | 119 ++++++++++++++++++ .../graph/generation/buildIdeogram4Graph.ts | 26 +++- .../generation/buildIdeogram4Prompt.test.ts | 33 +++++ .../graph/generation/buildIdeogram4Prompt.ts | 48 ++++--- .../Core/ParamIdeogram4ColorPalette.tsx | 103 +++++++++++++++ .../Core/ParamIdeogram4GuidanceScale.tsx | 68 ++++++++++ .../components/Core/ParamIdeogram4Mu.tsx | 73 +++++++++++ .../Core/ParamIdeogram4SamplerPreset.tsx | 8 ++ .../components/Core/ParamIdeogram4Steps.tsx | 71 +++++++++++ .../AdvancedSettingsAccordion.tsx | 20 ++- .../frontend/web/src/services/api/schema.ts | 79 +++++------- 15 files changed, 664 insertions(+), 77 deletions(-) create mode 100644 invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4ColorPalette.tsx create mode 100644 invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4GuidanceScale.tsx create mode 100644 invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Mu.tsx create mode 100644 invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Steps.tsx diff --git a/invokeai/app/invocations/ideogram4_denoise.py b/invokeai/app/invocations/ideogram4_denoise.py index 1ebe4686a34..aea9ec1a67c 100644 --- a/invokeai/app/invocations/ideogram4_denoise.py +++ b/invokeai/app/invocations/ideogram4_denoise.py @@ -1,4 +1,4 @@ -from typing import Literal +from typing import Literal, Optional import torch @@ -23,6 +23,26 @@ IDEOGRAM4_SAMPLER_PRESETS = Literal["V4_QUALITY_48", "V4_DEFAULT_20", "V4_TURBO_12"] +def _effective_guidance_schedule( + base_schedule: tuple[float, ...], preset_num_steps: int, num_steps: int, guidance_scale: Optional[float] +) -> tuple[float, ...]: + """Build the per-step guidance schedule for the (possibly overridden) step count. + + The preset schedule is ``(polish_gw,)*N_polish + (main_gw,)*N_main`` in loop-index order + (index 0 = the final/polish step). A ``guidance_scale`` override replaces the main weight while + the preset's polish tail is preserved; a changed step count rescales the polish tail + proportionally (always keeping at least one polish and one main step). + """ + polish_gw = base_schedule[0] + main_gw = float(guidance_scale) if guidance_scale is not None else float(base_schedule[-1]) + if num_steps == preset_num_steps and guidance_scale is None: + return base_schedule + n_polish_base = sum(1 for gw in base_schedule if gw == base_schedule[0]) + polish_count = min(num_steps, max(1, round(n_polish_base * num_steps / preset_num_steps))) + main_count = num_steps - polish_count + return (polish_gw,) * polish_count + (main_gw,) * main_count + + @invocation( "ideogram4_denoise", title="Denoise - Ideogram 4", @@ -48,12 +68,40 @@ class Ideogram4DenoiseInvocation(BaseInvocation): width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.") height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.") seed: int = InputField(default=0, description="Randomness seed for reproducibility.") + # Optional advanced overrides of the sampler preset. None = use the preset's value. + steps: Optional[int] = InputField( + default=None, + ge=1, + le=100, + description="Override the preset's step count. Leave empty to use the preset.", + ) + guidance_scale: Optional[float] = InputField( + default=None, + ge=1.0, + le=20.0, + description="Override the main guidance weight (the preset's polish tail is preserved). " + "Empty = use the preset.", + ) + mu: Optional[float] = InputField( + default=None, + ge=-4.0, + le=4.0, + description="Override the logit-normal schedule mean (resolution-adjusted internally). " + "Empty = use the preset.", + ) @torch.no_grad() def invoke(self, context: InvocationContext) -> LatentsOutput: device = TorchDevice.choose_torch_device() preset = PRESETS[self.sampler_preset] + # Apply optional advanced overrides on top of the preset. + num_steps = self.steps if self.steps is not None else preset.num_steps + mu = self.mu if self.mu is not None else preset.mu + guidance_schedule = _effective_guidance_schedule( + preset.guidance_schedule, preset.num_steps, num_steps, self.guidance_scale + ) + # Load conditioning (the stacked Qwen3-VL features). cond_data = context.conditioning.load(self.positive_conditioning.conditioning_name) assert len(cond_data.conditionings) == 1 @@ -73,10 +121,10 @@ def step_callback(step: int, total: int, _latents: torch.Tensor) -> None: llm_features=llm_features, height=self.height, width=self.width, - num_steps=preset.num_steps, - mu=preset.mu, + num_steps=num_steps, + mu=mu, std=preset.std, - guidance_schedule=preset.guidance_schedule, + guidance_schedule=guidance_schedule, seed=self.seed, device=device, step_callback=step_callback, diff --git a/invokeai/frontend/web/public/locales/en.json b/invokeai/frontend/web/public/locales/en.json index b644311dd47..44b1ece4102 100644 --- a/invokeai/frontend/web/public/locales/en.json +++ b/invokeai/frontend/web/public/locales/en.json @@ -1044,6 +1044,11 @@ "recallParameters": "Recall Parameters", "recallParameter": "Recall {{label}}", "ideogram4SamplerPreset": "Sampler Preset", + "ideogram4ScheduleShift": "Schedule Shift", + "ideogram4GuidanceScale": "Guidance Scale", + "ideogram4ColorPalette": "Color Palette", + "ideogram4AddColor": "Add color", + "ideogram4RemoveColor": "Remove color", "scheduler": "Scheduler", "seamlessXAxis": "Seamless X Axis", "seamlessYAxis": "Seamless Y Axis", diff --git a/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts b/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts index 9925d5dc2fc..09b3e3b2e46 100644 --- a/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts +++ b/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts @@ -102,6 +102,18 @@ const slice = createSlice({ setIdeogram4SamplerPreset: (state, action: PayloadAction) => { state.ideogram4SamplerPreset = action.payload; }, + setIdeogram4Steps: (state, action: PayloadAction) => { + state.ideogram4Steps = action.payload; + }, + setIdeogram4GuidanceScale: (state, action: PayloadAction) => { + state.ideogram4GuidanceScale = action.payload; + }, + setIdeogram4Mu: (state, action: PayloadAction) => { + state.ideogram4Mu = action.payload; + }, + setIdeogram4ColorPalette: (state, action: PayloadAction) => { + state.ideogram4ColorPalette = action.payload; + }, setZImageSeedVarianceEnabled: (state, action: PayloadAction) => { state.zImageSeedVarianceEnabled = action.payload; }, @@ -650,6 +662,10 @@ export const { setZImageScheduler, setZImageShift, setIdeogram4SamplerPreset, + setIdeogram4Steps, + setIdeogram4GuidanceScale, + setIdeogram4Mu, + setIdeogram4ColorPalette, setZImageSeedVarianceEnabled, setZImageSeedVarianceStrength, setZImageSeedVarianceRandomizePercent, @@ -917,6 +933,10 @@ export const selectFluxDypeExponent = createParamsSelector((params) => params.fl export const selectZImageScheduler = createParamsSelector((params) => params.zImageScheduler); export const selectZImageShift = createParamsSelector((params) => params.zImageShift); export const selectIdeogram4SamplerPreset = createParamsSelector((params) => params.ideogram4SamplerPreset); +export const selectIdeogram4Steps = createParamsSelector((params) => params.ideogram4Steps); +export const selectIdeogram4GuidanceScale = createParamsSelector((params) => params.ideogram4GuidanceScale); +export const selectIdeogram4Mu = createParamsSelector((params) => params.ideogram4Mu); +export const selectIdeogram4ColorPalette = createParamsSelector((params) => params.ideogram4ColorPalette); export const selectZImageSeedVarianceEnabled = createParamsSelector((params) => params.zImageSeedVarianceEnabled); export const selectZImageSeedVarianceStrength = createParamsSelector((params) => params.zImageSeedVarianceStrength); export const selectZImageSeedVarianceRandomizePercent = createParamsSelector( diff --git a/invokeai/frontend/web/src/features/controlLayers/store/types.ts b/invokeai/frontend/web/src/features/controlLayers/store/types.ts index 6147ad381d2..764c0717eec 100644 --- a/invokeai/frontend/web/src/features/controlLayers/store/types.ts +++ b/invokeai/frontend/web/src/features/controlLayers/store/types.ts @@ -805,8 +805,14 @@ export const zParamsState = z.object({ fluxDypeExponent: zParameterFluxDypeExponent, zImageScheduler: zParameterZImageScheduler, zImageShift: z.number().min(0).max(3).nullable(), - // Default makes this resilient to rehydration of persisted state saved before this field existed. + // Defaults make these resilient to rehydration of persisted state saved before the fields existed. ideogram4SamplerPreset: zParameterIdeogram4SamplerPreset.default('V4_QUALITY_48'), + // Optional advanced overrides of the Ideogram 4 sampler preset (null = use the preset's value). + ideogram4Steps: z.number().int().min(1).max(100).nullable().default(null), + ideogram4GuidanceScale: z.number().min(1).max(20).nullable().default(null), + ideogram4Mu: z.number().min(-4).max(4).nullable().default(null), + // Hex colors (#RRGGBB) injected into the JSON caption's style_description.color_palette. + ideogram4ColorPalette: z.array(z.string()).default([]), upscaleScheduler: zParameterScheduler, upscaleCfgScale: zParameterCFGScale, seed: zParameterSeed, @@ -896,6 +902,10 @@ export const getInitialParamsState = (): ParamsState => ({ zImageScheduler: 'euler', zImageShift: null, ideogram4SamplerPreset: 'V4_QUALITY_48', + ideogram4Steps: null, + ideogram4GuidanceScale: null, + ideogram4Mu: null, + ideogram4ColorPalette: [], upscaleScheduler: 'kdpm_2', upscaleCfgScale: 2, seed: 0, diff --git a/invokeai/frontend/web/src/features/metadata/parsing.tsx b/invokeai/frontend/web/src/features/metadata/parsing.tsx index e8da3946b6f..dbb82a0e282 100644 --- a/invokeai/frontend/web/src/features/metadata/parsing.tsx +++ b/invokeai/frontend/web/src/features/metadata/parsing.tsx @@ -39,7 +39,11 @@ import { setFluxDypeScale, setFluxScheduler, setGuidance, + setIdeogram4ColorPalette, + setIdeogram4GuidanceScale, + setIdeogram4Mu, setIdeogram4SamplerPreset, + setIdeogram4Steps, setImg2imgStrength, setRefinerCFGScale, setRefinerNegativeAestheticScore, @@ -905,6 +909,117 @@ const Ideogram4SamplerPreset: SingleMetadataHandler = { + [SingleMetadataKey]: true, + type: 'Ideogram4Steps', + parse: (metadata, _store) => { + const raw = getProperty(metadata, 'ideogram4_steps'); + if (raw === undefined) { + return Promise.reject(); + } + if (raw === null || raw === 'auto') { + return Promise.resolve(null); + } + return Promise.resolve(z.number().int().min(1).max(100).parse(raw)); + }, + recall: (value, store) => { + if (selectBase(store.getState()) !== 'ideogram-4') { + return; + } + store.dispatch(setIdeogram4Steps(value)); + }, + i18nKey: 'parameters.steps', + LabelComponent: MetadataLabel, + ValueComponent: ({ value }: SingleMetadataValueProps) => ( + + ), +}; +//#endregion Ideogram4Steps + +//#region Ideogram4GuidanceScale +const Ideogram4GuidanceScale: SingleMetadataHandler = { + [SingleMetadataKey]: true, + type: 'Ideogram4GuidanceScale', + parse: (metadata, _store) => { + const raw = getProperty(metadata, 'ideogram4_guidance_scale'); + if (raw === undefined) { + return Promise.reject(); + } + if (raw === null || raw === 'auto') { + return Promise.resolve(null); + } + return Promise.resolve(z.number().min(1).max(20).parse(raw)); + }, + recall: (value, store) => { + if (selectBase(store.getState()) !== 'ideogram-4') { + return; + } + store.dispatch(setIdeogram4GuidanceScale(value)); + }, + i18nKey: 'parameters.ideogram4GuidanceScale', + LabelComponent: MetadataLabel, + ValueComponent: ({ value }: SingleMetadataValueProps) => ( + + ), +}; +//#endregion Ideogram4GuidanceScale + +//#region Ideogram4Mu +const Ideogram4Mu: SingleMetadataHandler = { + [SingleMetadataKey]: true, + type: 'Ideogram4Mu', + parse: (metadata, _store) => { + const raw = getProperty(metadata, 'ideogram4_mu'); + if (raw === undefined) { + return Promise.reject(); + } + if (raw === null || raw === 'auto') { + return Promise.resolve(null); + } + return Promise.resolve(z.number().min(-4).max(4).parse(raw)); + }, + recall: (value, store) => { + if (selectBase(store.getState()) !== 'ideogram-4') { + return; + } + store.dispatch(setIdeogram4Mu(value)); + }, + i18nKey: 'parameters.ideogram4ScheduleShift', + LabelComponent: MetadataLabel, + ValueComponent: ({ value }: SingleMetadataValueProps) => ( + + ), +}; +//#endregion Ideogram4Mu + +//#region Ideogram4ColorPalette +const Ideogram4ColorPalette: SingleMetadataHandler = { + [SingleMetadataKey]: true, + type: 'Ideogram4ColorPalette', + parse: (metadata, _store) => { + const raw = getProperty(metadata, 'ideogram4_color_palette'); + if (raw === undefined) { + return Promise.reject(); + } + return Promise.resolve(z.array(z.string()).parse(raw)); + }, + recall: (value, store) => { + if (selectBase(store.getState()) !== 'ideogram-4') { + return; + } + store.dispatch(setIdeogram4ColorPalette(value)); + }, + i18nKey: 'parameters.ideogram4ColorPalette', + LabelComponent: MetadataLabel, + ValueComponent: ({ value }: SingleMetadataValueProps) => ( + + ), +}; +//#endregion Ideogram4ColorPalette + //#region RefinerModel const RefinerModel: SingleMetadataHandler = { [SingleMetadataKey]: true, @@ -1669,6 +1784,10 @@ export const ImageMetadataHandlers = { QwenImageShift, ZImageShift, Ideogram4SamplerPreset, + Ideogram4Steps, + Ideogram4GuidanceScale, + Ideogram4Mu, + Ideogram4ColorPalette, LoRAs, CanvasLayers, RefImages, diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts index 7f701c5a878..51af14c84fd 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts @@ -1,7 +1,14 @@ import { objectEquals } from '@observ33r/object-equals'; import { logger } from 'app/logging/logger'; import { getPrefixedId } from 'features/controlLayers/konva/util'; -import { selectIdeogram4SamplerPreset, selectMainModelConfig } from 'features/controlLayers/store/paramsSlice'; +import { + selectIdeogram4ColorPalette, + selectIdeogram4GuidanceScale, + selectIdeogram4Mu, + selectIdeogram4SamplerPreset, + selectIdeogram4Steps, + selectMainModelConfig, +} from 'features/controlLayers/store/paramsSlice'; import { selectCanvasMetadata } from 'features/controlLayers/store/selectors'; import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers'; import { addNSFWChecker } from 'features/nodes/util/graph/generation/addNSFWChecker'; @@ -38,9 +45,15 @@ export const buildIdeogram4Graph = async (arg: GraphBuilderArg): Promise { expect(result.prompt).toContain('café ☕'); expect(result.prompt).not.toContain('\\u'); }); + + it('injects a color palette as style_description.color_palette with correct key order', () => { + const result = buildIdeogram4Caption('a sunset', [region('a boat', [0, 0, 500, 500])], ['#FF6B35', '#004E89']); + expect(result.isStructured).toBe(true); + expect(result.prompt).toBe( + '{"high_level_description":"a sunset",' + + '"style_description":{"color_palette":["#FF6B35","#004E89"]},' + + '"compositional_deconstruction":{"background":"","elements":[{"type":"obj","bbox":[0,0,500,500],"desc":"a boat"}]}}' + ); + }); + + it('builds a structured caption from a palette alone (no regions)', () => { + const result = buildIdeogram4Caption('a sunset', [], ['#FF6B35']); + expect(result.isStructured).toBe(true); + const parsed = JSON.parse(result.prompt); + expect(parsed.style_description.color_palette).toEqual(['#FF6B35']); + expect(parsed.compositional_deconstruction.elements).toEqual([]); + }); + + it('uppercases palette colors and drops invalid hex (shorthand, names)', () => { + const result = buildIdeogram4Caption('x', [], ['#ff6b35', 'red', '#FFF', '#00cc88']); + const parsed = JSON.parse(result.prompt); + expect(parsed.style_description.color_palette).toEqual(['#FF6B35', '#00CC88']); + }); + + it('ignores the palette for raw-JSON passthrough', () => { + const raw = '{"a":1}'; + expect(buildIdeogram4Caption(raw, [], ['#FF6B35'])).toEqual({ prompt: raw, isStructured: true }); + }); + + it('stays plain text when there are no regions and no palette', () => { + expect(buildIdeogram4Caption('just text', [])).toEqual({ prompt: 'just text', isStructured: false }); + }); }); diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts index 4e9b1efc224..9a911f976d9 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts @@ -1,6 +1,6 @@ import type { RootState } from 'app/store/store'; import type { CanvasManager } from 'features/controlLayers/konva/CanvasManager'; -import { selectPositivePrompt } from 'features/controlLayers/store/paramsSlice'; +import { selectIdeogram4ColorPalette, selectPositivePrompt } from 'features/controlLayers/store/paramsSlice'; import { selectCanvasSlice } from 'features/controlLayers/store/selectors'; import type { Rect } from 'features/controlLayers/store/types'; @@ -62,16 +62,21 @@ export type Ideogram4PromptResult = { }; /** - * Assembles an Ideogram 4 prompt from a global prompt and a set of regions. + * Assembles an Ideogram 4 prompt from a global prompt, a set of regions, and an optional color palette. * * - Raw-JSON passthrough: if the global prompt is already a JSON object (trimmed, starts with `{`), it - * is used verbatim and treated as structured. - * - With regions: a structured JSON caption is built — the global prompt becomes - * `high_level_description`, and each region becomes an `obj` element with its bbox + desc. - * - Without regions: the plain global prompt is returned (the model accepts plain text). This keeps - * dynamic prompts and prompt batching working, since the caller can let the batch inject it directly. + * is used verbatim and treated as structured (the palette is ignored — the user controls the JSON). + * - With regions and/or a color palette: a structured JSON caption is built — the global prompt becomes + * `high_level_description`, each region becomes an `obj` element, and the palette (if any) becomes + * `style_description.color_palette`. + * - Otherwise: the plain global prompt is returned (the model accepts plain text). This keeps dynamic + * prompts and prompt batching working, since the caller can let the batch inject it directly. */ -export const buildIdeogram4Caption = (globalPrompt: string, regions: Ideogram4RegionInput[]): Ideogram4PromptResult => { +export const buildIdeogram4Caption = ( + globalPrompt: string, + regions: Ideogram4RegionInput[], + colorPalette: string[] = [] +): Ideogram4PromptResult => { const trimmed = globalPrompt.trim(); // The user pasted a structured caption (or any JSON object) — use it verbatim. @@ -85,18 +90,22 @@ export const buildIdeogram4Caption = (globalPrompt: string, regions: Ideogram4Re region.bbox ? { type: 'obj', bbox: region.bbox, desc: region.prompt } : { type: 'obj', desc: region.prompt } ); - // Nothing regional to encode — fall back to the plain prompt (documented to work). - if (elements.length === 0) { + // Normalize the palette to uppercase #RRGGBB (the schema's required hex form); drop invalid entries. + const palette = colorPalette.map((c) => c.toUpperCase()).filter((c) => /^#[0-9A-F]{6}$/.test(c)); + + // Nothing structured to encode — fall back to the plain prompt (documented to work). + if (elements.length === 0 && palette.length === 0) { return { prompt: trimmed, isStructured: false }; } - const caption = { - high_level_description: trimmed, - compositional_deconstruction: { - background: '', - elements, - }, - }; + // Strict key order: high_level_description, (style_description), compositional_deconstruction. + // style_description here carries only color_palette; the other style fields are left to raw-JSON use. + const compositional_deconstruction = { background: '', elements }; + const caption = + palette.length > 0 + ? { high_level_description: trimmed, style_description: { color_palette: palette }, compositional_deconstruction } + : { high_level_description: trimmed, compositional_deconstruction }; + return { prompt: JSON.stringify(caption), isStructured: true }; }; @@ -107,10 +116,11 @@ export const buildIdeogram4Caption = (globalPrompt: string, regions: Ideogram4Re */ export const buildIdeogram4Prompt = (state: RootState, manager: CanvasManager | null): Ideogram4PromptResult => { const globalPrompt = selectPositivePrompt(state); + const colorPalette = selectIdeogram4ColorPalette(state); // No canvas manager (e.g. the Generate tab) → no regions to read. if (manager === null) { - return buildIdeogram4Caption(globalPrompt, []); + return buildIdeogram4Caption(globalPrompt, [], colorPalette); } const canvas = selectCanvasSlice(state); @@ -133,5 +143,5 @@ export const buildIdeogram4Prompt = (state: RootState, manager: CanvasManager | regions.push({ prompt, bbox }); } - return buildIdeogram4Caption(globalPrompt, regions); + return buildIdeogram4Caption(globalPrompt, regions, colorPalette); }; diff --git a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4ColorPalette.tsx b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4ColorPalette.tsx new file mode 100644 index 00000000000..6d14f16bfa9 --- /dev/null +++ b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4ColorPalette.tsx @@ -0,0 +1,103 @@ +import { Flex, FormControl, FormLabel, IconButton } from '@invoke-ai/ui-library'; +import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; +import { selectIdeogram4ColorPalette, setIdeogram4ColorPalette } from 'features/controlLayers/store/paramsSlice'; +import type { ChangeEvent } from 'react'; +import { memo, useCallback } from 'react'; +import { useTranslation } from 'react-i18next'; +import { PiPlusBold, PiXBold } from 'react-icons/pi'; + +const MAX_COLORS = 16; +const DEFAULT_COLOR = '#808080'; +const HEX_RE = /^#[0-9A-Fa-f]{6}$/; + +const SWATCH_STYLE = { + width: 28, + height: 28, + padding: 0, + border: 'none', + background: 'none', + cursor: 'pointer', +} as const; + +type ColorSwatchProps = { + index: number; + color: string; + onSet: (index: number, value: string) => void; + onRemove: (index: number) => void; +}; + +const ColorSwatch = memo(({ index, color, onSet, onRemove }: ColorSwatchProps) => { + const { t } = useTranslation(); + const handleChange = useCallback((e: ChangeEvent) => onSet(index, e.target.value), [index, onSet]); + const handleRemove = useCallback(() => onRemove(index), [index, onRemove]); + return ( + + + } + size="xs" + variant="ghost" + onClick={handleRemove} + /> + + ); +}); +ColorSwatch.displayName = 'ColorSwatch'; + +// Up to 16 hex colors injected into the JSON caption's style_description.color_palette. Only applies +// in auto-build mode (ignored when the prompt is raw JSON). +const ParamIdeogram4ColorPalette = () => { + const { t } = useTranslation(); + const palette = useAppSelector(selectIdeogram4ColorPalette); + const dispatch = useAppDispatch(); + + const setColor = useCallback( + (index: number, value: string) => { + const next = palette.slice(); + next[index] = value.toUpperCase(); + dispatch(setIdeogram4ColorPalette(next)); + }, + [palette, dispatch] + ); + const removeColor = useCallback( + (index: number) => { + dispatch(setIdeogram4ColorPalette(palette.filter((_, i) => i !== index))); + }, + [palette, dispatch] + ); + const addColor = useCallback(() => { + if (palette.length >= MAX_COLORS) { + return; + } + dispatch(setIdeogram4ColorPalette([...palette, DEFAULT_COLOR])); + }, [palette, dispatch]); + + return ( + + {t('parameters.ideogram4ColorPalette')} + + {palette.map((color, index) => ( + + ))} + {palette.length < MAX_COLORS && ( + } + size="sm" + variant="ghost" + onClick={addColor} + /> + )} + + + ); +}; + +export default memo(ParamIdeogram4ColorPalette); diff --git a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4GuidanceScale.tsx b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4GuidanceScale.tsx new file mode 100644 index 00000000000..39b97b82c65 --- /dev/null +++ b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4GuidanceScale.tsx @@ -0,0 +1,68 @@ +import { CompositeNumberInput, CompositeSlider, FormControl, FormLabel, Text } from '@invoke-ai/ui-library'; +import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; +import { selectIdeogram4GuidanceScale, setIdeogram4GuidanceScale } from 'features/controlLayers/store/paramsSlice'; +import type React from 'react'; +import { memo, useCallback } from 'react'; +import { useTranslation } from 'react-i18next'; +import { PiXBold } from 'react-icons/pi'; + +// The preset's main per-step guidance weight (gw) is 7.0; shown as the "auto" default. +const PRESET_MAIN_GW = 7; +const MARKS = [1, 4, 7, 10, 12]; + +// Optional override of the main guidance weight. null = use the preset's guidance schedule. +const ParamIdeogram4GuidanceScale = () => { + const { t } = useTranslation(); + const guidanceScale = useAppSelector(selectIdeogram4GuidanceScale); + const dispatch = useAppDispatch(); + + const onChange = useCallback((v: number) => dispatch(setIdeogram4GuidanceScale(v)), [dispatch]); + const onReset = useCallback( + (e: React.MouseEvent) => { + e.preventDefault(); + e.stopPropagation(); + dispatch(setIdeogram4GuidanceScale(null)); + }, + [dispatch] + ); + + const displayValue = guidanceScale ?? PRESET_MAIN_GW; + + return ( + + + {t('parameters.ideogram4GuidanceScale')}{' '} + {guidanceScale !== null ? ( + + + + ) : ( + + ({t('common.auto').toLowerCase()}) + + )} + + + + + ); +}; + +export default memo(ParamIdeogram4GuidanceScale); diff --git a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Mu.tsx b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Mu.tsx new file mode 100644 index 00000000000..a41c341aeaa --- /dev/null +++ b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Mu.tsx @@ -0,0 +1,73 @@ +import { CompositeNumberInput, CompositeSlider, FormControl, FormLabel, Text } from '@invoke-ai/ui-library'; +import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; +import { + selectIdeogram4Mu, + selectIdeogram4SamplerPreset, + setIdeogram4Mu, +} from 'features/controlLayers/store/paramsSlice'; +import { IDEOGRAM4_PRESET_DEFAULTS } from 'features/parameters/components/Core/ParamIdeogram4SamplerPreset'; +import type React from 'react'; +import { memo, useCallback } from 'react'; +import { useTranslation } from 'react-i18next'; +import { PiXBold } from 'react-icons/pi'; + +const MARKS = [0, 0.5, 1, 1.5, 2]; + +// Optional override of the logit-normal schedule mean (mu). null = use the preset's mu. +const ParamIdeogram4Mu = () => { + const { t } = useTranslation(); + const mu = useAppSelector(selectIdeogram4Mu); + const preset = useAppSelector(selectIdeogram4SamplerPreset); + const dispatch = useAppDispatch(); + + const presetMu = IDEOGRAM4_PRESET_DEFAULTS[preset]?.mu ?? 0; + const onChange = useCallback((v: number) => dispatch(setIdeogram4Mu(v)), [dispatch]); + const onReset = useCallback( + (e: React.MouseEvent) => { + e.preventDefault(); + e.stopPropagation(); + dispatch(setIdeogram4Mu(null)); + }, + [dispatch] + ); + + const displayValue = mu ?? presetMu; + + return ( + + + {t('parameters.ideogram4ScheduleShift')}{' '} + {mu !== null ? ( + + + + ) : ( + + ({t('common.auto').toLowerCase()}) + + )} + + + + + ); +}; + +export default memo(ParamIdeogram4Mu); diff --git a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx index a25de5beba3..b294b30981a 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx @@ -14,6 +14,14 @@ const IDEOGRAM4_SAMPLER_PRESET_OPTIONS: ComboboxOption[] = [ { value: 'V4_TURBO_12', label: 'Turbo (12 steps)' }, ]; +// Per-preset step count and schedule mean (mu), mirroring the backend PRESETS. Used by the advanced +// override controls to show the active preset's value as the "auto" default. +export const IDEOGRAM4_PRESET_DEFAULTS: Record = { + V4_QUALITY_48: { steps: 48, mu: 0.0 }, + V4_DEFAULT_20: { steps: 20, mu: 0.0 }, + V4_TURBO_12: { steps: 12, mu: 0.5 }, +}; + const ParamIdeogram4SamplerPreset = () => { const dispatch = useAppDispatch(); const { t } = useTranslation(); diff --git a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Steps.tsx b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Steps.tsx new file mode 100644 index 00000000000..ae5ed70520d --- /dev/null +++ b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Steps.tsx @@ -0,0 +1,71 @@ +import { CompositeNumberInput, CompositeSlider, FormControl, FormLabel, Text } from '@invoke-ai/ui-library'; +import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; +import { + selectIdeogram4SamplerPreset, + selectIdeogram4Steps, + setIdeogram4Steps, +} from 'features/controlLayers/store/paramsSlice'; +import { IDEOGRAM4_PRESET_DEFAULTS } from 'features/parameters/components/Core/ParamIdeogram4SamplerPreset'; +import type React from 'react'; +import { memo, useCallback } from 'react'; +import { useTranslation } from 'react-i18next'; +import { PiXBold } from 'react-icons/pi'; + +const MARKS = [1, 12, 20, 48, 100]; + +// Optional override of the sampler preset's step count. null = use the preset. +const ParamIdeogram4Steps = () => { + const { t } = useTranslation(); + const steps = useAppSelector(selectIdeogram4Steps); + const preset = useAppSelector(selectIdeogram4SamplerPreset); + const dispatch = useAppDispatch(); + + const presetSteps = IDEOGRAM4_PRESET_DEFAULTS[preset]?.steps ?? 48; + const onChange = useCallback((v: number) => dispatch(setIdeogram4Steps(v)), [dispatch]); + const onReset = useCallback( + (e: React.MouseEvent) => { + e.preventDefault(); + e.stopPropagation(); + dispatch(setIdeogram4Steps(null)); + }, + [dispatch] + ); + + const displayValue = steps ?? presetSteps; + + return ( + + + {t('parameters.steps')}{' '} + {steps !== null ? ( + + + + ) : ( + + ({t('common.auto').toLowerCase()}) + + )} + + + + + ); +}; + +export default memo(ParamIdeogram4Steps); diff --git a/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx b/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx index bfb69b945c8..025e9677b79 100644 --- a/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx +++ b/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx @@ -8,6 +8,7 @@ import { selectIsExternal, selectIsFLUX, selectIsFlux2, + selectIsIdeogram4, selectIsQwenImage, selectIsSD3, selectIsZImage, @@ -25,6 +26,10 @@ import ParamQwenImageComponentSourceSelect from 'features/parameters/components/ import ParamQwenImageQuantization from 'features/parameters/components/Advanced/ParamQwenImageQuantization'; import ParamT5EncoderModelSelect from 'features/parameters/components/Advanced/ParamT5EncoderModelSelect'; import ParamZImageQwen3VaeModelSelect from 'features/parameters/components/Advanced/ParamZImageQwen3VaeModelSelect'; +import ParamIdeogram4ColorPalette from 'features/parameters/components/Core/ParamIdeogram4ColorPalette'; +import ParamIdeogram4GuidanceScale from 'features/parameters/components/Core/ParamIdeogram4GuidanceScale'; +import ParamIdeogram4Mu from 'features/parameters/components/Core/ParamIdeogram4Mu'; +import ParamIdeogram4Steps from 'features/parameters/components/Core/ParamIdeogram4Steps'; import ParamSeamlessXAxis from 'features/parameters/components/Seamless/ParamSeamlessXAxis'; import ParamSeamlessYAxis from 'features/parameters/components/Seamless/ParamSeamlessYAxis'; import ParamColorCompensation from 'features/parameters/components/VAEModel/ParamColorCompensation'; @@ -51,6 +56,7 @@ export const AdvancedSettingsAccordion = memo(() => { const isFlux2 = useAppSelector(selectIsFlux2); const isSD3 = useAppSelector(selectIsSD3); const isZImage = useAppSelector(selectIsZImage); + const isIdeogram4 = useAppSelector(selectIsIdeogram4); const isExternal = useAppSelector(selectIsExternal); const isQwenImage = useAppSelector(selectIsQwenImage); const isAnima = useAppSelector(selectIsAnima); @@ -107,13 +113,13 @@ export const AdvancedSettingsAccordion = memo(() => { return ( - {!isZImage && !isAnima && !isFlux2 && !isQwenImage && ( + {!isZImage && !isAnima && !isFlux2 && !isQwenImage && !isIdeogram4 && ( {isFLUX ? : } {!isFLUX && !isSD3 && } )} - {!isFLUX && !isFlux2 && !isSD3 && !isZImage && !isQwenImage && !isAnima && ( + {!isFLUX && !isFlux2 && !isSD3 && !isZImage && !isQwenImage && !isAnima && !isIdeogram4 && ( <> @@ -166,6 +172,16 @@ export const AdvancedSettingsAccordion = memo(() => { )} + {isIdeogram4 && ( + <> + + + + + + + + )} ); diff --git a/invokeai/frontend/web/src/services/api/schema.ts b/invokeai/frontend/web/src/services/api/schema.ts index 8b77ef7a95d..86100e8cedb 100644 --- a/invokeai/frontend/web/src/services/api/schema.ts +++ b/invokeai/frontend/web/src/services/api/schema.ts @@ -4352,7 +4352,6 @@ export type components = { /** * Resize To * @description Dimensions to resize the image to, must be stringified tuple of 2 integers. Max total pixel count: 16777216 - * @example "[1024,1024]" */ resize_to?: string | null; /** @@ -13464,6 +13463,24 @@ export type components = { * @default 0 */ seed?: number; + /** + * Steps + * @description Override the preset's step count. Leave empty to use the preset. + * @default null + */ + steps?: number | null; + /** + * Guidance Scale + * @description Override the main guidance weight (the preset's polish tail is preserved). Empty = use the preset. + * @default null + */ + guidance_scale?: number | null; + /** + * Mu + * @description Override the logit-normal schedule mean (resolution-adjusted internally). Empty = use the preset. + * @default null + */ + mu?: number | null; /** * type * @default ideogram4_denoise @@ -24146,18 +24163,6 @@ export type components = { /** * Model Keys * @description List of model keys to fetch related models for - * @example [ - * "aa3b247f-90c9-4416-bfcd-aeaa57a5339e", - * "ac32b914-10ab-496e-a24a-3068724b9c35" - * ] - * @example [ - * "b1c2d3e4-f5a6-7890-abcd-ef1234567890", - * "12345678-90ab-cdef-1234-567890abcdef", - * "fedcba98-7654-3210-fedc-ba9876543210" - * ] - * @example [ - * "3bb7c0eb-b6c8-469c-ad8c-4d69c06075e4" - * ] */ model_keys: string[]; }; @@ -24166,23 +24171,11 @@ export type components = { /** * Model Key 1 * @description The key of the first model in the relationship - * @example aa3b247f-90c9-4416-bfcd-aeaa57a5339e - * @example ac32b914-10ab-496e-a24a-3068724b9c35 - * @example d944abfd-c7c3-42e2-a4ff-da640b29b8b4 - * @example b1c2d3e4-f5a6-7890-abcd-ef1234567890 - * @example 12345678-90ab-cdef-1234-567890abcdef - * @example fedcba98-7654-3210-fedc-ba9876543210 */ model_key_1: string; /** * Model Key 2 * @description The key of the second model in the relationship - * @example 3bb7c0eb-b6c8-469c-ad8c-4d69c06075e4 - * @example f0c3da4e-d9ff-42b5-a45c-23be75c887c9 - * @example 38170dd8-f1e5-431e-866c-2c81f1277fcc - * @example c57fea2d-7646-424c-b9ad-c0ba60fc68be - * @example 10f7807b-ab54-46a9-ab03-600e88c630a1 - * @example f6c1d267-cf87-4ee0-bee0-37e791eacab7 */ model_key_2: string; }; @@ -33910,8 +33903,7 @@ export interface operations { [name: string]: unknown; }; content: { - /** - * @example { + /** @example { * "path": "string", * "name": "string", * "base": "sd-1", @@ -33928,8 +33920,7 @@ export interface operations { * "prediction_type": "epsilon", * "repo_variant": "fp16", * "upcast_attention": false - * } - */ + * } */ "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; }; }; @@ -34017,8 +34008,7 @@ export interface operations { [name: string]: unknown; }; content: { - /** - * @example { + /** @example { * "path": "string", * "name": "string", * "base": "sd-1", @@ -34035,8 +34025,7 @@ export interface operations { * "prediction_type": "epsilon", * "repo_variant": "fp16", * "upcast_attention": false - * } - */ + * } */ "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; }; }; @@ -34090,8 +34079,7 @@ export interface operations { [name: string]: unknown; }; content: { - /** - * @example { + /** @example { * "path": "string", * "name": "string", * "base": "sd-1", @@ -34108,8 +34096,7 @@ export interface operations { * "prediction_type": "epsilon", * "repo_variant": "fp16", * "upcast_attention": false - * } - */ + * } */ "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; }; }; @@ -34825,8 +34812,7 @@ export interface operations { [name: string]: unknown; }; content: { - /** - * @example { + /** @example { * "path": "string", * "name": "string", * "base": "sd-1", @@ -34843,8 +34829,7 @@ export interface operations { * "prediction_type": "epsilon", * "repo_variant": "fp16", * "upcast_attention": false - * } - */ + * } */ "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]; }; }; @@ -36443,13 +36428,11 @@ export interface operations { [name: string]: unknown; }; content: { - /** - * @example [ + /** @example [ * "15e9eb28-8cfe-47c9-b610-37907a79fc3c", * "71272e82-0e5f-46d5-bca9-9a61f4bd8a82", * "a5d7cd49-1b98-4534-a475-aeee4ccf5fa2" - * ] - */ + * ] */ "application/json": string[]; }; }; @@ -36588,8 +36571,7 @@ export interface operations { [name: string]: unknown; }; content: { - /** - * @example [ + /** @example [ * "ca562b14-995e-4a42-90c1-9528f1a5921d", * "cc0c2b8a-c62e-41d6-878e-cc74dde5ca8f", * "18ca7649-6a9e-47d5-bc17-41ab1e8cec81", @@ -36597,8 +36579,7 @@ export interface operations { * "c382eaa3-0e28-4ab0-9446-408667699aeb", * "71272e82-0e5f-46d5-bca9-9a61f4bd8a82", * "a5d7cd49-1b98-4534-a475-aeee4ccf5fa2" - * ] - */ + * ] */ "application/json": string[]; }; }; From 443e0daa9766169d13741da4c982e16803897090 Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Thu, 25 Jun 2026 07:01:07 +0200 Subject: [PATCH 15/33] Use existing keys + fix select size --- invokeai/frontend/web/public/locales/en.json | 8 ++------ .../web/src/features/metadata/parsing.tsx | 8 ++++---- .../Core/ParamIdeogram4ColorPalette.tsx | 20 ++++--------------- .../Core/ParamIdeogram4GuidanceScale.tsx | 2 +- .../components/Core/ParamIdeogram4Mu.tsx | 2 +- .../Core/ParamIdeogram4SamplerPreset.tsx | 10 +++++++--- 6 files changed, 19 insertions(+), 31 deletions(-) diff --git a/invokeai/frontend/web/public/locales/en.json b/invokeai/frontend/web/public/locales/en.json index 44b1ece4102..ad8526f30c5 100644 --- a/invokeai/frontend/web/public/locales/en.json +++ b/invokeai/frontend/web/public/locales/en.json @@ -1043,12 +1043,6 @@ "qwen3Source": "Qwen3 Source", "recallParameters": "Recall Parameters", "recallParameter": "Recall {{label}}", - "ideogram4SamplerPreset": "Sampler Preset", - "ideogram4ScheduleShift": "Schedule Shift", - "ideogram4GuidanceScale": "Guidance Scale", - "ideogram4ColorPalette": "Color Palette", - "ideogram4AddColor": "Add color", - "ideogram4RemoveColor": "Remove color", "scheduler": "Scheduler", "seamlessXAxis": "Seamless X Axis", "seamlessYAxis": "Seamless Y Axis", @@ -1625,6 +1619,8 @@ }, "parameters": { "aspect": "Aspect", + "samplerPreset": "Sampler Preset", + "colorPalette": "Color Palette", "duration": "Duration", "lockAspectRatio": "Lock Aspect Ratio", "swapDimensions": "Swap Dimensions", diff --git a/invokeai/frontend/web/src/features/metadata/parsing.tsx b/invokeai/frontend/web/src/features/metadata/parsing.tsx index dbb82a0e282..24590388e62 100644 --- a/invokeai/frontend/web/src/features/metadata/parsing.tsx +++ b/invokeai/frontend/web/src/features/metadata/parsing.tsx @@ -901,7 +901,7 @@ const Ideogram4SamplerPreset: SingleMetadataHandler) => ( @@ -959,7 +959,7 @@ const Ideogram4GuidanceScale: SingleMetadataHandler = { } store.dispatch(setIdeogram4GuidanceScale(value)); }, - i18nKey: 'parameters.ideogram4GuidanceScale', + i18nKey: 'parameters.guidance', LabelComponent: MetadataLabel, ValueComponent: ({ value }: SingleMetadataValueProps) => ( @@ -987,7 +987,7 @@ const Ideogram4Mu: SingleMetadataHandler = { } store.dispatch(setIdeogram4Mu(value)); }, - i18nKey: 'parameters.ideogram4ScheduleShift', + i18nKey: 'parameters.shift', LabelComponent: MetadataLabel, ValueComponent: ({ value }: SingleMetadataValueProps) => ( @@ -1012,7 +1012,7 @@ const Ideogram4ColorPalette: SingleMetadataHandler = { } store.dispatch(setIdeogram4ColorPalette(value)); }, - i18nKey: 'parameters.ideogram4ColorPalette', + i18nKey: 'parameters.colorPalette', LabelComponent: MetadataLabel, ValueComponent: ({ value }: SingleMetadataValueProps) => ( diff --git a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4ColorPalette.tsx b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4ColorPalette.tsx index 6d14f16bfa9..b9a7e726a37 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4ColorPalette.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4ColorPalette.tsx @@ -34,18 +34,12 @@ const ColorSwatch = memo(({ index, color, onSet, onRemove }: ColorSwatchProps) = - } - size="xs" - variant="ghost" - onClick={handleRemove} - /> + } size="xs" variant="ghost" onClick={handleRemove} /> ); }); @@ -81,19 +75,13 @@ const ParamIdeogram4ColorPalette = () => { return ( - {t('parameters.ideogram4ColorPalette')} + {t('parameters.colorPalette')} {palette.map((color, index) => ( ))} {palette.length < MAX_COLORS && ( - } - size="sm" - variant="ghost" - onClick={addColor} - /> + } size="sm" variant="ghost" onClick={addColor} /> )} diff --git a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4GuidanceScale.tsx b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4GuidanceScale.tsx index 39b97b82c65..c5d579e73b5 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4GuidanceScale.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4GuidanceScale.tsx @@ -31,7 +31,7 @@ const ParamIdeogram4GuidanceScale = () => { return ( - {t('parameters.ideogram4GuidanceScale')}{' '} + {t('parameters.guidance')}{' '} {guidanceScale !== null ? ( diff --git a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Mu.tsx b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Mu.tsx index a41c341aeaa..6a5c2e01e74 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Mu.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Mu.tsx @@ -36,7 +36,7 @@ const ParamIdeogram4Mu = () => { return ( - {t('parameters.ideogram4ScheduleShift')}{' '} + {t('parameters.shift')}{' '} {mu !== null ? ( diff --git a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx index b294b30981a..e39245c4b1a 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx @@ -1,4 +1,4 @@ -import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library'; +import type { ComboboxOnChange, ComboboxOption, SystemStyleObject } from '@invoke-ai/ui-library'; import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library'; import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import { selectIdeogram4SamplerPreset, setIdeogram4SamplerPreset } from 'features/controlLayers/store/paramsSlice'; @@ -22,6 +22,10 @@ export const IDEOGRAM4_PRESET_DEFAULTS: Record { const dispatch = useAppDispatch(); const { t } = useTranslation(); @@ -41,8 +45,8 @@ const ParamIdeogram4SamplerPreset = () => { return ( - {t('parameters.ideogram4SamplerPreset')} - + {t('parameters.samplerPreset')} + ); }; From c2ca937fbe22bedc29744530e2f726c6336586e6 Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Thu, 25 Jun 2026 07:12:11 +0200 Subject: [PATCH 16/33] Update Readme --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index afc0211bd91..73d0fbf60e0 100644 --- a/README.md +++ b/README.md @@ -78,6 +78,7 @@ Invoke features an organized gallery system for easily storing, accessing, and r - Anima - Qwen Image - Qwen Image Edit +- Ideogram 4 - Nano Banana (API Only) - GPT Image (API Only) - Wan (API Only) From d3bca0573847aa956f9c16073e88fe33d3a33153 Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Thu, 16 Jul 2026 04:57:53 +0200 Subject: [PATCH 17/33] feat(ideogram4): add Ideogram 4 to starter models with non-commercial license hint - Implement the weight-only fp8 text-encoder load path (was NotImplementedError); validated against the real fp8 build + add CPU unit tests for the fp8 mechanism - Show Ideogram 4 handlers in the Recall Parameters tab - Recall the assembled JSON caption back into the positive prompt - Translate the metadata "Auto" values - Document Ideogram 4 (install/license, regional-guidance JSON prompting, presets) - Add Ideogram 4 nf4 (CUDA) + fp8 (any device) starter models and bundle - Surface the FLUX-style Non-Commercial License popover for Ideogram 4 models - Note the gated HuggingFace license requirement in the model descriptions --- docs/src/content/docs/concepts/models.mdx | 32 ++++ .../load/model_loaders/ideogram4.py | 36 +++-- .../backend/model_manager/starter_models.py | 32 ++++ invokeai/frontend/web/public/locales/en.json | 3 +- .../ImageMetadataActions.tsx | 6 + .../web/src/features/metadata/parsing.tsx | 56 +++++-- .../frontend/web/src/services/api/types.ts | 8 +- .../ideogram4/test_quantized_loading.py | 142 ++++++++++++++++++ 8 files changed, 289 insertions(+), 26 deletions(-) create mode 100644 tests/backend/ideogram4/test_quantized_loading.py diff --git a/docs/src/content/docs/concepts/models.mdx b/docs/src/content/docs/concepts/models.mdx index 3ebdf27c788..0eb6868db8a 100644 --- a/docs/src/content/docs/concepts/models.mdx +++ b/docs/src/content/docs/concepts/models.mdx @@ -51,6 +51,38 @@ In this situation, you may need to provide some additional information to identi [set up in the config file]: ../../configuration/invokeai-yaml +## Ideogram 4 + +Ideogram 4 is an open-weight text-to-image model with a distinctive **structured JSON prompt**: instead of a single sentence, the model is trained to read an overall scene description plus a list of regions, each with a bounding box and its own text. Invoke assembles this JSON for you. + +### Installing Ideogram 4 + +The weights are gated on HuggingFace under a **non-commercial license**. Open the model page, accept the terms, and make sure your HuggingFace token is [set up in the config file] before installing. Two builds are available: + +- [`ideogram-ai/ideogram-4-nf4`](https://huggingface.co/ideogram-ai/ideogram-4-nf4) — nf4 quantized, **CUDA only**, fits in 24 GB VRAM. Recommended for NVIDIA GPUs. +- [`ideogram-ai/ideogram-4-fp8`](https://huggingface.co/ideogram-ai/ideogram-4-fp8) — fp8 quantized, runs on **any device**, with higher memory use. + +Paste either repo ID into the Model Manager's HuggingFace / URL field to install. + +### Prompting Ideogram 4 + +When an Ideogram 4 model is selected, Invoke builds the structured JSON prompt automatically: + +- The **positive prompt** becomes the overall scene description. +- Each enabled **Regional Guidance** layer on the Canvas contributes one element: its drawn box becomes the region's bounding box and its prompt becomes that region's description. Draw a box where you want something and describe it there. +- To drive the model directly, paste a **raw JSON** object into the prompt box — anything starting with `{` is passed through unchanged. + +The exact JSON that was encoded is stored in the image metadata as **Structured Caption**, and can be recalled straight back into the prompt box from the metadata viewer. + +:::note[No negative prompt] +Ideogram 4 does not use a negative prompt — it has a dedicated unconditional branch instead, so the negative prompt box has no effect. +::: + +### Generation settings + +- **Sampler Preset** — the primary quality/speed control. `Quality (48 steps)`, `Default (20 steps)`, and `Turbo (12 steps)` each bundle a step count, a guidance schedule, and the schedule shift. +- **Advanced** overrides (all optional, leave on *Auto* to use the preset's values): **Steps**, **Guidance Scale**, **Schedule Shift (mu)**, and a **Color Palette** that biases the generated colors. + ## Editing model metadata Every model has an editable **Source URL** field alongside its name and description. Use it to record where a model came from — for example a Civitai or HuggingFace page — independent of how it was originally installed. The URL is editable from the model's **Edit** view and appears as a clickable link in the model header once set. Models without a URL simply hide the field. diff --git a/invokeai/backend/model_manager/load/model_loaders/ideogram4.py b/invokeai/backend/model_manager/load/model_loaders/ideogram4.py index a764b6e6f6c..6c7401c66bd 100644 --- a/invokeai/backend/model_manager/load/model_loaders/ideogram4.py +++ b/invokeai/backend/model_manager/load/model_loaders/ideogram4.py @@ -131,6 +131,11 @@ def _load_text_encoder(self, model_path: Path) -> AnyModel: import accelerate from transformers import AutoConfig, AutoModel + from invokeai.backend.ideogram4.quantized_loading import ( + FP8_TEXT_ENCODER_CONFIG_FLAG, + load_fp8_state_dict, + swap_linears_to_fp8, + ) from invokeai.backend.quantization.bnb_nf4 import quantize_model_nf4 encoder_path = model_path / "text_encoder" @@ -138,18 +143,11 @@ def _load_text_encoder(self, model_path: Path) -> AnyModel: compute_dtype = TorchDevice.choose_bfloat16_safe_dtype(target_device) raw_cfg = json.loads((encoder_path / "config.json").read_text(encoding="utf-8")) - if raw_cfg.get("ideogram_fp8_weight_only", False): - # The fp8 text encoder uses Ideogram's custom weight-only fp8 layout; supporting it - # requires the vendored _load_fp8_text_encoder path. Deferred (nf4 is the 24 GB path). - raise NotImplementedError( - "Ideogram 4 fp8 text encoder loading is not yet implemented; use the nf4 build." - ) - # Build the bare architecture from config, then quantize with InvokeAI's InvokeLinearNF4 and - # load the prequantized weights. We must NOT use transformers' native bitsandbytes loading - # (from_pretrained with a quantization_config) because the resulting bnb Linear4bit layers are - # not compatible with InvokeAI's partial-loading model cache. This mirrors how the FLUX T5 bnb - # encoder is loaded. + # Build the bare architecture from config, then load the prequantized weights ourselves. We must + # NOT use transformers' native bitsandbytes loading (from_pretrained with a quantization_config) + # because the resulting bnb Linear4bit layers are not compatible with InvokeAI's partial-loading + # model cache. This mirrors how the FLUX T5 bnb encoder is loaded. cfg = AutoConfig.from_pretrained(encoder_path, local_files_only=True) # Drop the quantization_config so from_config builds a plain (unquantized) architecture. if hasattr(cfg, "quantization_config"): @@ -158,12 +156,26 @@ def _load_text_encoder(self, model_path: Path) -> AnyModel: sd = _load_local_state_dict(encoder_path, "model") self._ram_cache.make_room(sum(t.nelement() * t.element_size() for t in sd.values())) + if raw_cfg.get(FP8_TEXT_ENCODER_CONFIG_FLAG, False): + # Weight-only fp8 (e4m3): build the empty architecture, swap the quantized Linears for + # Fp8Linear (gated on a saved per-row scale), then load. Mirrors the transformer fp8 branch; + # runs on any device. strict=False tolerates the tied embed weights transformers resolves + # itself; unexpected keys still raise. assign=True fills the meta params directly. + with accelerate.init_empty_weights(): + model: torch.nn.Module = AutoModel.from_config(cfg) + swap_linears_to_fp8(model, sd, compute_dtype=compute_dtype) + load_fp8_state_dict( + model, sd, device=torch.device("cpu"), dtype=compute_dtype, assign=True, strict=False + ) + model.eval() + return model + is_bnb_nf4 = "quantization_config" in raw_cfg and bool( raw_cfg["quantization_config"].get("load_in_4bit") ) with accelerate.init_empty_weights(): - model: torch.nn.Module = AutoModel.from_config(cfg) + model = AutoModel.from_config(cfg) if is_bnb_nf4: model = quantize_model_nf4(model, modules_to_not_convert=set(), compute_dtype=compute_dtype) diff --git a/invokeai/backend/model_manager/starter_models.py b/invokeai/backend/model_manager/starter_models.py index 9bc58e44269..1e674ee5bd3 100644 --- a/invokeai/backend/model_manager/starter_models.py +++ b/invokeai/backend/model_manager/starter_models.py @@ -1574,6 +1574,30 @@ def _gemini_3_resolution_presets( ) # endregion +# region Ideogram 4 +# Self-contained diffusers pipelines (both transformers + Qwen3-VL text encoder + VAE in one folder), so +# no separate dependencies. Gated, non-commercial license: the license must be accepted on the +# HuggingFace model page and a HuggingFace token configured before the download will succeed — same as +# FLUX.1 dev. +ideogram_4_nf4 = StarterModel( + name="Ideogram 4 (nf4)", + base=BaseModelType.Ideogram4, + source="ideogram-ai/ideogram-4-nf4", + description="Ideogram 4 text-to-image in nf4-quantized Diffusers format (CUDA only). Structured JSON " + "prompting with regional layout control. Non-commercial license — accept it on HuggingFace first. ~16GB", + type=ModelType.Main, +) + +ideogram_4_fp8 = StarterModel( + name="Ideogram 4 (fp8)", + base=BaseModelType.Ideogram4, + source="ideogram-ai/ideogram-4-fp8", + description="Ideogram 4 text-to-image in fp8-quantized Diffusers format (runs on any device, higher " + "memory use). Non-commercial license — accept it on HuggingFace first. ~26GB", + type=ModelType.Main, +) +# endregion + # List of starter models, displayed on the frontend. # The order/sort of this list is not changed by the frontend - set it how you want it here. STARTER_MODELS: list[StarterModel] = [ @@ -1584,6 +1608,8 @@ def _gemini_3_resolution_presets( flux_dev, sd35_medium, sd35_large, + ideogram_4_nf4, + ideogram_4_fp8, cyberrealistic_sd1, rev_animated_sd1, dreamshaper_8_sd1, @@ -1799,6 +1825,11 @@ def _gemini_3_resolution_presets( anima_vae, ] +# nf4 is the recommended 24GB CUDA path; the fp8 build is offered separately for non-CUDA / more VRAM. +ideogram_bundle: list[StarterModel] = [ + ideogram_4_nf4, +] + STARTER_BUNDLES: dict[str, StarterModelBundle] = { BaseModelType.StableDiffusion1: StarterModelBundle(name="Stable Diffusion 1.5", models=sd1_bundle), BaseModelType.StableDiffusionXL: StarterModelBundle(name="SDXL", models=sdxl_bundle), @@ -1807,6 +1838,7 @@ def _gemini_3_resolution_presets( BaseModelType.ZImage: StarterModelBundle(name="Z-Image Turbo", models=zimage_bundle), BaseModelType.QwenImage: StarterModelBundle(name="Qwen Image", models=qwen_image_bundle), BaseModelType.Anima: StarterModelBundle(name="Anima", models=anima_bundle), + BaseModelType.Ideogram4: StarterModelBundle(name="Ideogram 4", models=ideogram_bundle), } assert len(STARTER_MODELS) == len({m.source for m in STARTER_MODELS}), "Duplicate starter models" diff --git a/invokeai/frontend/web/public/locales/en.json b/invokeai/frontend/web/public/locales/en.json index bd81f8c75cd..4f80390c399 100644 --- a/invokeai/frontend/web/public/locales/en.json +++ b/invokeai/frontend/web/public/locales/en.json @@ -1629,6 +1629,7 @@ "aspect": "Aspect", "samplerPreset": "Sampler Preset", "colorPalette": "Color Palette", + "ideogram4Caption": "Structured Caption", "duration": "Duration", "lockAspectRatio": "Lock Aspect Ratio", "swapDimensions": "Swap Dimensions", @@ -2445,7 +2446,7 @@ "fluxDevLicense": { "heading": "Non-Commercial License", "paragraphs": [ - "This model is licensed for non-commercial use only. FLUX.1 [dev] models use the FLUX.1 [dev] Non-Commercial License, and FLUX.2 Klein 9B uses the FLUX.2 Non-Commercial License." + "This model is licensed for non-commercial use only. FLUX.1 [dev] models use the FLUX.1 [dev] Non-Commercial License, FLUX.2 Klein 9B uses the FLUX.2 Non-Commercial License, and Ideogram 4 uses the Ideogram 4 Non-Commercial License." ] }, "optimizedDenoising": { diff --git a/invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataActions.tsx b/invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataActions.tsx index f20ef705be3..a0a3489630a 100644 --- a/invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataActions.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataActions.tsx @@ -61,6 +61,12 @@ export const IMAGE_METADATA_ACTION_HANDLERS: ImageMetadataActionHandler[] = [ ImageMetadataHandlers.QwenImageQuantization, ImageMetadataHandlers.QwenImageShift, ImageMetadataHandlers.ZImageShift, + ImageMetadataHandlers.Ideogram4SamplerPreset, + ImageMetadataHandlers.Ideogram4Steps, + ImageMetadataHandlers.Ideogram4GuidanceScale, + ImageMetadataHandlers.Ideogram4Mu, + ImageMetadataHandlers.Ideogram4ColorPalette, + ImageMetadataHandlers.Ideogram4Caption, ImageMetadataHandlers.CanvasLayers, ImageMetadataHandlers.RefImages, ImageMetadataHandlers.KleinVAEModel, diff --git a/invokeai/frontend/web/src/features/metadata/parsing.tsx b/invokeai/frontend/web/src/features/metadata/parsing.tsx index 51fd5f63719..ec37f680232 100644 --- a/invokeai/frontend/web/src/features/metadata/parsing.tsx +++ b/invokeai/frontend/web/src/features/metadata/parsing.tsx @@ -893,9 +893,10 @@ const ZImageShift: SingleMetadataHandler = { }, i18nKey: 'metadata.zImageShift', LabelComponent: MetadataLabel, - ValueComponent: ({ value }: SingleMetadataValueProps) => ( - - ), + ValueComponent: ({ value }: SingleMetadataValueProps) => { + const { t } = useTranslation(); + return ; + }, }; //#endregion ZImageShift @@ -947,9 +948,10 @@ const Ideogram4Steps: SingleMetadataHandler = { }, i18nKey: 'parameters.steps', LabelComponent: MetadataLabel, - ValueComponent: ({ value }: SingleMetadataValueProps) => ( - - ), + ValueComponent: ({ value }: SingleMetadataValueProps) => { + const { t } = useTranslation(); + return ; + }, }; //#endregion Ideogram4Steps @@ -975,9 +977,10 @@ const Ideogram4GuidanceScale: SingleMetadataHandler = { }, i18nKey: 'parameters.guidance', LabelComponent: MetadataLabel, - ValueComponent: ({ value }: SingleMetadataValueProps) => ( - - ), + ValueComponent: ({ value }: SingleMetadataValueProps) => { + const { t } = useTranslation(); + return ; + }, }; //#endregion Ideogram4GuidanceScale @@ -1003,9 +1006,10 @@ const Ideogram4Mu: SingleMetadataHandler = { }, i18nKey: 'parameters.shift', LabelComponent: MetadataLabel, - ValueComponent: ({ value }: SingleMetadataValueProps) => ( - - ), + ValueComponent: ({ value }: SingleMetadataValueProps) => { + const { t } = useTranslation(); + return ; + }, }; //#endregion Ideogram4Mu @@ -1034,6 +1038,33 @@ const Ideogram4ColorPalette: SingleMetadataHandler = { }; //#endregion Ideogram4ColorPalette +//#region Ideogram4Caption +// For regional/structured prompts the value actually encoded by the model is this assembled JSON +// caption, while `positive_prompt` holds the raw overall description (via the graph's decoy node). +// Recalling it into the positive prompt round-trips: the graph builder detects a leading `{` and passes +// the JSON through unchanged. +const Ideogram4Caption: SingleMetadataHandler = { + [SingleMetadataKey]: true, + type: 'Ideogram4Caption', + parse: (metadata, _store) => { + const raw = getProperty(metadata, 'ideogram4_caption'); + if (raw === undefined) { + return Promise.reject(); + } + return Promise.resolve(z.string().parse(raw)); + }, + recall: (value, store) => { + if (selectBase(store.getState()) !== 'ideogram-4') { + return; + } + store.dispatch(positivePromptChanged(value)); + }, + i18nKey: 'parameters.ideogram4Caption', + LabelComponent: MetadataLabel, + ValueComponent: ({ value }: SingleMetadataValueProps) => , +}; +//#endregion Ideogram4Caption + //#region RefinerModel const RefinerModel: SingleMetadataHandler = { [SingleMetadataKey]: true, @@ -1802,6 +1833,7 @@ export const ImageMetadataHandlers = { Ideogram4GuidanceScale, Ideogram4Mu, Ideogram4ColorPalette, + Ideogram4Caption, LoRAs, CanvasLayers, RefImages, diff --git a/invokeai/frontend/web/src/services/api/types.ts b/invokeai/frontend/web/src/services/api/types.ts index 27c6fcbf3c3..20271008bec 100644 --- a/invokeai/frontend/web/src/services/api/types.ts +++ b/invokeai/frontend/web/src/services/api/types.ts @@ -466,8 +466,14 @@ const isFlux2Klein9BMainModelConfig = (config: AnyModelConfig): config is MainMo return config.type === 'main' && config.base === 'flux2' && config.name.toLowerCase().includes('9b'); }; +const isIdeogram4MainModelConfig = (config: AnyModelConfig): config is MainModelConfig => { + return config.type === 'main' && config.base === 'ideogram-4'; +}; + export const isNonCommercialMainModelConfig = (config: AnyModelConfig): config is MainModelConfig => { - return isFluxDevMainModelConfig(config) || isFlux2Klein9BMainModelConfig(config); + return ( + isFluxDevMainModelConfig(config) || isFlux2Klein9BMainModelConfig(config) || isIdeogram4MainModelConfig(config) + ); }; export const isFluxFillMainModelModelConfig = (config: AnyModelConfig): config is MainModelConfig => { diff --git a/tests/backend/ideogram4/test_quantized_loading.py b/tests/backend/ideogram4/test_quantized_loading.py new file mode 100644 index 00000000000..fa5e9ad154c --- /dev/null +++ b/tests/backend/ideogram4/test_quantized_loading.py @@ -0,0 +1,142 @@ +"""Tests for the Ideogram 4 weight-only fp8 loading mechanism. + +The Ideogram 4 fp8 text encoder is loaded by building the empty architecture, swapping its +quantized ``nn.Linear`` layers for ``Fp8Linear`` (gated on a saved per-row scale), then loading the +prequantized state dict with ``assign=True`` / ``strict=False`` — the exact pattern the model loader +uses in ``model_loaders/ideogram4.py::_load_text_encoder``. These tests exercise that mechanism on a +tiny CPU model so the fp8 path has regression coverage without a multi-GB checkpoint. +""" + +import accelerate +import pytest +import torch +import torch.nn as nn + +from invokeai.backend.ideogram4.quantized_loading import ( + FP8_TEXT_ENCODER_CONFIG_FLAG, + Fp8Linear, + is_fp8_state_dict, + load_fp8_state_dict, + quantize_weight_to_fp8, + swap_linears_to_fp8, +) + + +class _TinyEncoder(nn.Module): + """A stand-in for the text encoder: two Linears (fp8-quantized) around a non-quantized norm, + plus a non-persistent buffer that mimics the rotary caches transformers models compute in + ``__init__`` (and which must survive the meta-device build).""" + + def __init__(self) -> None: + super().__init__() + self.lin1 = nn.Linear(8, 16) + self.norm = nn.LayerNorm(16) + self.lin2 = nn.Linear(16, 4) + self.register_buffer("rope_cache", torch.arange(4, dtype=torch.float32), persistent=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.lin2(self.norm(self.lin1(x))) + + +def _make_fp8_state_dict(ref: _TinyEncoder, compute_dtype: torch.dtype) -> dict[str, torch.Tensor]: + """Build a prequantized state dict: the two Linears become fp8 weight + per-row scale, everything + else stays a normal float tensor. Mirrors the on-disk fp8 checkpoint layout.""" + sd: dict[str, torch.Tensor] = {} + for name in ("lin1", "lin2"): + lin: nn.Linear = getattr(ref, name) + q, scale = quantize_weight_to_fp8(lin.weight) + sd[f"{name}.weight"] = q + sd[f"{name}.weight_scale"] = scale + sd[f"{name}.bias"] = lin.bias.detach().to(compute_dtype) + sd["norm.weight"] = ref.norm.weight.detach().to(compute_dtype) + sd["norm.bias"] = ref.norm.bias.detach().to(compute_dtype) + return sd + + +def _dequant_reference(ref: _TinyEncoder, sd: dict[str, torch.Tensor], x: torch.Tensor) -> torch.Tensor: + """Forward pass using the dequantized fp8 weights — the exact math ``Fp8Linear.forward`` runs, so + the loaded model must match this to within dtype rounding (not the lossy original weights).""" + dtype = x.dtype + + def deq(name: str) -> tuple[torch.Tensor, torch.Tensor]: + w = sd[f"{name}.weight"].to(dtype) * sd[f"{name}.weight_scale"].to(dtype).unsqueeze(1) + return w, sd[f"{name}.bias"].to(dtype) + + w1, b1 = deq("lin1") + w2, b2 = deq("lin2") + h = torch.nn.functional.linear(x, w1, b1) + h = torch.nn.functional.layer_norm(h, (16,), sd["norm.weight"].to(dtype), sd["norm.bias"].to(dtype)) + return torch.nn.functional.linear(h, w2, b2) + + +def test_fp8_load_matches_loader_pattern() -> None: + """Build empty -> swap -> load(assign, strict=False), exactly as the ideogram4 loader does, and + verify no meta tensors survive and the forward matches the dequantized reference.""" + torch.manual_seed(0) + compute_dtype = torch.float32 + + ref = _TinyEncoder().to(compute_dtype).eval() + sd = _make_fp8_state_dict(ref, compute_dtype) + + assert is_fp8_state_dict(sd) + + with accelerate.init_empty_weights(): + model = _TinyEncoder() + swap_linears_to_fp8(model, sd, compute_dtype=compute_dtype) + + # Only the two Linears carry a saved scale, so exactly two get swapped. + assert sum(1 for m in model.modules() if isinstance(m, Fp8Linear)) == 2 + + load_fp8_state_dict(model, sd, device=torch.device("cpu"), dtype=compute_dtype, assign=True, strict=False) + model.eval() + + assert not any(p.is_meta for p in model.parameters()), "meta params remained after fp8 load" + assert not any(b.is_meta for b in model.buffers()), "meta buffers remained after fp8 load" + # The non-persistent rope buffer must have been rebuilt with real data by the meta-device init. + assert torch.equal(model.rope_cache, torch.arange(4, dtype=torch.float32)) + + x = torch.randn(2, 8, dtype=compute_dtype) + with torch.no_grad(): + out = model(x) + expected = _dequant_reference(ref, sd, x) + assert torch.allclose(out, expected, atol=1e-5, rtol=1e-4) + + +def test_fp8_load_rejects_unexpected_keys() -> None: + """A key the model has no home for must fail loudly rather than load silently.""" + torch.manual_seed(1) + compute_dtype = torch.float32 + ref = _TinyEncoder().to(compute_dtype).eval() + sd = _make_fp8_state_dict(ref, compute_dtype) + sd["lin1.bogus_extra"] = torch.zeros(3) + + model = _TinyEncoder().to(compute_dtype) + swap_linears_to_fp8(model, sd, compute_dtype=compute_dtype) + with pytest.raises(RuntimeError, match="unexpected keys"): + load_fp8_state_dict(model, sd, device=torch.device("cpu"), dtype=compute_dtype, strict=False) + + +def test_fp8_missing_key_strictness() -> None: + """strict=True raises on a missing weight; strict=False downgrades it to a warning.""" + torch.manual_seed(2) + compute_dtype = torch.float32 + ref = _TinyEncoder().to(compute_dtype).eval() + sd = _make_fp8_state_dict(ref, compute_dtype) + del sd["norm.bias"] + + def build() -> _TinyEncoder: + m = _TinyEncoder().to(compute_dtype) + swap_linears_to_fp8(m, sd, compute_dtype=compute_dtype) + return m + + with pytest.raises(RuntimeError, match="missing keys"): + load_fp8_state_dict(build(), sd, device=torch.device("cpu"), dtype=compute_dtype, strict=True) + + with pytest.warns(UserWarning, match="missing keys"): + load_fp8_state_dict(build(), sd, device=torch.device("cpu"), dtype=compute_dtype, strict=False) + + +def test_fp8_config_flag_constant() -> None: + """The loader keys the fp8 path off this exact config.json marker; pin it so a rename can't + silently reintroduce the 'importable but fails at encode time' bug.""" + assert FP8_TEXT_ENCODER_CONFIG_FLAG == "ideogram_fp8_weight_only" From ec1c65b7d13ed45069a12e7c3ae1ff84051904c7 Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Thu, 16 Jul 2026 05:03:33 +0200 Subject: [PATCH 18/33] Chore Ruff --- invokeai/app/invocations/primitives.py | 2 +- .../backend/ideogram4/quantized_loading.py | 431 +++++++++--------- .../load/model_loaders/ideogram4.py | 10 +- 3 files changed, 218 insertions(+), 225 deletions(-) diff --git a/invokeai/app/invocations/primitives.py b/invokeai/app/invocations/primitives.py index a6a054c3a2b..7b580b21d4d 100644 --- a/invokeai/app/invocations/primitives.py +++ b/invokeai/app/invocations/primitives.py @@ -20,6 +20,7 @@ DenoiseMaskField, FieldDescriptions, FluxConditioningField, + Ideogram4ConditioningField, ImageField, Input, InputField, @@ -29,7 +30,6 @@ SD3ConditioningField, TensorField, UIComponent, - Ideogram4ConditioningField, ZImageConditioningField, ) from invokeai.app.services.images.images_common import ImageDTO diff --git a/invokeai/backend/ideogram4/quantized_loading.py b/invokeai/backend/ideogram4/quantized_loading.py index 99d1ce26094..7440de043bf 100644 --- a/invokeai/backend/ideogram4/quantized_loading.py +++ b/invokeai/backend/ideogram4/quantized_loading.py @@ -7,12 +7,11 @@ import torch.nn as nn import torch.nn.functional as F - _BNB_SIBLING_SUFFIXES = ( - ".absmax", - ".quant_map", - ".nested_absmax", - ".nested_quant_map", + ".absmax", + ".quant_map", + ".nested_absmax", + ".nested_quant_map", ) # Largest magnitude representable by the e4m3 float8 format. Per-row weight @@ -26,105 +25,105 @@ def is_bnb4bit_state_dict(state_dict: dict[str, torch.Tensor]) -> bool: - """True if any key looks like a bnb 4-bit quant_state sibling.""" - return any(".quant_state.bitsandbytes__" in k for k in state_dict) + """True if any key looks like a bnb 4-bit quant_state sibling.""" + return any(".quant_state.bitsandbytes__" in k for k in state_dict) def swap_linears_to_bnb4bit( - module: nn.Module, - compute_dtype: torch.dtype, - *, - quant_type: str = "nf4", - compress_statistics: bool = False, + module: nn.Module, + compute_dtype: torch.dtype, + *, + quant_type: str = "nf4", + compress_statistics: bool = False, ) -> None: - for name, child in list(module.named_children()): - if isinstance(child, nn.Linear): - new_linear = bnb.nn.Linear4bit( - child.in_features, - child.out_features, - bias=child.bias is not None, - compute_dtype=compute_dtype, - compress_statistics=compress_statistics, - quant_type=quant_type, - ) - setattr(module, name, new_linear) - else: - swap_linears_to_bnb4bit( - child, - compute_dtype, - quant_type=quant_type, - compress_statistics=compress_statistics, - ) + for name, child in list(module.named_children()): + if isinstance(child, nn.Linear): + new_linear = bnb.nn.Linear4bit( + child.in_features, + child.out_features, + bias=child.bias is not None, + compute_dtype=compute_dtype, + compress_statistics=compress_statistics, + quant_type=quant_type, + ) + setattr(module, name, new_linear) + else: + swap_linears_to_bnb4bit( + child, + compute_dtype, + quant_type=quant_type, + compress_statistics=compress_statistics, + ) def load_bnb4bit_state_dict( - model: nn.Module, - state_dict: dict[str, torch.Tensor], - device: torch.device, - dtype: torch.dtype, + model: nn.Module, + state_dict: dict[str, torch.Tensor], + device: torch.device, + dtype: torch.dtype, ) -> None: - consumed: set[str] = set() - for full_name, tensor in state_dict.items(): - if ".quant_state." in full_name or full_name.endswith(_BNB_SIBLING_SUFFIXES): - continue - parent_path, _, param_name = full_name.rpartition(".") - parent = model.get_submodule(parent_path) if parent_path else model - current = parent._parameters.get(param_name) - if not isinstance(current, bnb.nn.Params4bit): - continue - prefix = full_name + "." - quantized_stats = {k: v for k, v in state_dict.items() if k.startswith(prefix)} - # bnb's from_prequantized pops keys it consumes from the dict, so snapshot - # the names first. - consumed.add(full_name) - consumed.update(quantized_stats.keys()) - parent._parameters[param_name] = bnb.nn.Params4bit.from_prequantized( - data=tensor, - quantized_stats=quantized_stats, - requires_grad=False, - device=device, - ) - - remaining = {k: v for k, v in state_dict.items() if k not in consumed} - for k in list(remaining): - if remaining[k].is_floating_point(): - remaining[k] = remaining[k].to(device=device, dtype=dtype) - else: - remaining[k] = remaining[k].to(device=device) - - missing, unexpected = model.load_state_dict(remaining, strict=False) - # Quantized weights are loaded via from_prequantized above, so they appear in - # `missing` from load_state_dict's perspective — filter those out. - real_missing = [m for m in missing if m not in consumed] - if real_missing: - raise RuntimeError(f"missing keys after quantized load: {real_missing[:10]}") - if unexpected: - raise RuntimeError(f"unexpected keys after quantized load: {unexpected[:10]}") - - for p in model.parameters(): - if isinstance(p, bnb.nn.Params4bit): - continue - if p.is_floating_point() and p.dtype != dtype: - p.data = p.data.to(dtype=dtype) - if p.device != device: - p.data = p.data.to(device=device) - for name, b in list(model.named_buffers()): - if b.is_floating_point() and b.dtype != dtype: - parent_path, _, leaf = name.rpartition(".") - parent = model.get_submodule(parent_path) if parent_path else model - parent.register_buffer( - leaf, - b.to(device=device, dtype=dtype), - persistent=leaf not in parent._non_persistent_buffers_set, - ) - elif b.device != device: - parent_path, _, leaf = name.rpartition(".") - parent = model.get_submodule(parent_path) if parent_path else model - parent.register_buffer( - leaf, - b.to(device=device), - persistent=leaf not in parent._non_persistent_buffers_set, - ) + consumed: set[str] = set() + for full_name, tensor in state_dict.items(): + if ".quant_state." in full_name or full_name.endswith(_BNB_SIBLING_SUFFIXES): + continue + parent_path, _, param_name = full_name.rpartition(".") + parent = model.get_submodule(parent_path) if parent_path else model + current = parent._parameters.get(param_name) + if not isinstance(current, bnb.nn.Params4bit): + continue + prefix = full_name + "." + quantized_stats = {k: v for k, v in state_dict.items() if k.startswith(prefix)} + # bnb's from_prequantized pops keys it consumes from the dict, so snapshot + # the names first. + consumed.add(full_name) + consumed.update(quantized_stats.keys()) + parent._parameters[param_name] = bnb.nn.Params4bit.from_prequantized( + data=tensor, + quantized_stats=quantized_stats, + requires_grad=False, + device=device, + ) + + remaining = {k: v for k, v in state_dict.items() if k not in consumed} + for k in list(remaining): + if remaining[k].is_floating_point(): + remaining[k] = remaining[k].to(device=device, dtype=dtype) + else: + remaining[k] = remaining[k].to(device=device) + + missing, unexpected = model.load_state_dict(remaining, strict=False) + # Quantized weights are loaded via from_prequantized above, so they appear in + # `missing` from load_state_dict's perspective — filter those out. + real_missing = [m for m in missing if m not in consumed] + if real_missing: + raise RuntimeError(f"missing keys after quantized load: {real_missing[:10]}") + if unexpected: + raise RuntimeError(f"unexpected keys after quantized load: {unexpected[:10]}") + + for p in model.parameters(): + if isinstance(p, bnb.nn.Params4bit): + continue + if p.is_floating_point() and p.dtype != dtype: + p.data = p.data.to(dtype=dtype) + if p.device != device: + p.data = p.data.to(device=device) + for name, b in list(model.named_buffers()): + if b.is_floating_point() and b.dtype != dtype: + parent_path, _, leaf = name.rpartition(".") + parent = model.get_submodule(parent_path) if parent_path else model + parent.register_buffer( + leaf, + b.to(device=device, dtype=dtype), + persistent=leaf not in parent._non_persistent_buffers_set, + ) + elif b.device != device: + parent_path, _, leaf = name.rpartition(".") + parent = model.get_submodule(parent_path) if parent_path else model + parent.register_buffer( + leaf, + b.to(device=device), + persistent=leaf not in parent._non_persistent_buffers_set, + ) # --------------------------------------------------------------------------- @@ -139,140 +138,138 @@ def load_bnb4bit_state_dict( def quantize_weight_to_fp8( - weight: torch.Tensor, + weight: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor]: - """Quantize a 2-D Linear weight to e4m3 float8 with per-row scales. + """Quantize a 2-D Linear weight to e4m3 float8 with per-row scales. - Returns ``(weight_fp8, scale)`` where ``weight_fp8`` has shape ``(out, in)`` - in ``float8_e4m3fn`` and ``scale`` has shape ``(out,)`` in float32 such that - ``weight ≈ weight_fp8.to(dtype) * scale[:, None]``. - """ - w = weight.detach().to(torch.float32) - amax = w.abs().amax(dim=1, keepdim=True).clamp(min=1e-12) - scale = amax / FP8_E4M3_MAX - q = (w / scale).clamp(-FP8_E4M3_MAX, FP8_E4M3_MAX).to(FP8_WEIGHT_DTYPE) - return q, scale.squeeze(1).to(torch.float32) + Returns ``(weight_fp8, scale)`` where ``weight_fp8`` has shape ``(out, in)`` + in ``float8_e4m3fn`` and ``scale`` has shape ``(out,)`` in float32 such that + ``weight ≈ weight_fp8.to(dtype) * scale[:, None]``. + """ + w = weight.detach().to(torch.float32) + amax = w.abs().amax(dim=1, keepdim=True).clamp(min=1e-12) + scale = amax / FP8_E4M3_MAX + q = (w / scale).clamp(-FP8_E4M3_MAX, FP8_E4M3_MAX).to(FP8_WEIGHT_DTYPE) + return q, scale.squeeze(1).to(torch.float32) def is_fp8_state_dict(state_dict: dict[str, torch.Tensor]) -> bool: - """True if the checkpoint carries weight-only FP8 Linear weights.""" - return any(k.endswith(FP8_SCALE_SUFFIX) for k in state_dict) or any( - v.dtype == FP8_WEIGHT_DTYPE for v in state_dict.values() - ) + """True if the checkpoint carries weight-only FP8 Linear weights.""" + return any(k.endswith(FP8_SCALE_SUFFIX) for k in state_dict) or any( + v.dtype == FP8_WEIGHT_DTYPE for v in state_dict.values() + ) class Fp8Linear(nn.Module): - """Linear layer holding an e4m3 float8 weight + per-row float32 scale. - - The weight and scale are registered as buffers (not parameters) so they load - via ``load_state_dict`` and are excluded from optimizer/grad machinery. The - dequantized matmul runs in ``compute_dtype``. - """ - - weight: torch.Tensor - weight_scale: torch.Tensor - bias: torch.Tensor | None - - def __init__( - self, - in_features: int, - out_features: int, - bias: bool, - compute_dtype: torch.dtype, - ) -> None: - super().__init__() - self.in_features = in_features - self.out_features = out_features - self.compute_dtype = compute_dtype - self.register_buffer( - "weight", - torch.empty(out_features, in_features, dtype=FP8_WEIGHT_DTYPE), - ) - self.register_buffer("weight_scale", torch.empty(out_features, dtype=torch.float32)) - if bias: - self.register_buffer("bias", torch.empty(out_features, dtype=compute_dtype)) - else: - self.bias = None - - def forward(self, x: torch.Tensor) -> torch.Tensor: - w = self.weight.to(x.dtype) * self.weight_scale.to(x.dtype).unsqueeze(1) - bias = self.bias.to(x.dtype) if self.bias is not None else None - return F.linear(x, w, bias) + """Linear layer holding an e4m3 float8 weight + per-row float32 scale. + + The weight and scale are registered as buffers (not parameters) so they load + via ``load_state_dict`` and are excluded from optimizer/grad machinery. The + dequantized matmul runs in ``compute_dtype``. + """ + + weight: torch.Tensor + weight_scale: torch.Tensor + bias: torch.Tensor | None + + def __init__( + self, + in_features: int, + out_features: int, + bias: bool, + compute_dtype: torch.dtype, + ) -> None: + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.compute_dtype = compute_dtype + self.register_buffer( + "weight", + torch.empty(out_features, in_features, dtype=FP8_WEIGHT_DTYPE), + ) + self.register_buffer("weight_scale", torch.empty(out_features, dtype=torch.float32)) + if bias: + self.register_buffer("bias", torch.empty(out_features, dtype=compute_dtype)) + else: + self.bias = None + + def forward(self, x: torch.Tensor) -> torch.Tensor: + w = self.weight.to(x.dtype) * self.weight_scale.to(x.dtype).unsqueeze(1) + bias = self.bias.to(x.dtype) if self.bias is not None else None + return F.linear(x, w, bias) def swap_linears_to_fp8( - module: nn.Module, - state_dict: dict[str, torch.Tensor], - compute_dtype: torch.dtype, - *, - prefix: str = "", + module: nn.Module, + state_dict: dict[str, torch.Tensor], + compute_dtype: torch.dtype, + *, + prefix: str = "", ) -> None: - """Replace each ``nn.Linear`` that has a saved FP8 scale with an ``Fp8Linear``. - - Gating on the presence of ``.weight_scale`` means only layers that were - actually quantized at save time are swapped; everything else loads normally in - the compute dtype. - """ - for name, child in list(module.named_children()): - child_prefix = f"{prefix}{name}" - if ( - isinstance(child, nn.Linear) and f"{child_prefix}{FP8_SCALE_SUFFIX}" in state_dict - ): - setattr( - module, - name, - Fp8Linear( - child.in_features, - child.out_features, - bias=child.bias is not None, - compute_dtype=compute_dtype, - ), - ) - else: - swap_linears_to_fp8(child, state_dict, compute_dtype, prefix=f"{child_prefix}.") + """Replace each ``nn.Linear`` that has a saved FP8 scale with an ``Fp8Linear``. + + Gating on the presence of ``.weight_scale`` means only layers that were + actually quantized at save time are swapped; everything else loads normally in + the compute dtype. + """ + for name, child in list(module.named_children()): + child_prefix = f"{prefix}{name}" + if isinstance(child, nn.Linear) and f"{child_prefix}{FP8_SCALE_SUFFIX}" in state_dict: + setattr( + module, + name, + Fp8Linear( + child.in_features, + child.out_features, + bias=child.bias is not None, + compute_dtype=compute_dtype, + ), + ) + else: + swap_linears_to_fp8(child, state_dict, compute_dtype, prefix=f"{child_prefix}.") def load_fp8_state_dict( - model: nn.Module, - state_dict: dict[str, torch.Tensor], - device: torch.device, - dtype: torch.dtype, - *, - assign: bool = False, - strict: bool = True, + model: nn.Module, + state_dict: dict[str, torch.Tensor], + device: torch.device, + dtype: torch.dtype, + *, + assign: bool = False, + strict: bool = True, ) -> None: - """Load a weight-only FP8 checkpoint into ``model``. - - ``model`` must already have its FP8 Linear layers swapped in (see - ``swap_linears_to_fp8``). FP8 weights are kept as float8, scales stay float32, - and every other floating tensor is cast to ``dtype``. - - ``assign=True`` replaces the module's tensors with the prepared ones rather than - copying into them. Use it when the model was built with ``from_config`` so the - non-quantized params take the loaded dtype directly and computed non-persistent - buffers (e.g. rotary caches) are left untouched. With ``assign=False`` (default), - the caller must have already put the unquantized params in ``dtype``. - - ``strict=False`` downgrades missing keys to a warning (e.g. tied weights that a - ``transformers`` model resolves itself); unexpected keys always raise. - """ - prepared: dict[str, torch.Tensor] = {} - for k, v in state_dict.items(): - if v.dtype == FP8_WEIGHT_DTYPE: - prepared[k] = v.to(device=device) - elif k.endswith(FP8_SCALE_SUFFIX): - prepared[k] = v.to(device=device, dtype=torch.float32) - elif v.is_floating_point(): - prepared[k] = v.to(device=device, dtype=dtype) - else: - prepared[k] = v.to(device=device) - - missing, unexpected = model.load_state_dict(prepared, strict=False, assign=assign) - if unexpected: - raise RuntimeError(f"unexpected keys after fp8 load: {unexpected[:10]}") - if missing: - if strict: - raise RuntimeError(f"missing keys after fp8 load: {missing[:10]}") - warnings.warn(f"missing keys after fp8 load: {missing[:10]}", stacklevel=2) - - model.to(device) + """Load a weight-only FP8 checkpoint into ``model``. + + ``model`` must already have its FP8 Linear layers swapped in (see + ``swap_linears_to_fp8``). FP8 weights are kept as float8, scales stay float32, + and every other floating tensor is cast to ``dtype``. + + ``assign=True`` replaces the module's tensors with the prepared ones rather than + copying into them. Use it when the model was built with ``from_config`` so the + non-quantized params take the loaded dtype directly and computed non-persistent + buffers (e.g. rotary caches) are left untouched. With ``assign=False`` (default), + the caller must have already put the unquantized params in ``dtype``. + + ``strict=False`` downgrades missing keys to a warning (e.g. tied weights that a + ``transformers`` model resolves itself); unexpected keys always raise. + """ + prepared: dict[str, torch.Tensor] = {} + for k, v in state_dict.items(): + if v.dtype == FP8_WEIGHT_DTYPE: + prepared[k] = v.to(device=device) + elif k.endswith(FP8_SCALE_SUFFIX): + prepared[k] = v.to(device=device, dtype=torch.float32) + elif v.is_floating_point(): + prepared[k] = v.to(device=device, dtype=dtype) + else: + prepared[k] = v.to(device=device) + + missing, unexpected = model.load_state_dict(prepared, strict=False, assign=assign) + if unexpected: + raise RuntimeError(f"unexpected keys after fp8 load: {unexpected[:10]}") + if missing: + if strict: + raise RuntimeError(f"missing keys after fp8 load: {missing[:10]}") + warnings.warn(f"missing keys after fp8 load: {missing[:10]}", stacklevel=2) + + model.to(device) diff --git a/invokeai/backend/model_manager/load/model_loaders/ideogram4.py b/invokeai/backend/model_manager/load/model_loaders/ideogram4.py index 6c7401c66bd..c02bea2dc3f 100644 --- a/invokeai/backend/model_manager/load/model_loaders/ideogram4.py +++ b/invokeai/backend/model_manager/load/model_loaders/ideogram4.py @@ -13,7 +13,7 @@ import json from pathlib import Path -from typing import Any, Optional +from typing import Optional import accelerate import torch @@ -164,15 +164,11 @@ def _load_text_encoder(self, model_path: Path) -> AnyModel: with accelerate.init_empty_weights(): model: torch.nn.Module = AutoModel.from_config(cfg) swap_linears_to_fp8(model, sd, compute_dtype=compute_dtype) - load_fp8_state_dict( - model, sd, device=torch.device("cpu"), dtype=compute_dtype, assign=True, strict=False - ) + load_fp8_state_dict(model, sd, device=torch.device("cpu"), dtype=compute_dtype, assign=True, strict=False) model.eval() return model - is_bnb_nf4 = "quantization_config" in raw_cfg and bool( - raw_cfg["quantization_config"].get("load_in_4bit") - ) + is_bnb_nf4 = "quantization_config" in raw_cfg and bool(raw_cfg["quantization_config"].get("load_in_4bit")) with accelerate.init_empty_weights(): model = AutoModel.from_config(cfg) From 0d6af8fa212ca578f45543da1ce0ef917cc9f0cf Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Thu, 16 Jul 2026 05:03:42 +0200 Subject: [PATCH 19/33] Chore Ruff --- invokeai/app/invocations/ideogram4_denoise.py | 3 +- .../invocations/ideogram4_latents_to_image.py | 8 +- invokeai/backend/ideogram4/autoencoder.py | 682 +++++++++--------- invokeai/backend/ideogram4/denoise.py | 8 +- invokeai/backend/ideogram4/latent_norm.py | 520 ++++++------- .../backend/ideogram4/modeling_ideogram4.py | 614 ++++++++-------- invokeai/backend/ideogram4/sampler_configs.py | 36 +- invokeai/backend/ideogram4/sampling_utils.py | 4 +- invokeai/backend/ideogram4/scheduler.py | 93 ++- invokeai/backend/ideogram4/text_encoding.py | 4 +- 10 files changed, 960 insertions(+), 1012 deletions(-) diff --git a/invokeai/app/invocations/ideogram4_denoise.py b/invokeai/app/invocations/ideogram4_denoise.py index aea9ec1a67c..d75ed09a026 100644 --- a/invokeai/app/invocations/ideogram4_denoise.py +++ b/invokeai/app/invocations/ideogram4_denoise.py @@ -86,8 +86,7 @@ class Ideogram4DenoiseInvocation(BaseInvocation): default=None, ge=-4.0, le=4.0, - description="Override the logit-normal schedule mean (resolution-adjusted internally). " - "Empty = use the preset.", + description="Override the logit-normal schedule mean (resolution-adjusted internally). Empty = use the preset.", ) @torch.no_grad() diff --git a/invokeai/app/invocations/ideogram4_latents_to_image.py b/invokeai/app/invocations/ideogram4_latents_to_image.py index 2b8120b6c1b..085f2c48e7a 100644 --- a/invokeai/app/invocations/ideogram4_latents_to_image.py +++ b/invokeai/app/invocations/ideogram4_latents_to_image.py @@ -44,16 +44,12 @@ def invoke(self, context: InvocationContext) -> ImageOutput: latent_shift, latent_scale = get_latent_norm() with vae_info.model_on_device() as (_, vae): - assert isinstance(vae, AutoEncoder), ( - f"Expected Ideogram 4 AutoEncoder, got {type(vae).__name__}." - ) + assert isinstance(vae, AutoEncoder), f"Expected Ideogram 4 AutoEncoder, got {type(vae).__name__}." context.util.signal_progress("Running VAE") vae_dtype = next(vae.parameters()).dtype # Denormalize + unpatchify to a standard (1, 32, H/8, W/8) latent. - z = unpatchify_and_denormalize( - latents.float().to(device), latent_shift.to(device), latent_scale.to(device) - ) + z = unpatchify_and_denormalize(latents.float().to(device), latent_shift.to(device), latent_scale.to(device)) TorchDevice.empty_cache() decoded = vae.decoder(z.to(vae_dtype)) diff --git a/invokeai/backend/ideogram4/autoencoder.py b/invokeai/backend/ideogram4/autoencoder.py index ba17ec939e2..c1f64e98961 100644 --- a/invokeai/backend/ideogram4/autoencoder.py +++ b/invokeai/backend/ideogram4/autoencoder.py @@ -13,398 +13,372 @@ @dataclass class AutoEncoderParams: - resolution: int = 256 - in_channels: int = 3 - ch: int = 128 - out_ch: int = 3 - ch_mult: list[int] = field(default_factory=lambda: [1, 2, 4, 4]) - num_res_blocks: int = 2 - z_channels: int = 32 + resolution: int = 256 + in_channels: int = 3 + ch: int = 128 + out_ch: int = 3 + ch_mult: list[int] = field(default_factory=lambda: [1, 2, 4, 4]) + num_res_blocks: int = 2 + z_channels: int = 32 def swish(x: Tensor) -> Tensor: - return x * torch.sigmoid(x) + return x * torch.sigmoid(x) class AttnBlock(nn.Module): - def __init__(self, in_channels: int): - super().__init__() - self.in_channels = in_channels + def __init__(self, in_channels: int): + super().__init__() + self.in_channels = in_channels - self.norm = nn.GroupNorm( - num_groups=32, num_channels=in_channels, eps=1e-6, affine=True - ) + self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) - self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1) - self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1) - self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1) - self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1) - def attention(self, h_: Tensor) -> Tensor: - h_ = self.norm(h_) - q = self.q(h_) - k = self.k(h_) - v = self.v(h_) + def attention(self, h_: Tensor) -> Tensor: + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) - b, c, h, w = q.shape - q = rearrange(q, "b c h w -> b 1 (h w) c").contiguous() - k = rearrange(k, "b c h w -> b 1 (h w) c").contiguous() - v = rearrange(v, "b c h w -> b 1 (h w) c").contiguous() - h_ = nn.functional.scaled_dot_product_attention(q, k, v) + b, c, h, w = q.shape + q = rearrange(q, "b c h w -> b 1 (h w) c").contiguous() + k = rearrange(k, "b c h w -> b 1 (h w) c").contiguous() + v = rearrange(v, "b c h w -> b 1 (h w) c").contiguous() + h_ = nn.functional.scaled_dot_product_attention(q, k, v) - return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b) + return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b) - def forward(self, x: Tensor) -> Tensor: - return x + self.proj_out(self.attention(x)) + def forward(self, x: Tensor) -> Tensor: + return x + self.proj_out(self.attention(x)) class ResnetBlock(nn.Module): - def __init__(self, in_channels: int, out_channels: int): - super().__init__() - self.in_channels = in_channels - out_channels = in_channels if out_channels is None else out_channels - self.out_channels = out_channels - - self.norm1 = nn.GroupNorm( - num_groups=32, num_channels=in_channels, eps=1e-6, affine=True - ) - self.conv1 = nn.Conv2d( - in_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - self.norm2 = nn.GroupNorm( - num_groups=32, num_channels=out_channels, eps=1e-6, affine=True - ) - self.conv2 = nn.Conv2d( - out_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - if self.in_channels != self.out_channels: - self.nin_shortcut = nn.Conv2d( - in_channels, out_channels, kernel_size=1, stride=1, padding=0 - ) - - def forward(self, x): - h = x - h = self.norm1(h) - h = swish(h) - h = self.conv1(h) - - h = self.norm2(h) - h = swish(h) - h = self.conv2(h) - - if self.in_channels != self.out_channels: - x = self.nin_shortcut(x) - - return x + h + def __init__(self, in_channels: int, out_channels: int): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + + self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + if self.in_channels != self.out_channels: + self.nin_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, x): + h = x + h = self.norm1(h) + h = swish(h) + h = self.conv1(h) + + h = self.norm2(h) + h = swish(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + x = self.nin_shortcut(x) + + return x + h class Downsample(nn.Module): - def __init__(self, in_channels: int): - super().__init__() - # no asymmetric padding in torch conv, must do it ourselves - self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) + def __init__(self, in_channels: int): + super().__init__() + # no asymmetric padding in torch conv, must do it ourselves + self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) - def forward(self, x: Tensor): - pad = (0, 1, 0, 1) - x = nn.functional.pad(x, pad, mode="constant", value=0) - x = self.conv(x) - return x + def forward(self, x: Tensor): + pad = (0, 1, 0, 1) + x = nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + return x class Upsample(nn.Module): - def __init__(self, in_channels: int): - super().__init__() - self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) + def __init__(self, in_channels: int): + super().__init__() + self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) - def forward(self, x: Tensor): - x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") - x = self.conv(x) - return x + def forward(self, x: Tensor): + x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + x = self.conv(x) + return x class Encoder(nn.Module): - def __init__( - self, - resolution: int, - in_channels: int, - ch: int, - ch_mult: list[int], - num_res_blocks: int, - z_channels: int, - ): - super().__init__() - self.quant_conv = torch.nn.Conv2d(2 * z_channels, 2 * z_channels, 1) - self.ch = ch - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.in_channels = in_channels - # downsampling - self.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) - - curr_res = resolution - in_ch_mult = (1,) + tuple(ch_mult) - self.in_ch_mult = in_ch_mult - self.down = nn.ModuleList() - block_in = self.ch - for i_level in range(self.num_resolutions): - block = nn.ModuleList() - attn = nn.ModuleList() - block_in = ch * in_ch_mult[i_level] - block_out = ch * ch_mult[i_level] - for _ in range(self.num_res_blocks): - block.append(ResnetBlock(in_channels=block_in, out_channels=block_out)) - block_in = block_out - down = nn.Module() - down.block = block - down.attn = attn - if i_level != self.num_resolutions - 1: - down.downsample = Downsample(block_in) - curr_res = curr_res // 2 - self.down.append(down) - - # middle - self.mid = nn.Module() - self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in) - self.mid.attn_1 = AttnBlock(block_in) - self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in) - - # end - self.norm_out = nn.GroupNorm( - num_groups=32, num_channels=block_in, eps=1e-6, affine=True - ) - self.conv_out = nn.Conv2d( - block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1 - ) - - def forward(self, x: Tensor) -> Tensor: - # downsampling - hs = [self.conv_in(x)] - for i_level in range(self.num_resolutions): - for i_block in range(self.num_res_blocks): - h = self.down[i_level].block[i_block](hs[-1]) # type: ignore[index, operator] - if len(self.down[i_level].attn) > 0: # type: ignore[arg-type] - h = self.down[i_level].attn[i_block](h) # type: ignore[index, operator] - hs.append(h) - if i_level != self.num_resolutions - 1: - hs.append(self.down[i_level].downsample(hs[-1])) # type: ignore[operator] - - # middle - h = hs[-1] - h = self.mid.block_1(h) # type: ignore[operator] - h = self.mid.attn_1(h) # type: ignore[operator] - h = self.mid.block_2(h) # type: ignore[operator] - # end - h = self.norm_out(h) - h = swish(h) - h = self.conv_out(h) - h = self.quant_conv(h) - return h + def __init__( + self, + resolution: int, + in_channels: int, + ch: int, + ch_mult: list[int], + num_res_blocks: int, + z_channels: int, + ): + super().__init__() + self.quant_conv = torch.nn.Conv2d(2 * z_channels, 2 * z_channels, 1) + self.ch = ch + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + # downsampling + self.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + block_in = self.ch + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for _ in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out)) + block_in = block_out + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in) + + # end + self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True) + self.conv_out = nn.Conv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x: Tensor) -> Tensor: + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1]) # type: ignore[index, operator] + if len(self.down[i_level].attn) > 0: # type: ignore[arg-type] + h = self.down[i_level].attn[i_block](h) # type: ignore[index, operator] + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) # type: ignore[operator] + + # middle + h = hs[-1] + h = self.mid.block_1(h) # type: ignore[operator] + h = self.mid.attn_1(h) # type: ignore[operator] + h = self.mid.block_2(h) # type: ignore[operator] + # end + h = self.norm_out(h) + h = swish(h) + h = self.conv_out(h) + h = self.quant_conv(h) + return h class Decoder(nn.Module): - def __init__( - self, - ch: int, - out_ch: int, - ch_mult: list[int], - num_res_blocks: int, - in_channels: int, - resolution: int, - z_channels: int, - ): - super().__init__() - self.post_quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1) - self.ch = ch - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.in_channels = in_channels - self.ffactor = 2 ** (self.num_resolutions - 1) - - # compute in_ch_mult, block_in and curr_res at lowest res - block_in = ch * ch_mult[self.num_resolutions - 1] - curr_res = resolution // 2 ** (self.num_resolutions - 1) - self.z_shape = (1, z_channels, curr_res, curr_res) - - # z to block_in - self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) - - # middle - self.mid = nn.Module() - self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in) - self.mid.attn_1 = AttnBlock(block_in) - self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in) - - # upsampling - self.up = nn.ModuleList() - for i_level in reversed(range(self.num_resolutions)): - block = nn.ModuleList() - attn = nn.ModuleList() - block_out = ch * ch_mult[i_level] - for _ in range(self.num_res_blocks + 1): - block.append(ResnetBlock(in_channels=block_in, out_channels=block_out)) - block_in = block_out - up = nn.Module() - up.block = block - up.attn = attn - if i_level != 0: - up.upsample = Upsample(block_in) - curr_res = curr_res * 2 - self.up.insert(0, up) # prepend to get consistent order - - # end - self.norm_out = nn.GroupNorm( - num_groups=32, num_channels=block_in, eps=1e-6, affine=True - ) - self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) - - def forward(self, z: Tensor) -> Tensor: - z = self.post_quant_conv(z) - - # get dtype for proper tracing - upscale_dtype = next(self.up.parameters()).dtype - - # z to block_in - h = self.conv_in(z) - - # middle - h = self.mid.block_1(h) # type: ignore[operator] - h = self.mid.attn_1(h) # type: ignore[operator] - h = self.mid.block_2(h) # type: ignore[operator] - - # cast to proper dtype - h = h.to(upscale_dtype) - # upsampling - for i_level in reversed(range(self.num_resolutions)): - for i_block in range(self.num_res_blocks + 1): - h = self.up[i_level].block[i_block](h) # type: ignore[index, operator] - if len(self.up[i_level].attn) > 0: # type: ignore[arg-type] - h = self.up[i_level].attn[i_block](h) # type: ignore[index, operator] - if i_level != 0: - h = self.up[i_level].upsample(h) # type: ignore[operator] - - # end - h = self.norm_out(h) - h = swish(h) - h = self.conv_out(h) - return h + def __init__( + self, + ch: int, + out_ch: int, + ch_mult: list[int], + num_res_blocks: int, + in_channels: int, + resolution: int, + z_channels: int, + ): + super().__init__() + self.post_quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1) + self.ch = ch + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.ffactor = 2 ** (self.num_resolutions - 1) + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + + # z to block_in + self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for _ in range(self.num_res_blocks + 1): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out)) + block_in = block_out + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True) + self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) + + def forward(self, z: Tensor) -> Tensor: + z = self.post_quant_conv(z) + + # get dtype for proper tracing + upscale_dtype = next(self.up.parameters()).dtype + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h) # type: ignore[operator] + h = self.mid.attn_1(h) # type: ignore[operator] + h = self.mid.block_2(h) # type: ignore[operator] + + # cast to proper dtype + h = h.to(upscale_dtype) + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h) # type: ignore[index, operator] + if len(self.up[i_level].attn) > 0: # type: ignore[arg-type] + h = self.up[i_level].attn[i_block](h) # type: ignore[index, operator] + if i_level != 0: + h = self.up[i_level].upsample(h) # type: ignore[operator] + + # end + h = self.norm_out(h) + h = swish(h) + h = self.conv_out(h) + return h class AutoEncoder(nn.Module): - def __init__(self, params: AutoEncoderParams): - super().__init__() - self.params = params - self.encoder = Encoder( - resolution=params.resolution, - in_channels=params.in_channels, - ch=params.ch, - ch_mult=params.ch_mult, - num_res_blocks=params.num_res_blocks, - z_channels=params.z_channels, - ) - self.decoder = Decoder( - resolution=params.resolution, - in_channels=params.in_channels, - ch=params.ch, - out_ch=params.out_ch, - ch_mult=params.ch_mult, - num_res_blocks=params.num_res_blocks, - z_channels=params.z_channels, - ) - - self.bn_eps = 1e-4 - self.bn_momentum = 0.1 - self.ps = [2, 2] - self.bn = torch.nn.BatchNorm2d( - math.prod(self.ps) * params.z_channels, - eps=self.bn_eps, - momentum=self.bn_momentum, - affine=False, - track_running_stats=True, - ) + def __init__(self, params: AutoEncoderParams): + super().__init__() + self.params = params + self.encoder = Encoder( + resolution=params.resolution, + in_channels=params.in_channels, + ch=params.ch, + ch_mult=params.ch_mult, + num_res_blocks=params.num_res_blocks, + z_channels=params.z_channels, + ) + self.decoder = Decoder( + resolution=params.resolution, + in_channels=params.in_channels, + ch=params.ch, + out_ch=params.out_ch, + ch_mult=params.ch_mult, + num_res_blocks=params.num_res_blocks, + z_channels=params.z_channels, + ) + + self.bn_eps = 1e-4 + self.bn_momentum = 0.1 + self.ps = [2, 2] + self.bn = torch.nn.BatchNorm2d( + math.prod(self.ps) * params.z_channels, + eps=self.bn_eps, + momentum=self.bn_momentum, + affine=False, + track_running_stats=True, + ) _NUM_RESOLUTIONS = 4 def convert_diffusers_state_dict(src: dict[str, Tensor]) -> dict[str, Tensor]: - out: dict[str, Tensor] = {} - attn_substrings = (".mid.attn_1.",) - for src_key, tensor in src.items(): - dst_key = _rewrite_diffusers_key(src_key) - if dst_key is None: - raise KeyError(f"Unrecognized diffusers VAE state-dict key: {src_key}") - if ( - any(s in dst_key for s in attn_substrings) - and dst_key.endswith(".weight") - and tensor.ndim == 2 - ): - tensor = tensor.unsqueeze(-1).unsqueeze(-1) - out[dst_key] = tensor - return out + out: dict[str, Tensor] = {} + attn_substrings = (".mid.attn_1.",) + for src_key, tensor in src.items(): + dst_key = _rewrite_diffusers_key(src_key) + if dst_key is None: + raise KeyError(f"Unrecognized diffusers VAE state-dict key: {src_key}") + if any(s in dst_key for s in attn_substrings) and dst_key.endswith(".weight") and tensor.ndim == 2: + tensor = tensor.unsqueeze(-1).unsqueeze(-1) + out[dst_key] = tensor + return out def _rewrite_diffusers_key(key: str) -> str | None: - if key.startswith("bn."): - return key - - if key.startswith("quant_conv."): - return key.replace("quant_conv.", "encoder.quant_conv.", 1) - if key.startswith("post_quant_conv."): - return key.replace("post_quant_conv.", "decoder.post_quant_conv.", 1) - - if key == "encoder.conv_norm_out.weight": - return "encoder.norm_out.weight" - if key == "encoder.conv_norm_out.bias": - return "encoder.norm_out.bias" - if key == "decoder.conv_norm_out.weight": - return "decoder.norm_out.weight" - if key == "decoder.conv_norm_out.bias": - return "decoder.norm_out.bias" - - m = re.match(r"^(encoder|decoder)\.mid_block\.resnets\.(\d+)\.(.+)$", key) - if m: - side, idx, rest = m.group(1), int(m.group(2)), m.group(3) - rest = rest.replace("conv_shortcut", "nin_shortcut") - return f"{side}.mid.block_{idx + 1}.{rest}" - m = re.match(r"^(encoder|decoder)\.mid_block\.attentions\.0\.(.+)$", key) - if m: - side, rest = m.group(1), m.group(2) - rest = ( - rest.replace("group_norm.", "norm.") - .replace("to_q.", "q.") - .replace("to_k.", "k.") - .replace("to_v.", "v.") - .replace("to_out.0.", "proj_out.") - ) - return f"{side}.mid.attn_1.{rest}" - - m = re.match(r"^encoder\.down_blocks\.(\d+)\.resnets\.(\d+)\.(.+)$", key) - if m: - level, res_idx, rest = m.group(1), m.group(2), m.group(3) - rest = rest.replace("conv_shortcut", "nin_shortcut") - return f"encoder.down.{level}.block.{res_idx}.{rest}" - m = re.match(r"^encoder\.down_blocks\.(\d+)\.downsamplers\.0\.conv\.(.+)$", key) - if m: - return f"encoder.down.{m.group(1)}.downsample.conv.{m.group(2)}" - - m = re.match(r"^decoder\.up_blocks\.(\d+)\.resnets\.(\d+)\.(.+)$", key) - if m: - diffusers_idx = int(m.group(1)) - res_idx = m.group(2) - rest = m.group(3).replace("conv_shortcut", "nin_shortcut") - return f"decoder.up.{_NUM_RESOLUTIONS - 1 - diffusers_idx}.block.{res_idx}.{rest}" - m = re.match(r"^decoder\.up_blocks\.(\d+)\.upsamplers\.0\.conv\.(.+)$", key) - if m: - diffusers_idx = int(m.group(1)) - return ( - f"decoder.up.{_NUM_RESOLUTIONS - 1 - diffusers_idx}.upsample.conv.{m.group(2)}" - ) - - if key.startswith( - ("encoder.conv_in.", "encoder.conv_out.", "decoder.conv_in.", "decoder.conv_out.") - ): - return key - - return None + if key.startswith("bn."): + return key + + if key.startswith("quant_conv."): + return key.replace("quant_conv.", "encoder.quant_conv.", 1) + if key.startswith("post_quant_conv."): + return key.replace("post_quant_conv.", "decoder.post_quant_conv.", 1) + + if key == "encoder.conv_norm_out.weight": + return "encoder.norm_out.weight" + if key == "encoder.conv_norm_out.bias": + return "encoder.norm_out.bias" + if key == "decoder.conv_norm_out.weight": + return "decoder.norm_out.weight" + if key == "decoder.conv_norm_out.bias": + return "decoder.norm_out.bias" + + m = re.match(r"^(encoder|decoder)\.mid_block\.resnets\.(\d+)\.(.+)$", key) + if m: + side, idx, rest = m.group(1), int(m.group(2)), m.group(3) + rest = rest.replace("conv_shortcut", "nin_shortcut") + return f"{side}.mid.block_{idx + 1}.{rest}" + m = re.match(r"^(encoder|decoder)\.mid_block\.attentions\.0\.(.+)$", key) + if m: + side, rest = m.group(1), m.group(2) + rest = ( + rest.replace("group_norm.", "norm.") + .replace("to_q.", "q.") + .replace("to_k.", "k.") + .replace("to_v.", "v.") + .replace("to_out.0.", "proj_out.") + ) + return f"{side}.mid.attn_1.{rest}" + + m = re.match(r"^encoder\.down_blocks\.(\d+)\.resnets\.(\d+)\.(.+)$", key) + if m: + level, res_idx, rest = m.group(1), m.group(2), m.group(3) + rest = rest.replace("conv_shortcut", "nin_shortcut") + return f"encoder.down.{level}.block.{res_idx}.{rest}" + m = re.match(r"^encoder\.down_blocks\.(\d+)\.downsamplers\.0\.conv\.(.+)$", key) + if m: + return f"encoder.down.{m.group(1)}.downsample.conv.{m.group(2)}" + + m = re.match(r"^decoder\.up_blocks\.(\d+)\.resnets\.(\d+)\.(.+)$", key) + if m: + diffusers_idx = int(m.group(1)) + res_idx = m.group(2) + rest = m.group(3).replace("conv_shortcut", "nin_shortcut") + return f"decoder.up.{_NUM_RESOLUTIONS - 1 - diffusers_idx}.block.{res_idx}.{rest}" + m = re.match(r"^decoder\.up_blocks\.(\d+)\.upsamplers\.0\.conv\.(.+)$", key) + if m: + diffusers_idx = int(m.group(1)) + return f"decoder.up.{_NUM_RESOLUTIONS - 1 - diffusers_idx}.upsample.conv.{m.group(2)}" + + if key.startswith(("encoder.conv_in.", "encoder.conv_out.", "decoder.conv_in.", "decoder.conv_out.")): + return key + + return None diff --git a/invokeai/backend/ideogram4/denoise.py b/invokeai/backend/ideogram4/denoise.py index c39ab4ed692..3d3f5ee8d3c 100644 --- a/invokeai/backend/ideogram4/denoise.py +++ b/invokeai/backend/ideogram4/denoise.py @@ -66,9 +66,7 @@ def run_ideogram4_denoise( if guidance_schedule is not None: gw_per_step = torch.as_tensor(guidance_schedule, dtype=torch.float32, device=device) if gw_per_step.shape != (num_steps,): - raise ValueError( - f"guidance_schedule must have length {num_steps}, got {tuple(gw_per_step.shape)}" - ) + raise ValueError(f"guidance_schedule must have length {num_steps}, got {tuple(gw_per_step.shape)}") else: gw_per_step = torch.full((num_steps,), float(guidance_scale), dtype=torch.float32, device=device) @@ -87,9 +85,7 @@ def run_ideogram4_denoise( generator = torch.Generator(device=device) if seed is not None: generator.manual_seed(seed) - z = torch.randn( - 1, num_image_tokens, LATENT_DIM, dtype=torch.float32, device=device, generator=generator - ) + z = torch.randn(1, num_image_tokens, LATENT_DIM, dtype=torch.float32, device=device, generator=generator) text_z_padding = torch.zeros(1, num_text_tokens, LATENT_DIM, dtype=torch.float32, device=device) for i in range(num_steps - 1, -1, -1): diff --git a/invokeai/backend/ideogram4/latent_norm.py b/invokeai/backend/ideogram4/latent_norm.py index eb013e9926a..225c4ed5049 100644 --- a/invokeai/backend/ideogram4/latent_norm.py +++ b/invokeai/backend/ideogram4/latent_norm.py @@ -3,270 +3,270 @@ import torch LATENT_SHIFT: tuple[float, ...] = ( - 0.01984364, - 0.10149707, - 0.29689495, - 0.27188619, - -0.21445648, - -0.15979549, - 0.05021099, - -0.15083604, - -0.15360136, - -0.20131799, - 0.01922352, - 0.0622626, - 0.10140969, - -0.06739428, - 0.3758261, - -0.233712, - 0.35164491, - -0.02590912, - -0.0271935, - -0.10833897, - -0.1476848, - -0.01130957, - -0.2298372, - 0.23526423, - -0.10893522, - 0.11957631, - 0.04047799, - 0.3134589, - -0.17225064, - -0.18646109, - -0.34691978, - -0.03571246, - 0.02583857, - 0.10190072, - 0.28402294, - 0.26952152, - -0.21634675, - -0.17938656, - 0.04358909, - -0.15007621, - -0.1548502, - -0.18971131, - 0.02710861, - 0.05609494, - 0.10697846, - -0.06854968, - 0.38167698, - -0.24269937, - 0.35705471, - -0.03063305, - -0.02946109, - -0.11244286, - -0.14336038, - -0.01362137, - -0.21863696, - 0.23228983, - -0.11739769, - 0.11693044, - 0.02563311, - 0.31356594, - -0.17420591, - -0.19006285, - -0.34905377, - -0.04025005, - 0.01924137, - 0.07652984, - 0.2995608, - 0.2628057, - -0.22011674, - -0.12715361, - 0.04879879, - -0.14075719, - -0.15935895, - -0.2123584, - 0.01974813, - 0.05523547, - 0.10011992, - -0.06428964, - 0.37781868, - -0.21491644, - 0.34254215, - -0.03153528, - -0.0310082, - -0.10761415, - -0.14730405, - -0.02475182, - -0.2285588, - 0.2515081, - -0.10445128, - 0.12446, - 0.07062869, - 0.30880162, - -0.18016875, - -0.18869164, - -0.34533499, - -0.0129177, - 0.02578168, - 0.07993659, - 0.28642181, - 0.26038408, - -0.22459419, - -0.14820155, - 0.04059549, - -0.14043529, - -0.16111187, - -0.2020305, - 0.02602069, - 0.04852717, - 0.10432153, - -0.06309942, - 0.38402443, - -0.22397003, - 0.34814481, - -0.03774432, - -0.03381438, - -0.11245691, - -0.14128767, - -0.02853208, - -0.21752016, - 0.24872463, - -0.11399775, - 0.1222687, - 0.05620835, - 0.309178, - -0.18065738, - -0.19401479, - -0.34495114, - -0.01760592, + 0.01984364, + 0.10149707, + 0.29689495, + 0.27188619, + -0.21445648, + -0.15979549, + 0.05021099, + -0.15083604, + -0.15360136, + -0.20131799, + 0.01922352, + 0.0622626, + 0.10140969, + -0.06739428, + 0.3758261, + -0.233712, + 0.35164491, + -0.02590912, + -0.0271935, + -0.10833897, + -0.1476848, + -0.01130957, + -0.2298372, + 0.23526423, + -0.10893522, + 0.11957631, + 0.04047799, + 0.3134589, + -0.17225064, + -0.18646109, + -0.34691978, + -0.03571246, + 0.02583857, + 0.10190072, + 0.28402294, + 0.26952152, + -0.21634675, + -0.17938656, + 0.04358909, + -0.15007621, + -0.1548502, + -0.18971131, + 0.02710861, + 0.05609494, + 0.10697846, + -0.06854968, + 0.38167698, + -0.24269937, + 0.35705471, + -0.03063305, + -0.02946109, + -0.11244286, + -0.14336038, + -0.01362137, + -0.21863696, + 0.23228983, + -0.11739769, + 0.11693044, + 0.02563311, + 0.31356594, + -0.17420591, + -0.19006285, + -0.34905377, + -0.04025005, + 0.01924137, + 0.07652984, + 0.2995608, + 0.2628057, + -0.22011674, + -0.12715361, + 0.04879879, + -0.14075719, + -0.15935895, + -0.2123584, + 0.01974813, + 0.05523547, + 0.10011992, + -0.06428964, + 0.37781868, + -0.21491644, + 0.34254215, + -0.03153528, + -0.0310082, + -0.10761415, + -0.14730405, + -0.02475182, + -0.2285588, + 0.2515081, + -0.10445128, + 0.12446, + 0.07062869, + 0.30880162, + -0.18016875, + -0.18869164, + -0.34533499, + -0.0129177, + 0.02578168, + 0.07993659, + 0.28642181, + 0.26038408, + -0.22459419, + -0.14820155, + 0.04059549, + -0.14043529, + -0.16111187, + -0.2020305, + 0.02602069, + 0.04852717, + 0.10432153, + -0.06309942, + 0.38402443, + -0.22397003, + 0.34814481, + -0.03774432, + -0.03381438, + -0.11245691, + -0.14128767, + -0.02853208, + -0.21752016, + 0.24872463, + -0.11399775, + 0.1222687, + 0.05620835, + 0.309178, + -0.18065738, + -0.19401479, + -0.34495114, + -0.01760592, ) LATENT_SCALE: tuple[float, ...] = ( - 1.63933691, - 1.70204478, - 1.73642566, - 1.90004803, - 1.6675316, - 1.69059584, - 1.56853198, - 1.62314944, - 1.89106626, - 1.58086668, - 1.60822129, - 1.60962993, - 1.63322129, - 1.56074359, - 1.73419528, - 1.7919265, - 1.64040632, - 1.66802808, - 1.60390303, - 1.75480492, - 1.63187587, - 1.64334594, - 1.61722884, - 1.60146046, - 1.63459219, - 1.55291476, - 1.68771497, - 1.68415657, - 1.78966054, - 1.66631641, - 1.65626686, - 1.65976433, - 1.63487607, - 1.69513249, - 1.72933756, - 1.91310663, - 1.67035057, - 1.72286863, - 1.56719251, - 1.61934825, - 1.88628859, - 1.56911539, - 1.59455129, - 1.60829869, - 1.62470611, - 1.56052853, - 1.73677003, - 1.77563606, - 1.63732541, - 1.66370527, - 1.59508952, - 1.75153949, - 1.63029275, - 1.64517667, - 1.61659342, - 1.59722044, - 1.64103121, - 1.5408531, - 1.68610394, - 1.67772755, - 1.78998563, - 1.66621713, - 1.65458955, - 1.66041308, - 1.64710857, - 1.68163503, - 1.74000294, - 1.92784786, - 1.67411194, - 1.67395548, - 1.57406532, - 1.62199356, - 1.87618195, - 1.5584375, - 1.57438785, - 1.61711053, - 1.63094305, - 1.55644029, - 1.73124302, - 1.80666627, - 1.6463621, - 1.65932006, - 1.60816188, - 1.75682671, - 1.64695873, - 1.63121722, - 1.61380832, - 1.60478651, - 1.63396035, - 1.53505068, - 1.65534289, - 1.67132281, - 1.80317197, - 1.6767314, - 1.65700938, - 1.68426259, - 1.65339716, - 1.67540638, - 1.73298504, - 1.94067348, - 1.67893609, - 1.70635117, - 1.5730906, - 1.61928553, - 1.87148809, - 1.56244866, - 1.56697152, - 1.61584394, - 1.62759496, - 1.55480378, - 1.73484107, - 1.79055143, - 1.64688773, - 1.66121492, - 1.60135887, - 1.75254572, - 1.64798332, - 1.62989921, - 1.61381592, - 1.60792883, - 1.63939668, - 1.53075757, - 1.65371318, - 1.66801185, - 1.80029087, - 1.67591476, - 1.65655173, - 1.68533454, + 1.63933691, + 1.70204478, + 1.73642566, + 1.90004803, + 1.6675316, + 1.69059584, + 1.56853198, + 1.62314944, + 1.89106626, + 1.58086668, + 1.60822129, + 1.60962993, + 1.63322129, + 1.56074359, + 1.73419528, + 1.7919265, + 1.64040632, + 1.66802808, + 1.60390303, + 1.75480492, + 1.63187587, + 1.64334594, + 1.61722884, + 1.60146046, + 1.63459219, + 1.55291476, + 1.68771497, + 1.68415657, + 1.78966054, + 1.66631641, + 1.65626686, + 1.65976433, + 1.63487607, + 1.69513249, + 1.72933756, + 1.91310663, + 1.67035057, + 1.72286863, + 1.56719251, + 1.61934825, + 1.88628859, + 1.56911539, + 1.59455129, + 1.60829869, + 1.62470611, + 1.56052853, + 1.73677003, + 1.77563606, + 1.63732541, + 1.66370527, + 1.59508952, + 1.75153949, + 1.63029275, + 1.64517667, + 1.61659342, + 1.59722044, + 1.64103121, + 1.5408531, + 1.68610394, + 1.67772755, + 1.78998563, + 1.66621713, + 1.65458955, + 1.66041308, + 1.64710857, + 1.68163503, + 1.74000294, + 1.92784786, + 1.67411194, + 1.67395548, + 1.57406532, + 1.62199356, + 1.87618195, + 1.5584375, + 1.57438785, + 1.61711053, + 1.63094305, + 1.55644029, + 1.73124302, + 1.80666627, + 1.6463621, + 1.65932006, + 1.60816188, + 1.75682671, + 1.64695873, + 1.63121722, + 1.61380832, + 1.60478651, + 1.63396035, + 1.53505068, + 1.65534289, + 1.67132281, + 1.80317197, + 1.6767314, + 1.65700938, + 1.68426259, + 1.65339716, + 1.67540638, + 1.73298504, + 1.94067348, + 1.67893609, + 1.70635117, + 1.5730906, + 1.61928553, + 1.87148809, + 1.56244866, + 1.56697152, + 1.61584394, + 1.62759496, + 1.55480378, + 1.73484107, + 1.79055143, + 1.64688773, + 1.66121492, + 1.60135887, + 1.75254572, + 1.64798332, + 1.62989921, + 1.61381592, + 1.60792883, + 1.63939668, + 1.53075757, + 1.65371318, + 1.66801185, + 1.80029087, + 1.67591476, + 1.65655173, + 1.68533454, ) def get_latent_norm() -> tuple[torch.Tensor, torch.Tensor]: - shift = torch.tensor(LATENT_SHIFT, dtype=torch.float32) - scale = torch.tensor(LATENT_SCALE, dtype=torch.float32) - assert shift.shape == (128,) and scale.shape == (128,) - return shift, scale + shift = torch.tensor(LATENT_SHIFT, dtype=torch.float32) + scale = torch.tensor(LATENT_SCALE, dtype=torch.float32) + assert shift.shape == (128,) and scale.shape == (128,) + return shift, scale diff --git a/invokeai/backend/ideogram4/modeling_ideogram4.py b/invokeai/backend/ideogram4/modeling_ideogram4.py index 11086fbd795..6ab11c659c6 100644 --- a/invokeai/backend/ideogram4/modeling_ideogram4.py +++ b/invokeai/backend/ideogram4/modeling_ideogram4.py @@ -14,366 +14,354 @@ import torch.nn.functional as F from invokeai.backend.ideogram4.constants import ( - LLM_TOKEN_INDICATOR, - OUTPUT_IMAGE_INDICATOR, - QWEN3_VL_ACTIVATION_LAYERS, + LLM_TOKEN_INDICATOR, + OUTPUT_IMAGE_INDICATOR, + QWEN3_VL_ACTIVATION_LAYERS, ) @dataclass class Ideogram4Config: - emb_dim: int = 4608 - num_layers: int = 34 - num_heads: int = 18 - intermediate_size: int = 12288 - adanln_dim: int = 512 + emb_dim: int = 4608 + num_layers: int = 34 + num_heads: int = 18 + intermediate_size: int = 12288 + adanln_dim: int = 512 - # Latent dimension after patchification: ae_channels (32) * patch_size**2 (4) = 128. - in_channels: int = 128 + # Latent dimension after patchification: ae_channels (32) * patch_size**2 (4) = 128. + in_channels: int = 128 - # Hidden size of Qwen3-VL-8B-Instruct multiplied by the number of layers we extract - # Qwen3-VL hidden size = 4096 - llm_features_dim: int = 4096 * len(QWEN3_VL_ACTIVATION_LAYERS) + # Hidden size of Qwen3-VL-8B-Instruct multiplied by the number of layers we extract + # Qwen3-VL hidden size = 4096 + llm_features_dim: int = 4096 * len(QWEN3_VL_ACTIVATION_LAYERS) - rope_theta: int = 5_000_000 - mrope_section: tuple[int, ...] = (24, 20, 20) + rope_theta: int = 5_000_000 + mrope_section: tuple[int, ...] = (24, 20, 20) - norm_eps: float = 1e-5 + norm_eps: float = 1e-5 def _rotate_half(x: torch.Tensor) -> torch.Tensor: - half = x.shape[-1] // 2 - x1 = x[..., :half] - x2 = x[..., half:] - return torch.cat((-x2, x1), dim=-1) + half = x.shape[-1] // 2 + x1 = x[..., :half] + x2 = x[..., half:] + return torch.cat((-x2, x1), dim=-1) def _apply_rotary_pos_emb( - q: torch.Tensor, - k: torch.Tensor, - cos: torch.Tensor, - sin: torch.Tensor, + q: torch.Tensor, + k: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor]: - # q, k: (B, num_heads, L, head_dim); cos/sin: (B, L, head_dim). - cos = cos.unsqueeze(1) - sin = sin.unsqueeze(1) - q_embed = (q * cos) + (_rotate_half(q) * sin) - k_embed = (k * cos) + (_rotate_half(k) * sin) - return q_embed, k_embed + # q, k: (B, num_heads, L, head_dim); cos/sin: (B, L, head_dim). + cos = cos.unsqueeze(1) + sin = sin.unsqueeze(1) + q_embed = (q * cos) + (_rotate_half(q) * sin) + k_embed = (k * cos) + (_rotate_half(k) * sin) + return q_embed, k_embed class Ideogram4MRoPE(nn.Module): - inv_freq: torch.Tensor - - def __init__( - self, - head_dim: int, - base: int, - mrope_section: tuple[int, ...], - ) -> None: - super().__init__() - inv_freq = 1.0 / ( - base ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim) - ) - self.register_buffer("inv_freq", inv_freq, persistent=False) - self.mrope_section = tuple(mrope_section) - self.head_dim = head_dim - - @torch.no_grad() - def forward(self, position_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: - # position_ids: (B, L, 3) of int. - assert position_ids.ndim == 3 and position_ids.shape[-1] == 3 - batch_size, seq_len, _ = position_ids.shape - - # (3, B, inv_freq_size, L) - pos = position_ids.permute(2, 0, 1).to(dtype=torch.float32) # type: ignore[arg-type] - inv_freq = self.inv_freq.to(dtype=torch.float32)[None, None, :, None].expand( - 3, batch_size, -1, 1 - ) # type: ignore[index] - freqs = inv_freq @ pos.unsqueeze(2) - freqs = freqs.transpose(2, 3) # (3, B, L, inv_freq_size) - - # interleaved mrope: pull H freqs into idx 1 mod 3, W freqs into idx 2 mod 3. - freqs_t = freqs[0].clone() - for axis, offset in ((1, 1), (2, 2)): - length = self.mrope_section[axis] * 3 - idx = torch.arange(offset, length, 3, device=freqs_t.device) - freqs_t[..., idx] = freqs[axis][..., idx] - - emb = torch.cat((freqs_t, freqs_t), dim=-1) - return emb.cos(), emb.sin() + inv_freq: torch.Tensor + + def __init__( + self, + head_dim: int, + base: int, + mrope_section: tuple[int, ...], + ) -> None: + super().__init__() + inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.mrope_section = tuple(mrope_section) + self.head_dim = head_dim + + @torch.no_grad() + def forward(self, position_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + # position_ids: (B, L, 3) of int. + assert position_ids.ndim == 3 and position_ids.shape[-1] == 3 + batch_size, seq_len, _ = position_ids.shape + + # (3, B, inv_freq_size, L) + pos = position_ids.permute(2, 0, 1).to(dtype=torch.float32) # type: ignore[arg-type] + inv_freq = self.inv_freq.to(dtype=torch.float32)[None, None, :, None].expand(3, batch_size, -1, 1) # type: ignore[index] + freqs = inv_freq @ pos.unsqueeze(2) + freqs = freqs.transpose(2, 3) # (3, B, L, inv_freq_size) + + # interleaved mrope: pull H freqs into idx 1 mod 3, W freqs into idx 2 mod 3. + freqs_t = freqs[0].clone() + for axis, offset in ((1, 1), (2, 2)): + length = self.mrope_section[axis] * 3 + idx = torch.arange(offset, length, 3, device=freqs_t.device) + freqs_t[..., idx] = freqs[axis][..., idx] + + emb = torch.cat((freqs_t, freqs_t), dim=-1) + return emb.cos(), emb.sin() class Ideogram4RMSNorm(nn.Module): - def __init__(self, dim: int, eps: float = 1e-6) -> None: - super().__init__() - self.weight = nn.Parameter(torch.ones(dim)) - self.eps = eps + def __init__(self, dim: int, eps: float = 1e-6) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(dim)) + self.eps = eps - def forward(self, x: torch.Tensor) -> torch.Tensor: - return F.rms_norm(x, self.weight.shape, self.weight, self.eps) + def forward(self, x: torch.Tensor) -> torch.Tensor: + return F.rms_norm(x, self.weight.shape, self.weight, self.eps) class Ideogram4Attention(nn.Module): - def __init__(self, hidden_size: int, num_heads: int, eps: float = 1e-5) -> None: - super().__init__() - assert hidden_size % num_heads == 0 - self.hidden_size = hidden_size - self.num_heads = num_heads - self.head_dim = hidden_size // num_heads - - self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=False) - self.norm_q = Ideogram4RMSNorm(self.head_dim, eps=eps) - self.norm_k = Ideogram4RMSNorm(self.head_dim, eps=eps) - self.o = nn.Linear(hidden_size, hidden_size, bias=False) - - def forward( - self, - x: torch.Tensor, - segment_ids: torch.Tensor, - cos: torch.Tensor, - sin: torch.Tensor, - ) -> torch.Tensor: - batch_size, seq_len, _ = x.shape + def __init__(self, hidden_size: int, num_heads: int, eps: float = 1e-5) -> None: + super().__init__() + assert hidden_size % num_heads == 0 + self.hidden_size = hidden_size + self.num_heads = num_heads + self.head_dim = hidden_size // num_heads + + self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=False) + self.norm_q = Ideogram4RMSNorm(self.head_dim, eps=eps) + self.norm_k = Ideogram4RMSNorm(self.head_dim, eps=eps) + self.o = nn.Linear(hidden_size, hidden_size, bias=False) + + def forward( + self, + x: torch.Tensor, + segment_ids: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, + ) -> torch.Tensor: + batch_size, seq_len, _ = x.shape - qkv = self.qkv(x) - qkv = qkv.view(batch_size, seq_len, 3, self.num_heads, self.head_dim) - q, k, v = qkv.unbind(dim=2) + qkv = self.qkv(x) + qkv = qkv.view(batch_size, seq_len, 3, self.num_heads, self.head_dim) + q, k, v = qkv.unbind(dim=2) - q = self.norm_q(q) - k = self.norm_k(k) + q = self.norm_q(q) + k = self.norm_k(k) - # SDPA expects (B, num_heads, L, head_dim). - q = q.transpose(1, 2) - k = k.transpose(1, 2) - v = v.transpose(1, 2) + # SDPA expects (B, num_heads, L, head_dim). + q = q.transpose(1, 2) + k = k.transpose(1, 2) + v = v.transpose(1, 2) - q, k = _apply_rotary_pos_emb(q, k, cos, sin) + q, k = _apply_rotary_pos_emb(q, k, cos, sin) - # Block-diagonal mask from segment ids: (B, 1, L, L), True = attend. - attn_mask = (segment_ids.unsqueeze(2) == segment_ids.unsqueeze(1)).unsqueeze(1) + # Block-diagonal mask from segment ids: (B, 1, L, L), True = attend. + attn_mask = (segment_ids.unsqueeze(2) == segment_ids.unsqueeze(1)).unsqueeze(1) - out = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) - out = out.transpose(1, 2).reshape(batch_size, seq_len, self.hidden_size) - return self.o(out) + out = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + out = out.transpose(1, 2).reshape(batch_size, seq_len, self.hidden_size) + return self.o(out) class Ideogram4MLP(nn.Module): - def __init__(self, dim: int, hidden_dim: int) -> None: - super().__init__() - self.w1 = nn.Linear(dim, hidden_dim, bias=False) - self.w2 = nn.Linear(hidden_dim, dim, bias=False) - self.w3 = nn.Linear(dim, hidden_dim, bias=False) + def __init__(self, dim: int, hidden_dim: int) -> None: + super().__init__() + self.w1 = nn.Linear(dim, hidden_dim, bias=False) + self.w2 = nn.Linear(hidden_dim, dim, bias=False) + self.w3 = nn.Linear(dim, hidden_dim, bias=False) - def forward(self, x: torch.Tensor) -> torch.Tensor: - return self.w2(F.silu(self.w1(x)) * self.w3(x)) + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.w2(F.silu(self.w1(x)) * self.w3(x)) class Ideogram4TransformerBlock(nn.Module): - def __init__( - self, - hidden_size: int, - intermediate_size: int, - num_heads: int, - norm_eps: float, - adanln_dim: int, - ) -> None: - super().__init__() - self.attention = Ideogram4Attention(hidden_size, num_heads, eps=1e-5) - self.feed_forward = Ideogram4MLP(hidden_size, intermediate_size) - - self.attention_norm1 = Ideogram4RMSNorm(hidden_size, eps=norm_eps) - self.ffn_norm1 = Ideogram4RMSNorm(hidden_size, eps=norm_eps) - self.attention_norm2 = Ideogram4RMSNorm(hidden_size, eps=norm_eps) - self.ffn_norm2 = Ideogram4RMSNorm(hidden_size, eps=norm_eps) - - self.adaln_modulation = nn.Linear(adanln_dim, 4 * hidden_size, bias=True) - - def forward( - self, - x: torch.Tensor, - segment_ids: torch.Tensor, - cos: torch.Tensor, - sin: torch.Tensor, - adaln_input: torch.Tensor, - ) -> torch.Tensor: - mod = self.adaln_modulation(adaln_input) - scale_msa, gate_msa, scale_mlp, gate_mlp = mod.chunk(4, dim=-1) - gate_msa = torch.tanh(gate_msa) - gate_mlp = torch.tanh(gate_mlp) - scale_msa = 1.0 + scale_msa - scale_mlp = 1.0 + scale_mlp - - attn_out = self.attention( - self.attention_norm1(x) * scale_msa, - segment_ids=segment_ids, - cos=cos, - sin=sin, - ) - x = x + gate_msa * self.attention_norm2(attn_out) - x = x + gate_mlp * self.ffn_norm2(self.feed_forward(self.ffn_norm1(x) * scale_mlp)) - return x - - -def _sinusoidal_embedding( - t: torch.Tensor, dim: int, scale: float = 1e4 -) -> torch.Tensor: - t = t.to(torch.float32) - half = dim // 2 - freq = math.log(scale) / (half - 1) - freq = torch.exp(torch.arange(half, dtype=torch.float32, device=t.device) * -freq) # type: ignore[assignment] - emb = t.unsqueeze(-1) * freq - emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) - if dim % 2 == 1: - emb = F.pad(emb, (0, 1)) - return emb + def __init__( + self, + hidden_size: int, + intermediate_size: int, + num_heads: int, + norm_eps: float, + adanln_dim: int, + ) -> None: + super().__init__() + self.attention = Ideogram4Attention(hidden_size, num_heads, eps=1e-5) + self.feed_forward = Ideogram4MLP(hidden_size, intermediate_size) + + self.attention_norm1 = Ideogram4RMSNorm(hidden_size, eps=norm_eps) + self.ffn_norm1 = Ideogram4RMSNorm(hidden_size, eps=norm_eps) + self.attention_norm2 = Ideogram4RMSNorm(hidden_size, eps=norm_eps) + self.ffn_norm2 = Ideogram4RMSNorm(hidden_size, eps=norm_eps) + + self.adaln_modulation = nn.Linear(adanln_dim, 4 * hidden_size, bias=True) + + def forward( + self, + x: torch.Tensor, + segment_ids: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, + adaln_input: torch.Tensor, + ) -> torch.Tensor: + mod = self.adaln_modulation(adaln_input) + scale_msa, gate_msa, scale_mlp, gate_mlp = mod.chunk(4, dim=-1) + gate_msa = torch.tanh(gate_msa) + gate_mlp = torch.tanh(gate_mlp) + scale_msa = 1.0 + scale_msa + scale_mlp = 1.0 + scale_mlp + + attn_out = self.attention( + self.attention_norm1(x) * scale_msa, + segment_ids=segment_ids, + cos=cos, + sin=sin, + ) + x = x + gate_msa * self.attention_norm2(attn_out) + x = x + gate_mlp * self.ffn_norm2(self.feed_forward(self.ffn_norm1(x) * scale_mlp)) + return x + + +def _sinusoidal_embedding(t: torch.Tensor, dim: int, scale: float = 1e4) -> torch.Tensor: + t = t.to(torch.float32) + half = dim // 2 + freq = math.log(scale) / (half - 1) + freq = torch.exp(torch.arange(half, dtype=torch.float32, device=t.device) * -freq) # type: ignore[assignment] + emb = t.unsqueeze(-1) * freq + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) + if dim % 2 == 1: + emb = F.pad(emb, (0, 1)) + return emb class Ideogram4EmbedScalar(nn.Module): - def __init__(self, dim: int, input_range: tuple[float, float]) -> None: - super().__init__() - self.dim = dim - self.range_min, self.range_max = input_range - assert self.range_max > self.range_min - self.mlp_in = nn.Linear(dim, dim, bias=True) - self.mlp_out = nn.Linear(dim, dim, bias=True) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - # x is shape (..., 1) or (...,) holding a scalar per token. - x = x.to(torch.float32) - scaled = 1e4 * (x - self.range_min) / (self.range_max - self.range_min) - emb = _sinusoidal_embedding(scaled, self.dim) - emb = emb.to( - getattr(self.mlp_in, "compute_dtype", None) or self.mlp_in.weight.dtype - ) - emb = F.silu(self.mlp_in(emb)) - return self.mlp_out(emb) + def __init__(self, dim: int, input_range: tuple[float, float]) -> None: + super().__init__() + self.dim = dim + self.range_min, self.range_max = input_range + assert self.range_max > self.range_min + self.mlp_in = nn.Linear(dim, dim, bias=True) + self.mlp_out = nn.Linear(dim, dim, bias=True) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # x is shape (..., 1) or (...,) holding a scalar per token. + x = x.to(torch.float32) + scaled = 1e4 * (x - self.range_min) / (self.range_max - self.range_min) + emb = _sinusoidal_embedding(scaled, self.dim) + emb = emb.to(getattr(self.mlp_in, "compute_dtype", None) or self.mlp_in.weight.dtype) + emb = F.silu(self.mlp_in(emb)) + return self.mlp_out(emb) class Ideogram4FinalLayer(nn.Module): - def __init__(self, hidden_size: int, out_channels: int, adanln_dim: int) -> None: - super().__init__() - self.norm_final = nn.LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False) - self.linear = nn.Linear(hidden_size, out_channels, bias=True) - self.adaln_modulation = nn.Linear(adanln_dim, hidden_size, bias=True) + def __init__(self, hidden_size: int, out_channels: int, adanln_dim: int) -> None: + super().__init__() + self.norm_final = nn.LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False) + self.linear = nn.Linear(hidden_size, out_channels, bias=True) + self.adaln_modulation = nn.Linear(adanln_dim, hidden_size, bias=True) - def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: - scale = 1.0 + self.adaln_modulation(F.silu(c)) - return self.linear(self.norm_final(x) * scale) + def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + scale = 1.0 + self.adaln_modulation(F.silu(c)) + return self.linear(self.norm_final(x) * scale) class Ideogram4Transformer(nn.Module): - """Ideogram 4 flow-matching transformer.""" - - def __init__(self, config: Ideogram4Config) -> None: - super().__init__() - self.config = config - - head_dim = config.emb_dim // config.num_heads - - self.input_proj = nn.Linear(config.in_channels, config.emb_dim, bias=True) - self.llm_cond_norm = Ideogram4RMSNorm(config.llm_features_dim, eps=1e-6) - self.llm_cond_proj = nn.Linear(config.llm_features_dim, config.emb_dim, bias=True) - self.t_embedding = Ideogram4EmbedScalar(config.emb_dim, input_range=(0.0, 1.0)) - self.adaln_proj = nn.Linear(config.emb_dim, config.adanln_dim, bias=True) - - self.embed_image_indicator = nn.Embedding(2, config.emb_dim) - - self.rotary_emb = Ideogram4MRoPE( - head_dim=head_dim, - base=config.rope_theta, - mrope_section=config.mrope_section, - ) - - self.layers = nn.ModuleList( - [ - Ideogram4TransformerBlock( - hidden_size=config.emb_dim, - intermediate_size=config.intermediate_size, - num_heads=config.num_heads, - norm_eps=config.norm_eps, - adanln_dim=config.adanln_dim, + """Ideogram 4 flow-matching transformer.""" + + def __init__(self, config: Ideogram4Config) -> None: + super().__init__() + self.config = config + + head_dim = config.emb_dim // config.num_heads + + self.input_proj = nn.Linear(config.in_channels, config.emb_dim, bias=True) + self.llm_cond_norm = Ideogram4RMSNorm(config.llm_features_dim, eps=1e-6) + self.llm_cond_proj = nn.Linear(config.llm_features_dim, config.emb_dim, bias=True) + self.t_embedding = Ideogram4EmbedScalar(config.emb_dim, input_range=(0.0, 1.0)) + self.adaln_proj = nn.Linear(config.emb_dim, config.adanln_dim, bias=True) + + self.embed_image_indicator = nn.Embedding(2, config.emb_dim) + + self.rotary_emb = Ideogram4MRoPE( + head_dim=head_dim, + base=config.rope_theta, + mrope_section=config.mrope_section, ) - for _ in range(config.num_layers) - ] - ) - - self.final_layer = Ideogram4FinalLayer( - hidden_size=config.emb_dim, - out_channels=config.in_channels, - adanln_dim=config.adanln_dim, - ) - - @property - def device(self) -> torch.device: - return next(self.parameters()).device - - def forward( - self, - *, - llm_features: torch.Tensor, - x: torch.Tensor, - t: torch.Tensor, - position_ids: torch.Tensor, - segment_ids: torch.Tensor, - indicator: torch.Tensor, - ) -> torch.Tensor: - """Velocity prediction. - - Args: - llm_features: (B, L, llm_features_dim) Qwen3-VL conditioning features. - x: (B, L, in_channels) noise tokens. - t: (B,) or (B, L) flow-matching time in [0, 1]. - position_ids: (B, L, 3) (t, h, w) positions for MRoPE. - segment_ids: (B, L) sample id within a packed batch. - indicator: (B, L) per-token role: LLM_TOKEN_INDICATOR or OUTPUT_IMAGE_INDICATOR. - - Returns: - (B, L, in_channels) velocity prediction in float32. Only the positions - with ``indicator == OUTPUT_IMAGE_INDICATOR`` are meaningful. - """ - batch_size, seq_len, in_channels = x.shape - assert in_channels == self.config.in_channels - - param_dtype = ( - getattr(self.input_proj, "compute_dtype", None) or self.input_proj.weight.dtype - ) - x = x.to(param_dtype) - t = t.to(param_dtype) - llm_features = llm_features.to(param_dtype) - - indicator = indicator.to(torch.long) - llm_token_mask = (indicator == LLM_TOKEN_INDICATOR).to(x.dtype).unsqueeze(-1) - output_image_mask = (indicator == OUTPUT_IMAGE_INDICATOR).to(x.dtype).unsqueeze(-1) - - llm_features = llm_features * llm_token_mask - x = x * output_image_mask - - x = self.input_proj(x) * output_image_mask - - # Keep shape (B, 1, ...) when t is per-sample so downstream adaln_modulation - # projections don't pay for L identical copies. - t_cond = self.t_embedding(t) - if t.dim() == 1: - t_cond = t_cond.unsqueeze(1) - adaln_input = F.silu(self.adaln_proj(t_cond)) - - llm_features = self.llm_cond_norm(llm_features) - llm_features = self.llm_cond_proj(llm_features) * llm_token_mask - - h = x + llm_features - - image_indicator_embedding = self.embed_image_indicator( - (indicator == OUTPUT_IMAGE_INDICATOR).to(torch.long) - ) - h = h + image_indicator_embedding - - cos, sin = self.rotary_emb(position_ids) - cos = cos.to(h.dtype) - sin = sin.to(h.dtype) - - for layer in self.layers: - h = layer(h, segment_ids=segment_ids, cos=cos, sin=sin, adaln_input=adaln_input) - - out = self.final_layer(h, c=adaln_input) - return out.to(torch.float32) + + self.layers = nn.ModuleList( + [ + Ideogram4TransformerBlock( + hidden_size=config.emb_dim, + intermediate_size=config.intermediate_size, + num_heads=config.num_heads, + norm_eps=config.norm_eps, + adanln_dim=config.adanln_dim, + ) + for _ in range(config.num_layers) + ] + ) + + self.final_layer = Ideogram4FinalLayer( + hidden_size=config.emb_dim, + out_channels=config.in_channels, + adanln_dim=config.adanln_dim, + ) + + @property + def device(self) -> torch.device: + return next(self.parameters()).device + + def forward( + self, + *, + llm_features: torch.Tensor, + x: torch.Tensor, + t: torch.Tensor, + position_ids: torch.Tensor, + segment_ids: torch.Tensor, + indicator: torch.Tensor, + ) -> torch.Tensor: + """Velocity prediction. + + Args: + llm_features: (B, L, llm_features_dim) Qwen3-VL conditioning features. + x: (B, L, in_channels) noise tokens. + t: (B,) or (B, L) flow-matching time in [0, 1]. + position_ids: (B, L, 3) (t, h, w) positions for MRoPE. + segment_ids: (B, L) sample id within a packed batch. + indicator: (B, L) per-token role: LLM_TOKEN_INDICATOR or OUTPUT_IMAGE_INDICATOR. + + Returns: + (B, L, in_channels) velocity prediction in float32. Only the positions + with ``indicator == OUTPUT_IMAGE_INDICATOR`` are meaningful. + """ + batch_size, seq_len, in_channels = x.shape + assert in_channels == self.config.in_channels + + param_dtype = getattr(self.input_proj, "compute_dtype", None) or self.input_proj.weight.dtype + x = x.to(param_dtype) + t = t.to(param_dtype) + llm_features = llm_features.to(param_dtype) + + indicator = indicator.to(torch.long) + llm_token_mask = (indicator == LLM_TOKEN_INDICATOR).to(x.dtype).unsqueeze(-1) + output_image_mask = (indicator == OUTPUT_IMAGE_INDICATOR).to(x.dtype).unsqueeze(-1) + + llm_features = llm_features * llm_token_mask + x = x * output_image_mask + + x = self.input_proj(x) * output_image_mask + + # Keep shape (B, 1, ...) when t is per-sample so downstream adaln_modulation + # projections don't pay for L identical copies. + t_cond = self.t_embedding(t) + if t.dim() == 1: + t_cond = t_cond.unsqueeze(1) + adaln_input = F.silu(self.adaln_proj(t_cond)) + + llm_features = self.llm_cond_norm(llm_features) + llm_features = self.llm_cond_proj(llm_features) * llm_token_mask + + h = x + llm_features + + image_indicator_embedding = self.embed_image_indicator((indicator == OUTPUT_IMAGE_INDICATOR).to(torch.long)) + h = h + image_indicator_embedding + + cos, sin = self.rotary_emb(position_ids) + cos = cos.to(h.dtype) + sin = sin.to(h.dtype) + + for layer in self.layers: + h = layer(h, segment_ids=segment_ids, cos=cos, sin=sin, adaln_input=adaln_input) + + out = self.final_layer(h, c=adaln_input) + return out.to(torch.float32) diff --git a/invokeai/backend/ideogram4/sampler_configs.py b/invokeai/backend/ideogram4/sampler_configs.py index f3204b8c596..5aefa31e454 100644 --- a/invokeai/backend/ideogram4/sampler_configs.py +++ b/invokeai/backend/ideogram4/sampler_configs.py @@ -8,22 +8,22 @@ # Each preset does the first N_main sampling steps at gw=7, then N_cleanup # polish steps at gw=3. PRESETS: dict[str, SamplerParameters] = { - "V4_QUALITY_48": SamplerParameters( - num_steps=48, - guidance_schedule=(3.0,) * 3 + (7.0,) * 45, - mu=0.0, - std=1.5, - ), - "V4_DEFAULT_20": SamplerParameters( - num_steps=20, - guidance_schedule=(3.0,) * 2 + (7.0,) * 18, - mu=0.0, - std=1.75, - ), - "V4_TURBO_12": SamplerParameters( - num_steps=12, - guidance_schedule=(3.0,) * 1 + (7.0,) * 11, - mu=0.5, - std=1.75, - ), + "V4_QUALITY_48": SamplerParameters( + num_steps=48, + guidance_schedule=(3.0,) * 3 + (7.0,) * 45, + mu=0.0, + std=1.5, + ), + "V4_DEFAULT_20": SamplerParameters( + num_steps=20, + guidance_schedule=(3.0,) * 2 + (7.0,) * 18, + mu=0.0, + std=1.75, + ), + "V4_TURBO_12": SamplerParameters( + num_steps=12, + guidance_schedule=(3.0,) * 1 + (7.0,) * 11, + mu=0.5, + std=1.75, + ), } diff --git a/invokeai/backend/ideogram4/sampling_utils.py b/invokeai/backend/ideogram4/sampling_utils.py index 173014ea6ee..54dede15bb3 100644 --- a/invokeai/backend/ideogram4/sampling_utils.py +++ b/invokeai/backend/ideogram4/sampling_utils.py @@ -45,9 +45,7 @@ class Ideogram4DenoiseInputs(TypedDict): def validate_dimensions(height: int, width: int) -> None: """Ensure the requested resolution is compatible with the patch/VAE grid.""" if height % PIXELS_PER_IMAGE_TOKEN != 0 or width % PIXELS_PER_IMAGE_TOKEN != 0: - raise ValueError( - f"height and width must be divisible by {PIXELS_PER_IMAGE_TOKEN}, got {height}x{width}" - ) + raise ValueError(f"height and width must be divisible by {PIXELS_PER_IMAGE_TOKEN}, got {height}x{width}") def build_denoise_inputs( diff --git a/invokeai/backend/ideogram4/scheduler.py b/invokeai/backend/ideogram4/scheduler.py index d84b46be0a9..1a9d3fab10b 100644 --- a/invokeai/backend/ideogram4/scheduler.py +++ b/invokeai/backend/ideogram4/scheduler.py @@ -10,61 +10,60 @@ @dataclass(frozen=True) class LogitNormalSchedule: - mean: float - std: float = 1.0 - logsnr_min: float = -15.0 - logsnr_max: float = 18.0 - - def __call__(self, t: torch.Tensor) -> torch.Tensor: - t = t.to(torch.float64) - z = torch.special.ndtri(t) - y = self.mean + self.std * z - t_ = torch.special.expit(y) - t_ = 1 - t_ - t_min = 1.0 / (1 + math.exp(0.5 * self.logsnr_max)) - t_max = 1.0 / (1 + math.exp(0.5 * self.logsnr_min)) - return t_.clamp(t_min, t_max).to(torch.float32) + mean: float + std: float = 1.0 + logsnr_min: float = -15.0 + logsnr_max: float = 18.0 + + def __call__(self, t: torch.Tensor) -> torch.Tensor: + t = t.to(torch.float64) + z = torch.special.ndtri(t) + y = self.mean + self.std * z + t_ = torch.special.expit(y) + t_ = 1 - t_ + t_min = 1.0 / (1 + math.exp(0.5 * self.logsnr_max)) + t_max = 1.0 / (1 + math.exp(0.5 * self.logsnr_min)) + return t_.clamp(t_min, t_max).to(torch.float32) def get_schedule_for_resolution( - image_resolution: tuple[int, int], - known_resolution: tuple[int, int] = (512, 512), - known_mean: float = 1.0, - std: float = 1.0, + image_resolution: tuple[int, int], + known_resolution: tuple[int, int] = (512, 512), + known_mean: float = 1.0, + std: float = 1.0, ) -> LogitNormalSchedule: - """Resolution-aware schedule used at eval time.""" - num_pixels = image_resolution[0] * image_resolution[1] - known_pixels = known_resolution[0] * known_resolution[1] - mean = known_mean + 0.5 * math.log(num_pixels / known_pixels) - return LogitNormalSchedule(mean=mean, std=std) + """Resolution-aware schedule used at eval time.""" + num_pixels = image_resolution[0] * image_resolution[1] + known_pixels = known_resolution[0] * known_resolution[1] + mean = known_mean + 0.5 * math.log(num_pixels / known_pixels) + return LogitNormalSchedule(mean=mean, std=std) def make_step_intervals(num_steps: int) -> torch.Tensor: - """Default linear step schedule used by the v4 eval config.""" - return torch.linspace(0.0, 1.0, num_steps + 1, dtype=torch.float32) + """Default linear step schedule used by the v4 eval config.""" + return torch.linspace(0.0, 1.0, num_steps + 1, dtype=torch.float32) @dataclass(frozen=True, kw_only=True) class SamplerParameters: - """Bundle of sampling hyperparameters for a named preset. - - ``guidance_schedule`` is in LOOP-INDEX order: index 0 is the LAST sampling - step (final polish), index ``num_steps - 1`` is the FIRST sampling step. - ``mu`` and ``std`` are the mean and stddev of the logit-normal noise - schedule passed to ``get_schedule_for_resolution`` (as ``known_mean`` and - ``std`` respectively). - - See ``ideogram4.sampler_configs.PRESETS`` for the named preset registry. - """ - - num_steps: int - guidance_schedule: tuple[float, ...] - mu: float - std: float = 1.0 - - def __post_init__(self) -> None: - if len(self.guidance_schedule) != self.num_steps: - raise ValueError( - f"guidance_schedule has length {len(self.guidance_schedule)}, " - f"expected num_steps={self.num_steps}" - ) + """Bundle of sampling hyperparameters for a named preset. + + ``guidance_schedule`` is in LOOP-INDEX order: index 0 is the LAST sampling + step (final polish), index ``num_steps - 1`` is the FIRST sampling step. + ``mu`` and ``std`` are the mean and stddev of the logit-normal noise + schedule passed to ``get_schedule_for_resolution`` (as ``known_mean`` and + ``std`` respectively). + + See ``ideogram4.sampler_configs.PRESETS`` for the named preset registry. + """ + + num_steps: int + guidance_schedule: tuple[float, ...] + mu: float + std: float = 1.0 + + def __post_init__(self) -> None: + if len(self.guidance_schedule) != self.num_steps: + raise ValueError( + f"guidance_schedule has length {len(self.guidance_schedule)}, expected num_steps={self.num_steps}" + ) diff --git a/invokeai/backend/ideogram4/text_encoding.py b/invokeai/backend/ideogram4/text_encoding.py index 45cc7e3e7cf..47eb97925cd 100644 --- a/invokeai/backend/ideogram4/text_encoding.py +++ b/invokeai/backend/ideogram4/text_encoding.py @@ -45,9 +45,7 @@ def encode_qwen3vl_prompt( token_ids = encoded["input_ids"].to(device) # (1, L) num_text_tokens = int(token_ids.shape[1]) if num_text_tokens > max_text_tokens: - raise ValueError( - f"prompt has {num_text_tokens} tokens, exceeds max_text_tokens={max_text_tokens}" - ) + raise ValueError(f"prompt has {num_text_tokens} tokens, exceeds max_text_tokens={max_text_tokens}") # Text-only sequence: every position is a real LLM token. attention_mask = torch.ones((1, num_text_tokens), dtype=torch.long, device=device) From 151296de1503d816ea6cf30e0027d78c1cf184a0 Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Thu, 16 Jul 2026 05:04:25 +0200 Subject: [PATCH 20/33] Chore OpenApi --- invokeai/frontend/web/openapi.json | 1133 +++++++++++++++++++++++----- 1 file changed, 958 insertions(+), 175 deletions(-) diff --git a/invokeai/frontend/web/openapi.json b/invokeai/frontend/web/openapi.json index e2801e9e39a..e16cb03ca76 100644 --- a/invokeai/frontend/web/openapi.json +++ b/invokeai/frontend/web/openapi.json @@ -804,6 +804,9 @@ { "$ref": "#/components/schemas/Main_Diffusers_ZImage_Config" }, + { + "$ref": "#/components/schemas/Main_Diffusers_Ideogram4_Config" + }, { "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config" }, @@ -1128,6 +1131,9 @@ { "$ref": "#/components/schemas/Main_Diffusers_ZImage_Config" }, + { + "$ref": "#/components/schemas/Main_Diffusers_Ideogram4_Config" + }, { "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config" }, @@ -1452,6 +1458,9 @@ { "$ref": "#/components/schemas/Main_Diffusers_ZImage_Config" }, + { + "$ref": "#/components/schemas/Main_Diffusers_Ideogram4_Config" + }, { "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config" }, @@ -1826,6 +1835,9 @@ { "$ref": "#/components/schemas/Main_Diffusers_ZImage_Config" }, + { + "$ref": "#/components/schemas/Main_Diffusers_Ideogram4_Config" + }, { "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config" }, @@ -2224,6 +2236,9 @@ { "$ref": "#/components/schemas/Main_Diffusers_ZImage_Config" }, + { + "$ref": "#/components/schemas/Main_Diffusers_Ideogram4_Config" + }, { "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config" }, @@ -3442,6 +3457,9 @@ { "$ref": "#/components/schemas/Main_Diffusers_ZImage_Config" }, + { + "$ref": "#/components/schemas/Main_Diffusers_Ideogram4_Config" + }, { "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config" }, @@ -11740,6 +11758,9 @@ { "$ref": "#/components/schemas/Main_Diffusers_ZImage_Config" }, + { + "$ref": "#/components/schemas/Main_Diffusers_Ideogram4_Config" + }, { "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config" }, @@ -12276,6 +12297,7 @@ "flux2", "cogview4", "z-image", + "ideogram-4", "external", "qwen-image", "anima", @@ -19440,6 +19462,7 @@ "z_image_img2img", "z_image_inpaint", "z_image_outpaint", + "ideogram4_txt2img", "qwen_image_txt2img", "qwen_image_img2img", "qwen_image_inpaint", @@ -29507,6 +29530,18 @@ { "$ref": "#/components/schemas/IdealSizeInvocation" }, + { + "$ref": "#/components/schemas/Ideogram4DenoiseInvocation" + }, + { + "$ref": "#/components/schemas/Ideogram4LatentsToImageInvocation" + }, + { + "$ref": "#/components/schemas/Ideogram4ModelLoaderInvocation" + }, + { + "$ref": "#/components/schemas/Ideogram4TextEncoderInvocation" + }, { "$ref": "#/components/schemas/IfInvocation" }, @@ -30164,6 +30199,12 @@ { "$ref": "#/components/schemas/IdealSizeOutput" }, + { + "$ref": "#/components/schemas/Ideogram4ConditioningOutput" + }, + { + "$ref": "#/components/schemas/Ideogram4ModelLoaderOutput" + }, { "$ref": "#/components/schemas/IfInvocationOutput" }, @@ -32351,11 +32392,46 @@ "title": "IdealSizeOutput", "type": "object" }, - "IfInvocation": { - "category": "math", + "Ideogram4ConditioningField": { + "description": "An Ideogram 4 conditioning tensor primitive value", + "properties": { + "conditioning_name": { + "description": "The name of conditioning tensor", + "title": "Conditioning Name", + "type": "string" + } + }, + "required": ["conditioning_name"], + "title": "Ideogram4ConditioningField", + "type": "object" + }, + "Ideogram4ConditioningOutput": { + "class": "output", + "description": "Base class for nodes that output an Ideogram 4 text conditioning tensor.", + "properties": { + "conditioning": { + "$ref": "#/components/schemas/Ideogram4ConditioningField", + "description": "Conditioning tensor", + "field_kind": "output", + "ui_hidden": false + }, + "type": { + "const": "ideogram4_conditioning_output", + "default": "ideogram4_conditioning_output", + "field_kind": "node_attribute", + "title": "type", + "type": "string" + } + }, + "required": ["output_meta", "conditioning", "type", "type"], + "title": "Ideogram4ConditioningOutput", + "type": "object" + }, + "Ideogram4DenoiseInvocation": { + "category": "latents", "class": "invocation", - "classification": "stable", - "description": "Selects between two optional inputs based on a boolean condition.", + "classification": "prototype", + "description": "Runs the Ideogram 4 dual-branch flow-matching denoising loop (text-to-image).", "node_pack": "invokeai", "properties": { "id": { @@ -32382,99 +32458,564 @@ "title": "Use Cache", "type": "boolean" }, - "condition": { - "default": false, - "description": "The condition used to select an input", + "transformer": { + "anyOf": [ + { + "$ref": "#/components/schemas/TransformerField" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Transformer", + "field_kind": "input", + "input": "connection", + "orig_required": true, + "title": "Transformer" + }, + "positive_conditioning": { + "anyOf": [ + { + "$ref": "#/components/schemas/Ideogram4ConditioningField" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Positive conditioning tensor", + "field_kind": "input", + "input": "connection", + "orig_required": true + }, + "sampler_preset": { + "default": "V4_QUALITY_48", + "description": "Sampler preset (steps + guidance schedule + schedule mean/std).", + "enum": ["V4_QUALITY_48", "V4_DEFAULT_20", "V4_TURBO_12"], "field_kind": "input", "input": "any", - "orig_default": false, + "orig_default": "V4_QUALITY_48", "orig_required": false, - "title": "Condition", - "type": "boolean" + "title": "Sampler Preset", + "type": "string" }, - "true_input": { + "width": { + "default": 1024, + "description": "Width of the generated image.", + "field_kind": "input", + "input": "any", + "multipleOf": 16, + "orig_default": 1024, + "orig_required": false, + "title": "Width", + "type": "integer" + }, + "height": { + "default": 1024, + "description": "Height of the generated image.", + "field_kind": "input", + "input": "any", + "multipleOf": 16, + "orig_default": 1024, + "orig_required": false, + "title": "Height", + "type": "integer" + }, + "seed": { + "default": 0, + "description": "Randomness seed for reproducibility.", + "field_kind": "input", + "input": "any", + "orig_default": 0, + "orig_required": false, + "title": "Seed", + "type": "integer" + }, + "steps": { "anyOf": [ - {}, + { + "maximum": 100, + "minimum": 1, + "type": "integer" + }, { "type": "null" } ], "default": null, - "description": "Selected when the condition is true", + "description": "Override the preset's step count. Leave empty to use the preset.", "field_kind": "input", "input": "any", "orig_default": null, "orig_required": false, - "title": "True Input", - "ui_type": "AnyField" + "title": "Steps" }, - "false_input": { + "guidance_scale": { "anyOf": [ - {}, + { + "maximum": 20.0, + "minimum": 1.0, + "type": "number" + }, { "type": "null" } ], "default": null, - "description": "Selected when the condition is false", + "description": "Override the main guidance weight (the preset's polish tail is preserved). Empty = use the preset.", "field_kind": "input", "input": "any", "orig_default": null, "orig_required": false, - "title": "False Input", - "ui_type": "AnyField" + "title": "Guidance Scale" + }, + "mu": { + "anyOf": [ + { + "maximum": 4.0, + "minimum": -4.0, + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Override the logit-normal schedule mean (resolution-adjusted internally). Empty = use the preset.", + "field_kind": "input", + "input": "any", + "orig_default": null, + "orig_required": false, + "title": "Mu" }, "type": { - "const": "if", - "default": "if", + "const": "ideogram4_denoise", + "default": "ideogram4_denoise", "field_kind": "node_attribute", "title": "type", "type": "string" } }, "required": ["type", "id"], - "tags": ["logic", "conditional"], - "title": "If", + "tags": ["image", "ideogram4"], + "title": "Denoise - Ideogram 4", "type": "object", "version": "1.0.0", "output": { - "$ref": "#/components/schemas/IfInvocationOutput" + "$ref": "#/components/schemas/LatentsOutput" } }, - "IfInvocationOutput": { - "class": "output", + "Ideogram4LatentsToImageInvocation": { + "category": "latents", + "class": "invocation", + "classification": "prototype", + "description": "Decodes Ideogram 4 packed latents to an image with the FLUX.2-style VAE.", + "node_pack": "invokeai", "properties": { - "value": { + "board": { "anyOf": [ - {}, + { + "$ref": "#/components/schemas/BoardField" + }, { "type": "null" } ], "default": null, - "description": "The selected value", - "field_kind": "output", - "title": "Output", + "description": "The board to save the image to", + "field_kind": "internal", + "input": "direct", + "orig_required": false, + "ui_hidden": false + }, + "metadata": { + "anyOf": [ + { + "$ref": "#/components/schemas/MetadataField" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional metadata to be saved with the image", + "field_kind": "internal", + "input": "connection", + "orig_required": false, + "ui_hidden": false + }, + "id": { + "description": "The id of this instance of an invocation. Must be unique among all instances of invocations.", + "field_kind": "node_attribute", + "title": "Id", + "type": "string" + }, + "is_intermediate": { + "default": false, + "description": "Whether or not this is an intermediate invocation.", + "field_kind": "node_attribute", + "input": "direct", + "orig_required": true, + "title": "Is Intermediate", + "type": "boolean", "ui_hidden": false, - "ui_type": "AnyField" + "ui_type": "IsIntermediate" + }, + "use_cache": { + "default": true, + "description": "Whether or not to use the cache", + "field_kind": "node_attribute", + "title": "Use Cache", + "type": "boolean" + }, + "latents": { + "anyOf": [ + { + "$ref": "#/components/schemas/LatentsField" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Latents tensor", + "field_kind": "input", + "input": "connection", + "orig_required": true + }, + "vae": { + "anyOf": [ + { + "$ref": "#/components/schemas/VAEField" + }, + { + "type": "null" + } + ], + "default": null, + "description": "VAE", + "field_kind": "input", + "input": "connection", + "orig_required": true }, "type": { - "const": "if_output", - "default": "if_output", + "const": "ideogram4_l2i", + "default": "ideogram4_l2i", "field_kind": "node_attribute", "title": "type", "type": "string" } }, - "required": ["output_meta", "value", "type", "type"], - "title": "IfInvocationOutput", - "type": "object" + "required": ["type", "id"], + "tags": ["latents", "image", "vae", "l2i", "ideogram4"], + "title": "Latents to Image - Ideogram 4", + "type": "object", + "version": "1.0.0", + "output": { + "$ref": "#/components/schemas/ImageOutput" + } }, - "ImageBatchInvocation": { - "category": "batch", + "Ideogram4ModelLoaderInvocation": { + "category": "model", "class": "invocation", - "classification": "special", - "description": "Create a batched generation, where the workflow is executed once for each image in the batch.", + "classification": "prototype", + "description": "Loads an Ideogram 4 model, outputting its submodels.\n\nIdeogram 4 is distributed as a single bundled diffusers folder, so the transformer\n(both branches), the Qwen3-VL text encoder + tokenizer, and the VAE are all loaded\nfrom the one selected model.", + "node_pack": "invokeai", + "properties": { + "id": { + "description": "The id of this instance of an invocation. Must be unique among all instances of invocations.", + "field_kind": "node_attribute", + "title": "Id", + "type": "string" + }, + "is_intermediate": { + "default": false, + "description": "Whether or not this is an intermediate invocation.", + "field_kind": "node_attribute", + "input": "direct", + "orig_required": true, + "title": "Is Intermediate", + "type": "boolean", + "ui_hidden": false, + "ui_type": "IsIntermediate" + }, + "use_cache": { + "default": true, + "description": "Whether or not to use the cache", + "field_kind": "node_attribute", + "title": "Use Cache", + "type": "boolean" + }, + "model": { + "$ref": "#/components/schemas/ModelIdentifierField", + "description": "The Ideogram 4 model to load.", + "field_kind": "input", + "input": "direct", + "orig_required": true, + "title": "Model", + "ui_model_base": ["ideogram-4"], + "ui_model_type": ["main"] + }, + "type": { + "const": "ideogram4_model_loader", + "default": "ideogram4_model_loader", + "field_kind": "node_attribute", + "title": "type", + "type": "string" + } + }, + "required": ["model", "type", "id"], + "tags": ["model", "ideogram4"], + "title": "Main Model - Ideogram 4", + "type": "object", + "version": "1.0.0", + "output": { + "$ref": "#/components/schemas/Ideogram4ModelLoaderOutput" + } + }, + "Ideogram4ModelLoaderOutput": { + "class": "output", + "description": "Ideogram 4 model loader output.", + "properties": { + "transformer": { + "$ref": "#/components/schemas/TransformerField", + "description": "Transformer", + "field_kind": "output", + "title": "Transformer", + "ui_hidden": false + }, + "qwen3_encoder": { + "$ref": "#/components/schemas/Qwen3EncoderField", + "description": "Qwen3 tokenizer and text encoder", + "field_kind": "output", + "title": "Qwen3-VL Encoder", + "ui_hidden": false + }, + "vae": { + "$ref": "#/components/schemas/VAEField", + "description": "VAE", + "field_kind": "output", + "title": "VAE", + "ui_hidden": false + }, + "type": { + "const": "ideogram4_model_loader_output", + "default": "ideogram4_model_loader_output", + "field_kind": "node_attribute", + "title": "type", + "type": "string" + } + }, + "required": ["output_meta", "transformer", "qwen3_encoder", "vae", "type", "type"], + "title": "Ideogram4ModelLoaderOutput", + "type": "object" + }, + "Ideogram4TextEncoderInvocation": { + "category": "conditioning", + "class": "invocation", + "classification": "prototype", + "description": "Encodes a prompt for Ideogram 4 using the Qwen3-VL encoder.\n\nThe prompt is normally a structured JSON caption (see the Ideogram 4 prompting guide);\nplain text also works but yields lower-quality results.", + "node_pack": "invokeai", + "properties": { + "id": { + "description": "The id of this instance of an invocation. Must be unique among all instances of invocations.", + "field_kind": "node_attribute", + "title": "Id", + "type": "string" + }, + "is_intermediate": { + "default": false, + "description": "Whether or not this is an intermediate invocation.", + "field_kind": "node_attribute", + "input": "direct", + "orig_required": true, + "title": "Is Intermediate", + "type": "boolean", + "ui_hidden": false, + "ui_type": "IsIntermediate" + }, + "use_cache": { + "default": true, + "description": "Whether or not to use the cache", + "field_kind": "node_attribute", + "title": "Use Cache", + "type": "boolean" + }, + "prompt": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The prompt to encode. A structured JSON caption is recommended.", + "field_kind": "input", + "input": "any", + "orig_required": true, + "title": "Prompt", + "ui_component": "textarea" + }, + "qwen3_encoder": { + "anyOf": [ + { + "$ref": "#/components/schemas/Qwen3EncoderField" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Qwen3 tokenizer and text encoder", + "field_kind": "input", + "input": "connection", + "orig_required": true, + "title": "Qwen3-VL Encoder" + }, + "type": { + "const": "ideogram4_text_encoder", + "default": "ideogram4_text_encoder", + "field_kind": "node_attribute", + "title": "type", + "type": "string" + } + }, + "required": ["type", "id"], + "tags": ["prompt", "conditioning", "ideogram4"], + "title": "Prompt - Ideogram 4", + "type": "object", + "version": "1.0.0", + "output": { + "$ref": "#/components/schemas/Ideogram4ConditioningOutput" + } + }, + "IfInvocation": { + "category": "math", + "class": "invocation", + "classification": "stable", + "description": "Selects between two optional inputs based on a boolean condition.", + "node_pack": "invokeai", + "properties": { + "id": { + "description": "The id of this instance of an invocation. Must be unique among all instances of invocations.", + "field_kind": "node_attribute", + "title": "Id", + "type": "string" + }, + "is_intermediate": { + "default": false, + "description": "Whether or not this is an intermediate invocation.", + "field_kind": "node_attribute", + "input": "direct", + "orig_required": true, + "title": "Is Intermediate", + "type": "boolean", + "ui_hidden": false, + "ui_type": "IsIntermediate" + }, + "use_cache": { + "default": true, + "description": "Whether or not to use the cache", + "field_kind": "node_attribute", + "title": "Use Cache", + "type": "boolean" + }, + "condition": { + "default": false, + "description": "The condition used to select an input", + "field_kind": "input", + "input": "any", + "orig_default": false, + "orig_required": false, + "title": "Condition", + "type": "boolean" + }, + "true_input": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "description": "Selected when the condition is true", + "field_kind": "input", + "input": "any", + "orig_default": null, + "orig_required": false, + "title": "True Input", + "ui_type": "AnyField" + }, + "false_input": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "description": "Selected when the condition is false", + "field_kind": "input", + "input": "any", + "orig_default": null, + "orig_required": false, + "title": "False Input", + "ui_type": "AnyField" + }, + "type": { + "const": "if", + "default": "if", + "field_kind": "node_attribute", + "title": "type", + "type": "string" + } + }, + "required": ["type", "id"], + "tags": ["logic", "conditional"], + "title": "If", + "type": "object", + "version": "1.0.0", + "output": { + "$ref": "#/components/schemas/IfInvocationOutput" + } + }, + "IfInvocationOutput": { + "class": "output", + "properties": { + "value": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "description": "The selected value", + "field_kind": "output", + "title": "Output", + "ui_hidden": false, + "ui_type": "AnyField" + }, + "type": { + "const": "if_output", + "default": "if_output", + "field_kind": "node_attribute", + "title": "type", + "type": "string" + } + }, + "required": ["output_meta", "value", "type", "type"], + "title": "IfInvocationOutput", + "type": "object" + }, + "ImageBatchInvocation": { + "category": "batch", + "class": "invocation", + "classification": "special", + "description": "Create a batched generation, where the workflow is executed once for each image in the batch.", "node_pack": "invokeai", "properties": { "id": { @@ -37209,6 +37750,18 @@ { "$ref": "#/components/schemas/IdealSizeInvocation" }, + { + "$ref": "#/components/schemas/Ideogram4DenoiseInvocation" + }, + { + "$ref": "#/components/schemas/Ideogram4LatentsToImageInvocation" + }, + { + "$ref": "#/components/schemas/Ideogram4ModelLoaderInvocation" + }, + { + "$ref": "#/components/schemas/Ideogram4TextEncoderInvocation" + }, { "$ref": "#/components/schemas/IfInvocation" }, @@ -37823,6 +38376,12 @@ { "$ref": "#/components/schemas/IdealSizeOutput" }, + { + "$ref": "#/components/schemas/Ideogram4ConditioningOutput" + }, + { + "$ref": "#/components/schemas/Ideogram4ModelLoaderOutput" + }, { "$ref": "#/components/schemas/IfInvocationOutput" }, @@ -38365,6 +38924,18 @@ { "$ref": "#/components/schemas/IdealSizeInvocation" }, + { + "$ref": "#/components/schemas/Ideogram4DenoiseInvocation" + }, + { + "$ref": "#/components/schemas/Ideogram4LatentsToImageInvocation" + }, + { + "$ref": "#/components/schemas/Ideogram4ModelLoaderInvocation" + }, + { + "$ref": "#/components/schemas/Ideogram4TextEncoderInvocation" + }, { "$ref": "#/components/schemas/IfInvocation" }, @@ -39181,6 +39752,18 @@ "ideal_size": { "$ref": "#/components/schemas/IdealSizeOutput" }, + "ideogram4_denoise": { + "$ref": "#/components/schemas/LatentsOutput" + }, + "ideogram4_l2i": { + "$ref": "#/components/schemas/ImageOutput" + }, + "ideogram4_model_loader": { + "$ref": "#/components/schemas/Ideogram4ModelLoaderOutput" + }, + "ideogram4_text_encoder": { + "$ref": "#/components/schemas/Ideogram4ConditioningOutput" + }, "if": { "$ref": "#/components/schemas/IfInvocationOutput" }, @@ -39773,6 +40356,10 @@ "heuristic_resize", "i2l", "ideal_size", + "ideogram4_denoise", + "ideogram4_l2i", + "ideogram4_model_loader", + "ideogram4_text_encoder", "if", "image", "image_batch", @@ -40297,6 +40884,18 @@ { "$ref": "#/components/schemas/IdealSizeInvocation" }, + { + "$ref": "#/components/schemas/Ideogram4DenoiseInvocation" + }, + { + "$ref": "#/components/schemas/Ideogram4LatentsToImageInvocation" + }, + { + "$ref": "#/components/schemas/Ideogram4ModelLoaderInvocation" + }, + { + "$ref": "#/components/schemas/Ideogram4TextEncoderInvocation" + }, { "$ref": "#/components/schemas/IfInvocation" }, @@ -41199,6 +41798,18 @@ { "$ref": "#/components/schemas/IdealSizeInvocation" }, + { + "$ref": "#/components/schemas/Ideogram4DenoiseInvocation" + }, + { + "$ref": "#/components/schemas/Ideogram4LatentsToImageInvocation" + }, + { + "$ref": "#/components/schemas/Ideogram4ModelLoaderInvocation" + }, + { + "$ref": "#/components/schemas/Ideogram4TextEncoderInvocation" + }, { "$ref": "#/components/schemas/IfInvocation" }, @@ -48135,18 +48746,184 @@ }, "base": { "type": "string", - "const": "flux", + "const": "flux", + "title": "Base", + "default": "flux" + }, + "format": { + "type": "string", + "const": "bnb_quantized_nf4b", + "title": "Format", + "default": "bnb_quantized_nf4b" + }, + "variant": { + "$ref": "#/components/schemas/FluxVariantType" + } + }, + "type": "object", + "required": [ + "key", + "hash", + "path", + "file_size", + "name", + "description", + "source", + "source_type", + "source_api_response", + "source_url", + "cover_image", + "type", + "trigger_phrases", + "default_settings", + "config_path", + "base", + "format", + "variant" + ], + "title": "Main_BnBNF4_FLUX_Config", + "description": "Model config for main checkpoint models." + }, + "Main_Checkpoint_Anima_Config": { + "properties": { + "key": { + "type": "string", + "title": "Key", + "description": "A unique key for this model." + }, + "hash": { + "type": "string", + "title": "Hash", + "description": "The hash of the model file(s)." + }, + "path": { + "type": "string", + "title": "Path", + "description": "Path to the model on the filesystem. Relative paths are relative to the Invoke root directory." + }, + "file_size": { + "type": "integer", + "title": "File Size", + "description": "The size of the model in bytes." + }, + "name": { + "type": "string", + "title": "Name", + "description": "Name of the model." + }, + "description": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Description", + "description": "Model description" + }, + "source": { + "type": "string", + "title": "Source", + "description": "The original source of the model (path, URL or repo_id)." + }, + "source_type": { + "$ref": "#/components/schemas/ModelSourceType", + "description": "The type of source" + }, + "source_api_response": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Source Api Response", + "description": "The original API response from the source, as stringified JSON." + }, + "source_url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Source Url", + "description": "Optional URL for the model (e.g. download page or model page)." + }, + "cover_image": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Cover Image", + "description": "Url for image to preview model" + }, + "type": { + "type": "string", + "const": "main", + "title": "Type", + "default": "main" + }, + "trigger_phrases": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array", + "uniqueItems": true + }, + { + "type": "null" + } + ], + "title": "Trigger Phrases", + "description": "Set of trigger phrases for this model" + }, + "default_settings": { + "anyOf": [ + { + "$ref": "#/components/schemas/MainModelDefaultSettings" + }, + { + "type": "null" + } + ], + "description": "Default settings for this model" + }, + "config_path": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Config Path", + "description": "Path to the config for this model, if any." + }, + "base": { + "type": "string", + "const": "anima", "title": "Base", - "default": "flux" + "default": "anima" }, "format": { "type": "string", - "const": "bnb_quantized_nf4b", + "const": "checkpoint", "title": "Format", - "default": "bnb_quantized_nf4b" - }, - "variant": { - "$ref": "#/components/schemas/FluxVariantType" + "default": "checkpoint" } }, "type": "object", @@ -48167,13 +48944,12 @@ "default_settings", "config_path", "base", - "format", - "variant" + "format" ], - "title": "Main_BnBNF4_FLUX_Config", - "description": "Model config for main checkpoint models." + "title": "Main_Checkpoint_Anima_Config", + "description": "Model config for Anima single-file checkpoint models (safetensors).\n\nAnima is built on NVIDIA Cosmos Predict2 DiT with a custom LLM Adapter\nthat bridges Qwen3 0.6B text encoder outputs to the DiT." }, - "Main_Checkpoint_Anima_Config": { + "Main_Checkpoint_FLUX_Config": { "properties": { "key": { "type": "string", @@ -48302,17 +49078,20 @@ "title": "Config Path", "description": "Path to the config for this model, if any." }, - "base": { - "type": "string", - "const": "anima", - "title": "Base", - "default": "anima" - }, "format": { "type": "string", "const": "checkpoint", "title": "Format", "default": "checkpoint" + }, + "base": { + "type": "string", + "const": "flux", + "title": "Base", + "default": "flux" + }, + "variant": { + "$ref": "#/components/schemas/FluxVariantType" } }, "type": "object", @@ -48332,13 +49111,14 @@ "trigger_phrases", "default_settings", "config_path", + "format", "base", - "format" + "variant" ], - "title": "Main_Checkpoint_Anima_Config", - "description": "Model config for Anima single-file checkpoint models (safetensors).\n\nAnima is built on NVIDIA Cosmos Predict2 DiT with a custom LLM Adapter\nthat bridges Qwen3 0.6B text encoder outputs to the DiT." + "title": "Main_Checkpoint_FLUX_Config", + "description": "Model config for main checkpoint models." }, - "Main_Checkpoint_FLUX_Config": { + "Main_Checkpoint_Flux2_Config": { "properties": { "key": { "type": "string", @@ -48475,12 +49255,12 @@ }, "base": { "type": "string", - "const": "flux", + "const": "flux2", "title": "Base", - "default": "flux" + "default": "flux2" }, "variant": { - "$ref": "#/components/schemas/FluxVariantType" + "$ref": "#/components/schemas/Flux2VariantType" } }, "type": "object", @@ -48504,10 +49284,10 @@ "base", "variant" ], - "title": "Main_Checkpoint_FLUX_Config", - "description": "Model config for main checkpoint models." + "title": "Main_Checkpoint_Flux2_Config", + "description": "Model config for FLUX.2 checkpoint models (e.g. Klein)." }, - "Main_Checkpoint_Flux2_Config": { + "Main_Checkpoint_QwenImage_Config": { "properties": { "key": { "type": "string", @@ -48636,20 +49416,27 @@ "title": "Config Path", "description": "Path to the config for this model, if any." }, + "base": { + "type": "string", + "const": "qwen-image", + "title": "Base", + "default": "qwen-image" + }, "format": { "type": "string", "const": "checkpoint", "title": "Format", "default": "checkpoint" }, - "base": { - "type": "string", - "const": "flux2", - "title": "Base", - "default": "flux2" - }, "variant": { - "$ref": "#/components/schemas/Flux2VariantType" + "anyOf": [ + { + "$ref": "#/components/schemas/QwenImageVariantType" + }, + { + "type": "null" + } + ] } }, "type": "object", @@ -48669,14 +49456,14 @@ "trigger_phrases", "default_settings", "config_path", - "format", "base", + "format", "variant" ], - "title": "Main_Checkpoint_Flux2_Config", - "description": "Model config for FLUX.2 checkpoint models (e.g. Klein)." + "title": "Main_Checkpoint_QwenImage_Config", + "description": "Model config for Qwen Image single-file checkpoint models (safetensors, etc).\n\nCovers both raw bf16/fp16 checkpoints and ComfyUI-style fp8_scaled checkpoints.\nThe loader dequantizes fp8 weights back to bf16 at load time; the\n`default_settings.fp8_storage` toggle can then optionally re-cast to fp8 for\nVRAM savings." }, - "Main_Checkpoint_QwenImage_Config": { + "Main_Checkpoint_SD1_Config": { "properties": { "key": { "type": "string", @@ -48805,27 +49592,23 @@ "title": "Config Path", "description": "Path to the config for this model, if any." }, - "base": { - "type": "string", - "const": "qwen-image", - "title": "Base", - "default": "qwen-image" - }, "format": { "type": "string", "const": "checkpoint", "title": "Format", "default": "checkpoint" }, + "prediction_type": { + "$ref": "#/components/schemas/SchedulerPredictionType" + }, "variant": { - "anyOf": [ - { - "$ref": "#/components/schemas/QwenImageVariantType" - }, - { - "type": "null" - } - ] + "$ref": "#/components/schemas/ModelVariantType" + }, + "base": { + "type": "string", + "const": "sd-1", + "title": "Base", + "default": "sd-1" } }, "type": "object", @@ -48845,14 +49628,14 @@ "trigger_phrases", "default_settings", "config_path", - "base", "format", - "variant" + "prediction_type", + "variant", + "base" ], - "title": "Main_Checkpoint_QwenImage_Config", - "description": "Model config for Qwen Image single-file checkpoint models (safetensors, etc).\n\nCovers both raw bf16/fp16 checkpoints and ComfyUI-style fp8_scaled checkpoints.\nThe loader dequantizes fp8 weights back to bf16 at load time; the\n`default_settings.fp8_storage` toggle can then optionally re-cast to fp8 for\nVRAM savings." + "title": "Main_Checkpoint_SD1_Config" }, - "Main_Checkpoint_SD1_Config": { + "Main_Checkpoint_SD2_Config": { "properties": { "key": { "type": "string", @@ -48995,9 +49778,9 @@ }, "base": { "type": "string", - "const": "sd-1", + "const": "sd-2", "title": "Base", - "default": "sd-1" + "default": "sd-2" } }, "type": "object", @@ -49022,9 +49805,9 @@ "variant", "base" ], - "title": "Main_Checkpoint_SD1_Config" + "title": "Main_Checkpoint_SD2_Config" }, - "Main_Checkpoint_SD2_Config": { + "Main_Checkpoint_SDXLRefiner_Config": { "properties": { "key": { "type": "string", @@ -49167,9 +49950,9 @@ }, "base": { "type": "string", - "const": "sd-2", + "const": "sdxl-refiner", "title": "Base", - "default": "sd-2" + "default": "sdxl-refiner" } }, "type": "object", @@ -49194,9 +49977,9 @@ "variant", "base" ], - "title": "Main_Checkpoint_SD2_Config" + "title": "Main_Checkpoint_SDXLRefiner_Config" }, - "Main_Checkpoint_SDXLRefiner_Config": { + "Main_Checkpoint_SDXL_Config": { "properties": { "key": { "type": "string", @@ -49339,9 +50122,9 @@ }, "base": { "type": "string", - "const": "sdxl-refiner", + "const": "sdxl", "title": "Base", - "default": "sdxl-refiner" + "default": "sdxl" } }, "type": "object", @@ -49366,9 +50149,9 @@ "variant", "base" ], - "title": "Main_Checkpoint_SDXLRefiner_Config" + "title": "Main_Checkpoint_SDXL_Config" }, - "Main_Checkpoint_SDXL_Config": { + "Main_Checkpoint_ZImage_Config": { "properties": { "key": { "type": "string", @@ -49497,23 +50280,20 @@ "title": "Config Path", "description": "Path to the config for this model, if any." }, + "base": { + "type": "string", + "const": "z-image", + "title": "Base", + "default": "z-image" + }, "format": { "type": "string", "const": "checkpoint", "title": "Format", "default": "checkpoint" }, - "prediction_type": { - "$ref": "#/components/schemas/SchedulerPredictionType" - }, "variant": { - "$ref": "#/components/schemas/ModelVariantType" - }, - "base": { - "type": "string", - "const": "sdxl", - "title": "Base", - "default": "sdxl" + "$ref": "#/components/schemas/ZImageVariantType" } }, "type": "object", @@ -49533,14 +50313,14 @@ "trigger_phrases", "default_settings", "config_path", + "base", "format", - "prediction_type", - "variant", - "base" + "variant" ], - "title": "Main_Checkpoint_SDXL_Config" + "title": "Main_Checkpoint_ZImage_Config", + "description": "Model config for Z-Image single-file checkpoint models (safetensors, etc)." }, - "Main_Checkpoint_ZImage_Config": { + "Main_Diffusers_CogView4_Config": { "properties": { "key": { "type": "string", @@ -49657,32 +50437,21 @@ ], "description": "Default settings for this model" }, - "config_path": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "title": "Config Path", - "description": "Path to the config for this model, if any." - }, - "base": { - "type": "string", - "const": "z-image", - "title": "Base", - "default": "z-image" - }, "format": { "type": "string", - "const": "checkpoint", + "const": "diffusers", "title": "Format", - "default": "checkpoint" + "default": "diffusers" }, - "variant": { - "$ref": "#/components/schemas/ZImageVariantType" + "repo_variant": { + "$ref": "#/components/schemas/ModelRepoVariant", + "default": "" + }, + "base": { + "type": "string", + "const": "cogview4", + "title": "Base", + "default": "cogview4" } }, "type": "object", @@ -49701,15 +50470,13 @@ "type", "trigger_phrases", "default_settings", - "config_path", - "base", "format", - "variant" + "repo_variant", + "base" ], - "title": "Main_Checkpoint_ZImage_Config", - "description": "Model config for Z-Image single-file checkpoint models (safetensors, etc)." + "title": "Main_Diffusers_CogView4_Config" }, - "Main_Diffusers_CogView4_Config": { + "Main_Diffusers_FLUX_Config": { "properties": { "key": { "type": "string", @@ -49838,9 +50605,12 @@ }, "base": { "type": "string", - "const": "cogview4", + "const": "flux", "title": "Base", - "default": "cogview4" + "default": "flux" + }, + "variant": { + "$ref": "#/components/schemas/FluxVariantType" } }, "type": "object", @@ -49861,11 +50631,13 @@ "default_settings", "format", "repo_variant", - "base" + "base", + "variant" ], - "title": "Main_Diffusers_CogView4_Config" + "title": "Main_Diffusers_FLUX_Config", + "description": "Model config for FLUX.1 models in diffusers format." }, - "Main_Diffusers_FLUX_Config": { + "Main_Diffusers_Flux2_Config": { "properties": { "key": { "type": "string", @@ -49994,12 +50766,12 @@ }, "base": { "type": "string", - "const": "flux", + "const": "flux2", "title": "Base", - "default": "flux" + "default": "flux2" }, "variant": { - "$ref": "#/components/schemas/FluxVariantType" + "$ref": "#/components/schemas/Flux2VariantType" } }, "type": "object", @@ -50023,10 +50795,10 @@ "base", "variant" ], - "title": "Main_Diffusers_FLUX_Config", - "description": "Model config for FLUX.1 models in diffusers format." + "title": "Main_Diffusers_Flux2_Config", + "description": "Model config for FLUX.2 models in diffusers format (e.g. FLUX.2 Klein)." }, - "Main_Diffusers_Flux2_Config": { + "Main_Diffusers_Ideogram4_Config": { "properties": { "key": { "type": "string", @@ -50155,12 +50927,9 @@ }, "base": { "type": "string", - "const": "flux2", + "const": "ideogram-4", "title": "Base", - "default": "flux2" - }, - "variant": { - "$ref": "#/components/schemas/Flux2VariantType" + "default": "ideogram-4" } }, "type": "object", @@ -50181,11 +50950,10 @@ "default_settings", "format", "repo_variant", - "base", - "variant" + "base" ], - "title": "Main_Diffusers_Flux2_Config", - "description": "Model config for FLUX.2 models in diffusers format (e.g. FLUX.2 Klein)." + "title": "Main_Diffusers_Ideogram4_Config", + "description": "Model config for Ideogram 4 diffusers models (nf4 / fp8 quantized).\n\nThe on-disk layout is a diffusers pipeline folder bundling two transformers\n(transformer/ + unconditional_transformer/), a Qwen3-VL text_encoder/ + tokenizer/,\nand a FLUX.2-style vae/. Quantization (nf4 vs fp8) lives inside the component folders\nand is detected by the loader, not here." }, "Main_Diffusers_QwenImage_Config": { "properties": { @@ -55814,6 +56582,9 @@ { "$ref": "#/components/schemas/Main_Diffusers_ZImage_Config" }, + { + "$ref": "#/components/schemas/Main_Diffusers_Ideogram4_Config" + }, { "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config" }, @@ -56389,6 +57160,9 @@ { "$ref": "#/components/schemas/Main_Diffusers_ZImage_Config" }, + { + "$ref": "#/components/schemas/Main_Diffusers_Ideogram4_Config" + }, { "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config" }, @@ -56849,6 +57623,9 @@ { "$ref": "#/components/schemas/Main_Diffusers_ZImage_Config" }, + { + "$ref": "#/components/schemas/Main_Diffusers_Ideogram4_Config" + }, { "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config" }, @@ -57159,6 +57936,9 @@ { "$ref": "#/components/schemas/Main_Diffusers_ZImage_Config" }, + { + "$ref": "#/components/schemas/Main_Diffusers_Ideogram4_Config" + }, { "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config" }, @@ -57918,6 +58698,9 @@ { "$ref": "#/components/schemas/Main_Diffusers_ZImage_Config" }, + { + "$ref": "#/components/schemas/Main_Diffusers_Ideogram4_Config" + }, { "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config" }, From 9b80a5ae4b59f210b1b9ad84e5c769e5d2cf1118 Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Mon, 20 Jul 2026 23:30:49 +0200 Subject: [PATCH 21/33] Chore Knit --- .../nodes/util/graph/generation/buildIdeogram4Prompt.ts | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts index 9a911f976d9..65e77855cea 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts @@ -23,7 +23,7 @@ const IDEOGRAM4_COORD_MAX = 1000; const clamp = (value: number, min: number, max: number): number => Math.min(max, Math.max(min, value)); -export type Ideogram4Bbox = [number, number, number, number]; +type Ideogram4Bbox = [number, number, number, number]; /** * Converts a region's rect (canvas/layer coordinates — the same space as the generation bbox) into an @@ -50,7 +50,7 @@ export type Ideogram4RegionInput = { /** An `obj`-type element. Key order matches the training schema: `type`, `bbox`, `desc`. */ type Ideogram4Element = { type: 'obj'; bbox: Ideogram4Bbox; desc: string } | { type: 'obj'; desc: string }; -export type Ideogram4PromptResult = { +type Ideogram4PromptResult = { /** The final prompt string to feed to the text encoder. */ prompt: string; /** From 5bef7c3350ab884e7ed9a3d4d057328988e9c0c3 Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Mon, 20 Jul 2026 23:34:31 +0200 Subject: [PATCH 22/33] fix(deps): regenerate uv.lock to remove duplicate packages from bad merge --- uv.lock | 1544 ++++++++++++++++++++++--------------------------------- 1 file changed, 617 insertions(+), 927 deletions(-) diff --git a/uv.lock b/uv.lock index 07e4985a20f..9d83197f616 100644 --- a/uv.lock +++ b/uv.lock @@ -23,11 +23,11 @@ overrides = [{ name = "opencv-python", marker = "sys_platform == 'never'" }] [[package]] name = "absl-py" -version = "2.4.0" +version = "2.5.0" source = { registry = "https://pypi.org/simple" } -sdist 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"https://files.pythonhosted.org/packages/42/63/3eb25da41049d20ae18fcab2dd8b056e02387c4bfa626cbdfb7c3b872e4f/wrapt-2.2.2-cp312-cp312-win32.whl", hash = "sha256:ef2cce266b5b0b07e19fa82e59673b81142b7a3607c8ed1254113d048ed668da", size = 77734, upload-time = "2026-06-20T23:48:11.769Z" }, { url = "https://files.pythonhosted.org/packages/da/09/0390e008a305360948fa9ce69507d041ac12cb2ee5d28e34467e2ee79391/wrapt-2.2.2-cp312-cp312-win_amd64.whl", hash = "sha256:abf8c20a2d72ee69e16328b3c91342c446e723bfe48bfcc4dded3b9722ac027f", size = 80585, upload-time = "2026-06-20T23:48:13.117Z" }, From e6438e4435fbf7d9ba1e140251f6139265774825 Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Mon, 20 Jul 2026 23:39:26 +0200 Subject: [PATCH 23/33] fix(ideogram4): make bitsandbytes import lazy in quantized_loading bitsandbytes has no macOS wheels and is excluded on darwin, but the module-level import broke test collection on macOS CI. Move the import into the two bnb-only functions and a TYPE_CHECKING block so the fp8 path imports without bitsandbytes installed. --- invokeai/backend/ideogram4/quantized_loading.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/invokeai/backend/ideogram4/quantized_loading.py b/invokeai/backend/ideogram4/quantized_loading.py index 7440de043bf..2acb1b6777d 100644 --- a/invokeai/backend/ideogram4/quantized_loading.py +++ b/invokeai/backend/ideogram4/quantized_loading.py @@ -1,12 +1,15 @@ from __future__ import annotations import warnings +from typing import TYPE_CHECKING -import bitsandbytes as bnb import torch import torch.nn as nn import torch.nn.functional as F +if TYPE_CHECKING: + import bitsandbytes as bnb + _BNB_SIBLING_SUFFIXES = ( ".absmax", ".quant_map", @@ -36,6 +39,8 @@ def swap_linears_to_bnb4bit( quant_type: str = "nf4", compress_statistics: bool = False, ) -> None: + import bitsandbytes as bnb + for name, child in list(module.named_children()): if isinstance(child, nn.Linear): new_linear = bnb.nn.Linear4bit( @@ -62,6 +67,8 @@ def load_bnb4bit_state_dict( device: torch.device, dtype: torch.dtype, ) -> None: + import bitsandbytes as bnb + consumed: set[str] = set() for full_name, tensor in state_dict.items(): if ".quant_state." in full_name or full_name.endswith(_BNB_SIBLING_SUFFIXES): From a3f1d4cc0a30d9c7f4c0436f25b18428cf91843c Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Mon, 20 Jul 2026 23:41:10 +0200 Subject: [PATCH 24/33] fix(ideogram4): make bitsandbytes import lazy in quantized_loading bitsandbytes has no macOS wheels and is excluded on darwin, but the module-level import broke test collection on macOS CI. Move the import into the two bnb-only functions and a TYPE_CHECKING block so the fp8 path imports without bitsandbytes installed. --- invokeai/backend/ideogram4/quantized_loading.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/invokeai/backend/ideogram4/quantized_loading.py b/invokeai/backend/ideogram4/quantized_loading.py index 2acb1b6777d..1b710293bde 100644 --- a/invokeai/backend/ideogram4/quantized_loading.py +++ b/invokeai/backend/ideogram4/quantized_loading.py @@ -8,7 +8,7 @@ import torch.nn.functional as F if TYPE_CHECKING: - import bitsandbytes as bnb + pass _BNB_SIBLING_SUFFIXES = ( ".absmax", From a4ab7b600281e07dccdc2d3faa11037a5975e88b Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Sat, 25 Jul 2026 05:37:55 +0200 Subject: [PATCH 25/33] Fix: address ideogram4 review (strict load, 1-step guidance, i18n, runtime caption) - Non-fp8 Ideogram 4 text-encoder load now validates the state dict: unexpected keys raise, missing keys warn (mirrors the fp8 helper) instead of silently accepting a partial load. - Guidance schedule: cap the polish tail at num_steps-1 so at least one main step always remains (the guidance_scale override was silently dropped at num_steps=1), and require steps >= 2 (backend field + frontend slider/marks). - Localize the Ideogram sampler-preset option labels via t() with the step count interpolated; add the three preset i18n keys. - Assemble the structured JSON caption at generation time in a new ideogram4_caption_builder node (Python port of buildIdeogram4Caption) instead of at graph-build time. The graph now wires the real prompt node -> caption builder -> text encoder and returns it as positivePrompt, so dynamic prompts / prompt batching vary the encoded caption (the decoy that dropped them is removed). The builder's output is wired to a new declared ideogram4_caption metadata field via an edge, so each batched image records its actual caption. Regenerates schema.ts for the new node + metadata field. Adds tests for caption assembly, the guidance schedule, and the graph wiring. --- invokeai/app/invocations/ideogram4_caption.py | 61 +++++++ invokeai/app/invocations/ideogram4_denoise.py | 13 +- invokeai/app/invocations/metadata.py | 7 + invokeai/backend/ideogram4/caption.py | 76 +++++++++ .../load/model_loaders/ideogram4.py | 11 +- invokeai/frontend/web/public/locales/en.json | 5 + .../generation/buildIdeogram4Graph.test.ts | 157 ++++++++++++++++++ .../graph/generation/buildIdeogram4Graph.ts | 50 +++--- .../generation/buildIdeogram4Prompt.test.ts | 101 +---------- .../graph/generation/buildIdeogram4Prompt.ts | 99 +++-------- .../Core/ParamIdeogram4SamplerPreset.tsx | 19 ++- .../components/Core/ParamIdeogram4Steps.tsx | 6 +- .../frontend/web/src/services/api/schema.ts | 88 +++++++++- tests/backend/ideogram4/test_caption.py | 70 ++++++++ .../ideogram4/test_guidance_schedule.py | 48 ++++++ 15 files changed, 600 insertions(+), 211 deletions(-) create mode 100644 invokeai/app/invocations/ideogram4_caption.py create mode 100644 invokeai/backend/ideogram4/caption.py create mode 100644 invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.test.ts create mode 100644 tests/backend/ideogram4/test_caption.py create mode 100644 tests/backend/ideogram4/test_guidance_schedule.py diff --git a/invokeai/app/invocations/ideogram4_caption.py b/invokeai/app/invocations/ideogram4_caption.py new file mode 100644 index 00000000000..2eb3661ee66 --- /dev/null +++ b/invokeai/app/invocations/ideogram4_caption.py @@ -0,0 +1,61 @@ +from typing import Optional + +from pydantic import BaseModel, Field + +from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation +from invokeai.app.invocations.fields import InputField, UIComponent +from invokeai.app.invocations.primitives import StringOutput +from invokeai.app.services.shared.invocation_context import InvocationContext +from invokeai.backend.ideogram4.caption import build_ideogram4_caption + + +class Ideogram4Region(BaseModel): + """A single region of an Ideogram 4 structured caption (description + optional bounding box).""" + + prompt: str = Field(description="The region's description (becomes the element's `desc`).") + bbox: Optional[list[int]] = Field( + default=None, + description="Normalized bounding box [y_min, x_min, y_max, x_max] (0–1000), or null for a region " + "with no drawn content.", + ) + + +@invocation( + "ideogram4_caption_builder", + title="Caption Builder - Ideogram 4", + tags=["prompt", "ideogram4"], + category="conditioning", + version="1.0.0", + classification=Classification.Prototype, +) +class Ideogram4CaptionBuilderInvocation(BaseInvocation): + """Assembles the Ideogram 4 structured JSON caption at generation time. + + The caption is built here (not in the graph builder) so the batch-injectable global `prompt` — which + dynamic prompts and prompt batching vary — is folded into the encoded caption. The regions and color + palette are fixed per generation and supplied as inputs. If the prompt is already a JSON object it is + passed through verbatim; with no regions or palette it falls back to the plain prompt. + """ + + prompt: str = InputField( + default="", + description="The global prompt (becomes `high_level_description`, or is used verbatim if it is " + "already a JSON caption, or as plain text).", + ui_component=UIComponent.Textarea, + ) + regions: list[Ideogram4Region] = InputField( + default=[], + description="Regional descriptions and bounding boxes assembled from Canvas Regional Guidance layers.", + ) + color_palette: list[str] = InputField( + default=[], + description="Optional color palette as hex colors (#RRGGBB).", + ) + + def invoke(self, context: InvocationContext) -> StringOutput: + caption = build_ideogram4_caption( + self.prompt, + [(region.prompt, region.bbox) for region in self.regions], + self.color_palette, + ) + return StringOutput(value=caption) diff --git a/invokeai/app/invocations/ideogram4_denoise.py b/invokeai/app/invocations/ideogram4_denoise.py index d75ed09a026..9866f37b210 100644 --- a/invokeai/app/invocations/ideogram4_denoise.py +++ b/invokeai/app/invocations/ideogram4_denoise.py @@ -32,13 +32,19 @@ def _effective_guidance_schedule( (index 0 = the final/polish step). A ``guidance_scale`` override replaces the main weight while the preset's polish tail is preserved; a changed step count rescales the polish tail proportionally (always keeping at least one polish and one main step). + + ``num_steps`` must be >= 2 (enforced by the invocation's ``steps`` field) so both a polish and a + main step always exist — otherwise a single step would be all-polish and silently drop the + ``guidance_scale`` override. """ polish_gw = base_schedule[0] main_gw = float(guidance_scale) if guidance_scale is not None else float(base_schedule[-1]) if num_steps == preset_num_steps and guidance_scale is None: return base_schedule n_polish_base = sum(1 for gw in base_schedule if gw == base_schedule[0]) - polish_count = min(num_steps, max(1, round(n_polish_base * num_steps / preset_num_steps))) + # Cap the polish tail at num_steps - 1 so at least one main step always remains and the + # guidance_scale override is never silently dropped. + polish_count = max(1, min(round(n_polish_base * num_steps / preset_num_steps), num_steps - 1)) main_count = num_steps - polish_count return (polish_gw,) * polish_count + (main_gw,) * main_count @@ -71,9 +77,10 @@ class Ideogram4DenoiseInvocation(BaseInvocation): # Optional advanced overrides of the sampler preset. None = use the preset's value. steps: Optional[int] = InputField( default=None, - ge=1, + ge=2, le=100, - description="Override the preset's step count. Leave empty to use the preset.", + description="Override the preset's step count (minimum 2, so a polish and a main step both " + "exist). Leave empty to use the preset.", ) guidance_scale: Optional[float] = InputField( default=None, diff --git a/invokeai/app/invocations/metadata.py b/invokeai/app/invocations/metadata.py index 046e65954d3..a2baea7f81b 100644 --- a/invokeai/app/invocations/metadata.py +++ b/invokeai/app/invocations/metadata.py @@ -238,6 +238,13 @@ class CoreMetadataInvocation(BaseInvocation): default=None, description="The Qwen3 text encoder model used for Z-Image inference", ) + # Ideogram 4 assembles its structured JSON caption at generation time (ideogram4_caption_builder), + # so this is a declared field rather than a static extra: the graph wires the builder's output to it + # via an edge, capturing the exact caption encoded for each (possibly batched) image. + ideogram4_caption: Optional[str] = InputField( + default=None, + description="The structured JSON caption encoded for Ideogram 4 inference", + ) # High resolution fix metadata. hrf_enabled: Optional[bool] = InputField( diff --git a/invokeai/backend/ideogram4/caption.py b/invokeai/backend/ideogram4/caption.py new file mode 100644 index 00000000000..cb5db9af2c8 --- /dev/null +++ b/invokeai/backend/ideogram4/caption.py @@ -0,0 +1,76 @@ +"""Runtime assembly of Ideogram 4's structured JSON caption. + +This is the Python port of the frontend ``buildIdeogram4Caption`` (buildIdeogram4Prompt.ts). It runs +at generation time (inside the ``ideogram4_caption_builder`` node) rather than at graph-build time, so +that dynamic-prompt expansions and prompt batching — which vary the *global* prompt — are reflected in +the encoded caption. The regions (description + bbox) and color palette are fixed per generation and +are passed in as node inputs. + +Schema notes (must match the reference the model was trained on): + - bbox is ``[y_min, x_min, y_max, x_max]``, normalized to 0–1000, origin top-left. The graph builder + computes it from canvas coordinates; here it is passed through verbatim. + - Key order matters: ``high_level_description``, (``style_description``), ``compositional_deconstruction``; + ``obj`` elements use ``type``, ``bbox``, ``desc``. Python dicts preserve insertion order. + - Serialized with compact separators and non-ASCII preserved (matches JS ``JSON.stringify`` and the + reference's ``ensure_ascii=False``). +""" + +import json +import re +from typing import Optional + +_HEX_COLOR_RE = re.compile(r"#[0-9A-F]{6}") + + +def build_ideogram4_caption( + global_prompt: str, + regions: list[tuple[str, Optional[list[int]]]], + color_palette: list[str], +) -> str: + """Assemble the Ideogram 4 prompt from a global prompt, regions, and an optional color palette. + + - Raw-JSON passthrough: if the trimmed global prompt already starts with ``{`` it is returned + verbatim (the user controls the JSON; regions/palette are ignored). + - With regions and/or a palette: a structured JSON caption is built. + - Otherwise: the plain (trimmed) global prompt is returned — the model accepts plain text. + + ``regions`` is a list of ``(description, bbox)`` where bbox is ``[y_min, x_min, y_max, x_max]`` or + None. Regions with a blank description are dropped. + """ + trimmed = global_prompt.strip() + + # The user pasted a structured caption (or any JSON object) — use it verbatim. + if trimmed.startswith("{"): + return global_prompt + + elements: list[dict] = [] + for description, bbox in regions: + if not description.strip(): + continue + if bbox is not None: + elements.append({"type": "obj", "bbox": bbox, "desc": description}) + else: + elements.append({"type": "obj", "desc": description}) + + # Normalize the palette to uppercase #RRGGBB (the schema's required hex form); drop invalid entries. + # Mirror the JS order: uppercase first, then validate. + palette = [c.upper() for c in color_palette if _HEX_COLOR_RE.fullmatch(c.upper())] + + # Nothing structured to encode — fall back to the plain prompt (documented to work). + if not elements and not palette: + return trimmed + + compositional_deconstruction = {"background": "", "elements": elements} + if palette: + caption = { + "high_level_description": trimmed, + "style_description": {"color_palette": palette}, + "compositional_deconstruction": compositional_deconstruction, + } + else: + caption = { + "high_level_description": trimmed, + "compositional_deconstruction": compositional_deconstruction, + } + + return json.dumps(caption, separators=(",", ":"), ensure_ascii=False) diff --git a/invokeai/backend/model_manager/load/model_loaders/ideogram4.py b/invokeai/backend/model_manager/load/model_loaders/ideogram4.py index c02bea2dc3f..1ae8e2a7cad 100644 --- a/invokeai/backend/model_manager/load/model_loaders/ideogram4.py +++ b/invokeai/backend/model_manager/load/model_loaders/ideogram4.py @@ -12,6 +12,7 @@ """ import json +import warnings from pathlib import Path from typing import Optional @@ -175,7 +176,15 @@ def _load_text_encoder(self, model_path: Path) -> AnyModel: if is_bnb_nf4: model = quantize_model_nf4(model, modules_to_not_convert=set(), compute_dtype=compute_dtype) - model.load_state_dict(sd, strict=False, assign=True) + missing, unexpected = model.load_state_dict(sd, strict=False, assign=True) + # Mirror the fp8 helper's policy (load_fp8_state_dict): unexpected keys signal a wrong or + # contaminated checkpoint and must hard-fail; missing keys are expected only for tied weights + # that transformers re-ties itself, so downgrade those to a warning rather than silently + # accepting a partial load that would fail later during encoding. + if unexpected: + raise RuntimeError(f"unexpected keys loading Ideogram 4 text encoder: {unexpected[:10]}") + if missing: + warnings.warn(f"missing keys loading Ideogram 4 text encoder: {missing[:10]}", stacklevel=2) if not is_bnb_nf4: model = model.to(compute_dtype) model.eval() diff --git a/invokeai/frontend/web/public/locales/en.json b/invokeai/frontend/web/public/locales/en.json index 2e4578ea50b..45c29fc9ae4 100644 --- a/invokeai/frontend/web/public/locales/en.json +++ b/invokeai/frontend/web/public/locales/en.json @@ -1635,6 +1635,11 @@ "samplerPreset": "Sampler Preset", "colorPalette": "Color Palette", "ideogram4Caption": "Structured Caption", + "ideogram4SamplerPresets": { + "quality": "Quality ({{steps}} steps)", + "default": "Default ({{steps}} steps)", + "turbo": "Turbo ({{steps}} steps)" + }, "duration": "Duration", "lockAspectRatio": "Lock Aspect Ratio", "swapDimensions": "Swap Dimensions", diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.test.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.test.ts new file mode 100644 index 00000000000..dd17c6514b8 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.test.ts @@ -0,0 +1,157 @@ +import { afterEach, describe, expect, it, vi } from 'vitest'; + +vi.mock('app/logging/logger', () => ({ + logger: () => ({ debug: vi.fn() }), +})); + +let nextId = 0; +vi.mock('features/controlLayers/konva/util', () => ({ + getPrefixedId: (prefix: string) => `${prefix}:${nextId++}`, +})); + +const model = { key: 'ideogram4-model', hash: 'ideogram4-hash', name: 'Ideogram 4', base: 'ideogram-4', type: 'main' }; + +// Controlled prompt inputs — the structured case (regions + palette) is the one that used to be +// clobbered by the decoy. globalPrompt is what the linear batch injects into. +let promptInputs = { + globalPrompt: 'a global prompt', + regions: [{ prompt: 'a red bird', bbox: [10, 20, 300, 400] as [number, number, number, number] }], + colorPalette: ['#FF0000'], +}; + +vi.mock('features/controlLayers/store/paramsSlice', () => ({ + selectMainModelConfig: vi.fn(() => model), + selectIdeogram4SamplerPreset: vi.fn(() => 'V4_QUALITY_48'), + selectIdeogram4Steps: vi.fn(() => null), + selectIdeogram4GuidanceScale: vi.fn(() => null), + selectIdeogram4Mu: vi.fn(() => null), +})); + +vi.mock('features/controlLayers/store/selectors', () => ({ + selectCanvasMetadata: vi.fn(() => ({})), +})); + +vi.mock('features/metadata/util/modelFetchingHelpers', () => ({ + fetchModelConfigWithTypeGuard: vi.fn(() => Promise.resolve(model)), +})); + +vi.mock('features/nodes/util/graph/generation/addNSFWChecker', () => ({ + addNSFWChecker: vi.fn((_g, node) => node), +})); + +vi.mock('features/nodes/util/graph/generation/addWatermarker', () => ({ + addWatermarker: vi.fn((_g, node) => node), +})); + +vi.mock('features/nodes/util/graph/generation/buildIdeogram4Prompt', () => ({ + collectIdeogram4PromptInputs: vi.fn(() => promptInputs), +})); + +vi.mock('features/nodes/util/graph/graphBuilderUtils', () => ({ + getOriginalAndScaledSizesForTextToImage: vi.fn(() => ({ + originalSize: { width: 1024, height: 1024 }, + scaledSize: { width: 1024, height: 1024 }, + })), + selectCanvasOutputFields: vi.fn(() => ({})), +})); + +vi.mock('features/ui/store/uiSelectors', () => ({ + selectActiveTab: vi.fn(() => 'generation'), +})); + +vi.mock('services/api/types', async () => { + const actual = await vi.importActual('services/api/types'); + return { ...actual, isNonRefinerMainModelConfig: vi.fn(() => true) }; +}); + +import { buildIdeogram4Graph } from './buildIdeogram4Graph'; + +const state = { system: { shouldUseNSFWChecker: false, shouldUseWatermarker: false } }; + +// eslint-disable-next-line @typescript-eslint/no-explicit-any +const buildArg = (): any => ({ generationMode: 'txt2img', state, manager: null }); + +describe('buildIdeogram4Graph', () => { + afterEach(() => { + nextId = 0; + promptInputs = { + globalPrompt: 'a global prompt', + regions: [{ prompt: 'a red bird', bbox: [10, 20, 300, 400] }], + colorPalette: ['#FF0000'], + }; + }); + + it('routes the batch-injectable prompt node through the caption builder into the text encoder', async () => { + const { g, positivePrompt } = await buildIdeogram4Graph(buildArg()); + + // The returned positivePrompt (the node the linear batch / dynamic prompts inject into) is the real + // prompt node — NOT a decoy — so injected expansions reach the encoder instead of a throwaway node. + expect(positivePrompt.type).toBe('string'); + expect(positivePrompt.id).toContain('ideogram4_prompt'); + expect(positivePrompt.id).not.toContain('decoy'); + expect(positivePrompt.value).toBe('a global prompt'); + + const nodes = g.getNodes(); + const captionBuilder = nodes.find((n) => n.type === 'ideogram4_caption_builder'); + const textEncoder = nodes.find((n) => n.type === 'ideogram4_text_encoder'); + expect(captionBuilder).toBeDefined(); + expect(textEncoder).toBeDefined(); + + const edges = g.getEdges(); + // prompt node -> caption builder (so an injected prompt is what the caption is assembled from) + expect( + edges.some( + (e) => + e.source.node_id === positivePrompt.id && + e.source.field === 'value' && + e.destination.node_id === captionBuilder!.id && + e.destination.field === 'prompt' + ) + ).toBe(true); + // caption builder -> text encoder (the assembled caption is what actually gets encoded) + expect( + edges.some( + (e) => + e.source.node_id === captionBuilder!.id && + e.source.field === 'value' && + e.destination.node_id === textEncoder!.id && + e.destination.field === 'prompt' + ) + ).toBe(true); + }); + + it('bakes the fixed regions and palette into the caption builder node', async () => { + const { g } = await buildIdeogram4Graph(buildArg()); + // eslint-disable-next-line @typescript-eslint/no-explicit-any + const captionBuilder = g.getNodes().find((n) => n.type === 'ideogram4_caption_builder') as any; + expect(captionBuilder.regions).toEqual([{ prompt: 'a red bird', bbox: [10, 20, 300, 400] }]); + expect(captionBuilder.color_palette).toEqual(['#FF0000']); + }); + + it('records the runtime-assembled caption in metadata via an edge (structured inputs)', async () => { + const { g } = await buildIdeogram4Graph(buildArg()); + const captionBuilder = g.getNodes().find((n) => n.type === 'ideogram4_caption_builder'); + const metadataNode = g.getNodes().find((n) => n.type === 'core_metadata'); + expect(metadataNode).toBeDefined(); + // The caption is wired from the runtime builder into metadata, so each batch item records its own. + expect( + g.getEdges().some( + (e) => + e.source.node_id === captionBuilder!.id && + e.source.field === 'value' && + e.destination.node_id === metadataNode!.id && + e.destination.field === 'ideogram4_caption' + ) + ).toBe(true); + }); + + it('does not record a caption edge when there are no structured inputs (plain prompt)', async () => { + promptInputs = { globalPrompt: 'just plain text', regions: [], colorPalette: [] }; + const { g } = await buildIdeogram4Graph(buildArg()); + const edges = g.getEdges(); + expect(edges.some((e) => e.destination.field === 'ideogram4_caption')).toBe(false); + // The prompt still flows through the caption builder (which passes plain text through at runtime). + const captionBuilder = g.getNodes().find((n) => n.type === 'ideogram4_caption_builder'); + expect(captionBuilder).toBeDefined(); + }); +}); diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts index 51af14c84fd..85fb8aaae52 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts @@ -2,7 +2,6 @@ import { objectEquals } from '@observ33r/object-equals'; import { logger } from 'app/logging/logger'; import { getPrefixedId } from 'features/controlLayers/konva/util'; import { - selectIdeogram4ColorPalette, selectIdeogram4GuidanceScale, selectIdeogram4Mu, selectIdeogram4SamplerPreset, @@ -13,7 +12,7 @@ import { selectCanvasMetadata } from 'features/controlLayers/store/selectors'; import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers'; import { addNSFWChecker } from 'features/nodes/util/graph/generation/addNSFWChecker'; import { addWatermarker } from 'features/nodes/util/graph/generation/addWatermarker'; -import { buildIdeogram4Prompt } from 'features/nodes/util/graph/generation/buildIdeogram4Prompt'; +import { collectIdeogram4PromptInputs } from 'features/nodes/util/graph/generation/buildIdeogram4Prompt'; import { Graph } from 'features/nodes/util/graph/generation/Graph'; import { getOriginalAndScaledSizesForTextToImage, @@ -45,16 +44,18 @@ export const buildIdeogram4Graph = async (arg: GraphBuilderArg): Promise 0 || colorPalette.length > 0; const g = new Graph(getPrefixedId('ideogram4_graph')); @@ -64,10 +65,20 @@ export const buildIdeogram4Graph = async (arg: GraphBuilderArg): Promise = isStructured - ? g.addNode({ id: getPrefixedId('positive_prompt_decoy'), type: 'string' }) - : promptNode; + // Return the real prompt node so the linear batch injects dynamic-prompt expansions into it; they + // flow through the caption builder into the encoder. `positive_prompt` metadata is the global prompt. + const positivePrompt: Invocation<'string'> = promptNode; const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig); assert(modelConfig.base === 'ideogram-4'); @@ -131,9 +139,11 @@ export const buildIdeogram4Graph = async (arg: GraphBuilderArg): Promise { const genBbox: Rect = { x: 0, y: 0, width: 1024, height: 1024 }; @@ -31,97 +34,3 @@ describe('rectToIdeogram4Bbox', () => { expect(rectToIdeogram4Bbox(regionRect, { x: 0, y: 0, width: 0, height: 0 })).toEqual([0, 0, 0, 0]); }); }); - -describe('buildIdeogram4Caption', () => { - const region = (prompt: string, bbox: Ideogram4RegionInput['bbox']): Ideogram4RegionInput => ({ prompt, bbox }); - - it('passes through a raw JSON object verbatim and marks it structured', () => { - const raw = '{"high_level_description":"a cat","compositional_deconstruction":{"background":"","elements":[]}}'; - expect(buildIdeogram4Caption(raw, [])).toEqual({ prompt: raw, isStructured: true }); - }); - - it('passes through raw JSON even when leading/trailing whitespace is present', () => { - const raw = ' {"a":1} '; - expect(buildIdeogram4Caption(raw, [])).toEqual({ prompt: raw, isStructured: true }); - }); - - it('returns plain text (not structured) when there are no regions', () => { - expect(buildIdeogram4Caption('a golden retriever on a skateboard', [])).toEqual({ - prompt: 'a golden retriever on a skateboard', - isStructured: false, - }); - }); - - it('trims the plain prompt', () => { - expect(buildIdeogram4Caption(' hello world ', [])).toEqual({ prompt: 'hello world', isStructured: false }); - }); - - it('assembles a structured caption from regions with correct key order', () => { - const result = buildIdeogram4Caption('A dog and a ball.', [ - region('a fluffy dog', [200, 300, 800, 900]), - region('a red ball', [250, 750, 750, 950]), - ]); - expect(result.isStructured).toBe(true); - // Key order must be exactly: high_level_description, compositional_deconstruction{background, elements}; - // each obj element: type, bbox, desc. JSON.stringify preserves insertion order, so assert on the raw string. - expect(result.prompt).toBe( - '{"high_level_description":"A dog and a ball.",' + - '"compositional_deconstruction":{"background":"",' + - '"elements":[' + - '{"type":"obj","bbox":[200,300,800,900],"desc":"a fluffy dog"},' + - '{"type":"obj","bbox":[250,750,750,950],"desc":"a red ball"}' + - ']}}' - ); - }); - - it('omits bbox for regions with no drawn content', () => { - const result = buildIdeogram4Caption('scene', [region('floating element', null)]); - expect(result.prompt).toContain('{"type":"obj","desc":"floating element"}'); - }); - - it('skips regions with empty prompts', () => { - const result = buildIdeogram4Caption('scene', [region(' ', [0, 0, 100, 100]), region('kept', [1, 2, 3, 4])]); - const parsed = JSON.parse(result.prompt); - expect(parsed.compositional_deconstruction.elements).toHaveLength(1); - expect(parsed.compositional_deconstruction.elements[0].desc).toBe('kept'); - }); - - it('preserves non-ASCII characters without escaping', () => { - const result = buildIdeogram4Caption('café', [region('a café ☕', [0, 0, 100, 100])]); - expect(result.prompt).toContain('café ☕'); - expect(result.prompt).not.toContain('\\u'); - }); - - it('injects a color palette as style_description.color_palette with correct key order', () => { - const result = buildIdeogram4Caption('a sunset', [region('a boat', [0, 0, 500, 500])], ['#FF6B35', '#004E89']); - expect(result.isStructured).toBe(true); - expect(result.prompt).toBe( - '{"high_level_description":"a sunset",' + - '"style_description":{"color_palette":["#FF6B35","#004E89"]},' + - '"compositional_deconstruction":{"background":"","elements":[{"type":"obj","bbox":[0,0,500,500],"desc":"a boat"}]}}' - ); - }); - - it('builds a structured caption from a palette alone (no regions)', () => { - const result = buildIdeogram4Caption('a sunset', [], ['#FF6B35']); - expect(result.isStructured).toBe(true); - const parsed = JSON.parse(result.prompt); - expect(parsed.style_description.color_palette).toEqual(['#FF6B35']); - expect(parsed.compositional_deconstruction.elements).toEqual([]); - }); - - it('uppercases palette colors and drops invalid hex (shorthand, names)', () => { - const result = buildIdeogram4Caption('x', [], ['#ff6b35', 'red', '#FFF', '#00cc88']); - const parsed = JSON.parse(result.prompt); - expect(parsed.style_description.color_palette).toEqual(['#FF6B35', '#00CC88']); - }); - - it('ignores the palette for raw-JSON passthrough', () => { - const raw = '{"a":1}'; - expect(buildIdeogram4Caption(raw, [], ['#FF6B35'])).toEqual({ prompt: raw, isStructured: true }); - }); - - it('stays plain text when there are no regions and no palette', () => { - expect(buildIdeogram4Caption('just text', [])).toEqual({ prompt: 'just text', isStructured: false }); - }); -}); diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts index 65e77855cea..28d73ebee69 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts @@ -6,16 +6,14 @@ import type { Rect } from 'features/controlLayers/store/types'; /** * Ideogram 4 is prompted with a structured JSON caption describing the scene as a list of regions, - * each with a bounding box and a description. This module assembles that caption from InvokeAI's - * Canvas Regional Guidance layers — the bbox numbers live inside the prompt string (Ideogram 4 does - * not use spatial attention masks), so this is pure string assembly with no backend mask handling. + * each with a bounding box and a description. The bbox numbers live inside the prompt string (Ideogram 4 + * does not use spatial attention masks). * - * See the reference prompting guide for the schema; the key points used here: - * - bbox is `[y_min, x_min, y_max, x_max]`, normalized to 0–1000, origin at top-left. - * - Key order matters (the model was trained on a consistent order). `obj` elements use - * `type`, `bbox`, `desc`. We rely on JS object insertion order being preserved by JSON.stringify. - * - The reference serializes with compact separators and `ensure_ascii=False`; JSON.stringify - * already produces compact `,`/`:` separators and preserves non-ASCII characters. + * This module reads the raw inputs (global prompt, per-region description + bbox, color palette) from + * canvas state. The actual JSON assembly happens at generation time in the backend + * `ideogram4_caption_builder` node (see backend `build_ideogram4_caption`) so that dynamic-prompt + * expansions and prompt batching — which vary the global prompt — are folded into the encoded caption. + * Only the bbox normalization stays here, since it needs the canvas manager / generation bbox. */ /** Ideogram 4 normalizes spatial coordinates to a 0–1000 grid with the origin at the top-left. */ @@ -47,80 +45,31 @@ export type Ideogram4RegionInput = { bbox: Ideogram4Bbox | null; }; -/** An `obj`-type element. Key order matches the training schema: `type`, `bbox`, `desc`. */ -type Ideogram4Element = { type: 'obj'; bbox: Ideogram4Bbox; desc: string } | { type: 'obj'; desc: string }; - -type Ideogram4PromptResult = { - /** The final prompt string to feed to the text encoder. */ - prompt: string; - /** - * Whether the prompt is a structured caption (assembled JSON or raw-JSON passthrough). When true, - * the graph builder must NOT let the linear batch inject the raw positive prompt over it, otherwise - * the assembled caption would be clobbered by the plain prompt text. - */ - isStructured: boolean; -}; - -/** - * Assembles an Ideogram 4 prompt from a global prompt, a set of regions, and an optional color palette. - * - * - Raw-JSON passthrough: if the global prompt is already a JSON object (trimmed, starts with `{`), it - * is used verbatim and treated as structured (the palette is ignored — the user controls the JSON). - * - With regions and/or a color palette: a structured JSON caption is built — the global prompt becomes - * `high_level_description`, each region becomes an `obj` element, and the palette (if any) becomes - * `style_description.color_palette`. - * - Otherwise: the plain global prompt is returned (the model accepts plain text). This keeps dynamic - * prompts and prompt batching working, since the caller can let the batch inject it directly. - */ -export const buildIdeogram4Caption = ( - globalPrompt: string, - regions: Ideogram4RegionInput[], - colorPalette: string[] = [] -): Ideogram4PromptResult => { - const trimmed = globalPrompt.trim(); - - // The user pasted a structured caption (or any JSON object) — use it verbatim. - if (trimmed.startsWith('{')) { - return { prompt: globalPrompt, isStructured: true }; - } - - const elements: Ideogram4Element[] = regions - .filter((region) => region.prompt.trim().length > 0) - .map((region) => - region.bbox ? { type: 'obj', bbox: region.bbox, desc: region.prompt } : { type: 'obj', desc: region.prompt } - ); - - // Normalize the palette to uppercase #RRGGBB (the schema's required hex form); drop invalid entries. - const palette = colorPalette.map((c) => c.toUpperCase()).filter((c) => /^#[0-9A-F]{6}$/.test(c)); - - // Nothing structured to encode — fall back to the plain prompt (documented to work). - if (elements.length === 0 && palette.length === 0) { - return { prompt: trimmed, isStructured: false }; - } - - // Strict key order: high_level_description, (style_description), compositional_deconstruction. - // style_description here carries only color_palette; the other style fields are left to raw-JSON use. - const compositional_deconstruction = { background: '', elements }; - const caption = - palette.length > 0 - ? { high_level_description: trimmed, style_description: { color_palette: palette }, compositional_deconstruction } - : { high_level_description: trimmed, compositional_deconstruction }; - - return { prompt: JSON.stringify(caption), isStructured: true }; +export type Ideogram4PromptInputs = { + /** The global positive prompt (batch-injectable; becomes `high_level_description` / raw text). */ + globalPrompt: string; + /** Enabled Regional Guidance layers with a non-empty prompt (description + normalized bbox). */ + regions: Ideogram4RegionInput[]; + /** The raw color palette (normalized/validated by the backend caption builder). */ + colorPalette: string[]; }; /** - * Reads the global prompt and each enabled Regional Guidance layer (prompt + normalized bbox) from - * canvas state, then assembles the Ideogram 4 prompt. Regions with no drawn content contribute their - * description without a bbox. + * Reads the raw Ideogram 4 prompt inputs from state: the global prompt, each enabled Regional Guidance + * layer (prompt + normalized bbox), and the color palette. The JSON caption is assembled later, at + * generation time, by the backend `ideogram4_caption_builder` node — so the batch-injectable global + * prompt is reflected in the encoded caption. Regions with no drawn content contribute a null bbox. */ -export const buildIdeogram4Prompt = (state: RootState, manager: CanvasManager | null): Ideogram4PromptResult => { +export const collectIdeogram4PromptInputs = ( + state: RootState, + manager: CanvasManager | null +): Ideogram4PromptInputs => { const globalPrompt = selectPositivePrompt(state); const colorPalette = selectIdeogram4ColorPalette(state); // No canvas manager (e.g. the Generate tab) → no regions to read. if (manager === null) { - return buildIdeogram4Caption(globalPrompt, [], colorPalette); + return { globalPrompt, regions: [], colorPalette }; } const canvas = selectCanvasSlice(state); @@ -143,5 +92,5 @@ export const buildIdeogram4Prompt = (state: RootState, manager: CanvasManager | regions.push({ prompt, bbox }); } - return buildIdeogram4Caption(globalPrompt, regions, colorPalette); + return { globalPrompt, regions, colorPalette }; }; diff --git a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx index e39245c4b1a..c01f48a4db3 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4SamplerPreset.tsx @@ -7,11 +7,12 @@ import { memo, useCallback, useMemo } from 'react'; import { useTranslation } from 'react-i18next'; // Each preset bundles a step count, the per-step guidance schedule (with a polish tail), and the -// logit-normal schedule mean/std. The primary quality/speed control for Ideogram 4. -const IDEOGRAM4_SAMPLER_PRESET_OPTIONS: ComboboxOption[] = [ - { value: 'V4_QUALITY_48', label: 'Quality (48 steps)' }, - { value: 'V4_DEFAULT_20', label: 'Default (20 steps)' }, - { value: 'V4_TURBO_12', label: 'Turbo (12 steps)' }, +// logit-normal schedule mean/std. The primary quality/speed control for Ideogram 4. The visible +// labels are localized (the step count is interpolated so translators can reposition it). +const IDEOGRAM4_SAMPLER_PRESET_I18N: { value: string; i18nKey: string; steps: number }[] = [ + { value: 'V4_QUALITY_48', i18nKey: 'parameters.ideogram4SamplerPresets.quality', steps: 48 }, + { value: 'V4_DEFAULT_20', i18nKey: 'parameters.ideogram4SamplerPresets.default', steps: 20 }, + { value: 'V4_TURBO_12', i18nKey: 'parameters.ideogram4SamplerPresets.turbo', steps: 12 }, ]; // Per-preset step count and schedule mean (mu), mirroring the backend PRESETS. Used by the advanced @@ -41,12 +42,16 @@ const ParamIdeogram4SamplerPreset = () => { [dispatch] ); - const value = useMemo(() => IDEOGRAM4_SAMPLER_PRESET_OPTIONS.find((o) => o.value === samplerPreset), [samplerPreset]); + const options = useMemo( + () => IDEOGRAM4_SAMPLER_PRESET_I18N.map((o) => ({ value: o.value, label: t(o.i18nKey, { steps: o.steps }) })), + [t] + ); + const value = useMemo(() => options.find((o) => o.value === samplerPreset), [options, samplerPreset]); return ( {t('parameters.samplerPreset')} - + ); }; diff --git a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Steps.tsx b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Steps.tsx index ae5ed70520d..5233a0dea1f 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Steps.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Core/ParamIdeogram4Steps.tsx @@ -11,7 +11,7 @@ import { memo, useCallback } from 'react'; import { useTranslation } from 'react-i18next'; import { PiXBold } from 'react-icons/pi'; -const MARKS = [1, 12, 20, 48, 100]; +const MARKS = [2, 12, 20, 48, 100]; // Optional override of the sampler preset's step count. null = use the preset. const ParamIdeogram4Steps = () => { @@ -50,7 +50,7 @@ const ParamIdeogram4Steps = () => { { = 1, "the guidance override must always occupy at least one step" + assert polish_count >= 1, "the polish tail must always be preserved" + assert result[-1] == override + assert result[0] == _QUALITY.guidance_schedule[0] From 75cc67a4181c0b490a0075a146a0718ceda53303 Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Sat, 25 Jul 2026 07:07:36 +0200 Subject: [PATCH 26/33] feat(ideogram4): step previews + document the model's built-in safety filter MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Emit a low-res progress preview each denoise step so the forming image is visible during generation, like the other denoise nodes. Ideogram uses a FLUX.2-style 32-channel VAE, so the packed latent is unpatchified/denormalized (get_latent_norm) and run through the FLUX.2 latent->RGB factors — no full VAE decode per step. The denoise loop now hands the callback the packed grid latent. - Document Ideogram 4's built-in content safety filter in models.mdx: it lives in the model weights (not Invoke's NSFW checker, can't be disabled from Invoke) and false-positives on benign prompts; structured JSON prompts trip it less. --- docs/src/content/docs/concepts/models.mdx | 6 +++ invokeai/app/invocations/ideogram4_denoise.py | 40 ++++++++++++++++++- invokeai/backend/ideogram4/denoise.py | 7 +++- 3 files changed, 49 insertions(+), 4 deletions(-) diff --git a/docs/src/content/docs/concepts/models.mdx b/docs/src/content/docs/concepts/models.mdx index 0eb6868db8a..ada15da80e8 100644 --- a/docs/src/content/docs/concepts/models.mdx +++ b/docs/src/content/docs/concepts/models.mdx @@ -83,6 +83,12 @@ Ideogram 4 does not use a negative prompt — it has a dedicated unconditional b - **Sampler Preset** — the primary quality/speed control. `Quality (48 steps)`, `Default (20 steps)`, and `Turbo (12 steps)` each bundle a step count, a guidance schedule, and the schedule shift. - **Advanced** overrides (all optional, leave on *Auto* to use the preset's values): **Steps**, **Guidance Scale**, **Schedule Shift (mu)**, and a **Color Palette** that biases the generated colors. +:::caution[Built-in safety filter] +Ideogram 4 ships with a **content safety filter baked into the released model weights**. When it triggers, the model outputs a placeholder image reading *"Image blocked by safety filter"* instead of your image. This is the model's own filter — it is **not** Invoke's NSFW checker (it fires even with the NSFW checker off) and cannot be disabled from Invoke. + +The filter is known to **false-positive on completely benign prompts**, and the quantized builds can trigger it more readily. Invoke always sends the model a structured JSON prompt, which tends to trip the filter less than plain text; if you hit it, rephrasing or adjusting the prompt usually clears it. +::: + ## Editing model metadata Every model has an editable **Source URL** field alongside its name and description. Use it to record where a model came from — for example a Civitai or HuggingFace page — independent of how it was originally installed. The URL is editable from the model's **Edit** view and appears as a clickable link in the model header once set. Models without a URL simply hide the field. diff --git a/invokeai/app/invocations/ideogram4_denoise.py b/invokeai/app/invocations/ideogram4_denoise.py index 9866f37b210..e2e8c5c6fd6 100644 --- a/invokeai/app/invocations/ideogram4_denoise.py +++ b/invokeai/app/invocations/ideogram4_denoise.py @@ -12,8 +12,15 @@ from invokeai.app.invocations.model import TransformerField from invokeai.app.invocations.primitives import LatentsOutput from invokeai.app.services.shared.invocation_context import InvocationContext +from invokeai.app.util.step_callback import ( + FLUX2_LATENT_RGB_BIAS, + FLUX2_LATENT_RGB_FACTORS, + sample_to_lowres_estimated_image, +) from invokeai.backend.ideogram4 import run_ideogram4_denoise +from invokeai.backend.ideogram4.latent_norm import get_latent_norm from invokeai.backend.ideogram4.sampler_configs import PRESETS +from invokeai.backend.ideogram4.sampling_utils import unpatchify_and_denormalize from invokeai.backend.ideogram4.transformer_pair import Ideogram4TransformerPair from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Ideogram4ConditioningInfo from invokeai.backend.util.devices import TorchDevice @@ -115,8 +122,37 @@ def invoke(self, context: InvocationContext) -> LatentsOutput: assert isinstance(info, Ideogram4ConditioningInfo) llm_features = info.prompt_embeds.to(device=device, dtype=torch.float32) - def step_callback(step: int, total: int, _latents: torch.Tensor) -> None: - context.util.signal_progress("Running Ideogram 4 denoising", step / total) + # Progress-preview setup: Ideogram uses a FLUX.2-style 32-channel VAE, so the FLUX.2 + # latent->RGB factors give a usable (approximate) low-res preview of the forming image at each + # step, without a full VAE decode. Denormalization params come from get_latent_norm (no VAE). + latent_shift, latent_scale = get_latent_norm() + rgb_factors = torch.tensor(FLUX2_LATENT_RGB_FACTORS, dtype=torch.float32) + rgb_bias = torch.tensor(FLUX2_LATENT_RGB_BIAS, dtype=torch.float32) + + def step_callback(step: int, total: int, packed_latents: torch.Tensor) -> None: + preview = None + try: + # packed_latents: (1, LATENT_DIM, grid_h, grid_w) -> VAE latent (1, 32, H/8, W/8). + vae_latent = unpatchify_and_denormalize( + packed_latents.float(), latent_shift.to(packed_latents.device), latent_scale.to(packed_latents.device) + ) + preview = sample_to_lowres_estimated_image( + samples=vae_latent, + latent_rgb_factors=rgb_factors.to(vae_latent.device), + latent_rgb_bias=rgb_bias.to(vae_latent.device), + ) + except Exception: + # A preview must never break generation — fall back to a plain progress signal. + preview = None + if preview is not None: + context.util.signal_progress( + "Running Ideogram 4 denoising", + step / total, + preview, + (preview.width * 8, preview.height * 8), + ) + else: + context.util.signal_progress("Running Ideogram 4 denoising", step / total) transformer_info = context.models.load(self.transformer.transformer) with transformer_info.model_on_device() as (_, transformers): diff --git a/invokeai/backend/ideogram4/denoise.py b/invokeai/backend/ideogram4/denoise.py index 3d3f5ee8d3c..2f985595711 100644 --- a/invokeai/backend/ideogram4/denoise.py +++ b/invokeai/backend/ideogram4/denoise.py @@ -20,7 +20,9 @@ ) from invokeai.backend.ideogram4.scheduler import get_schedule_for_resolution, make_step_intervals -# Called after each completed step with (step_index, total_steps, latents). +# Called after each completed step with (step_index, total_steps, packed_latents), where +# packed_latents is the current estimate in packed grid form ``(1, LATENT_DIM, grid_h, grid_w)`` — +# ready for a preview (unpatchify + VAE decode / RGB approximation) without re-deriving the grid. StepCallback = Callable[[int, int, torch.Tensor], None] @@ -118,6 +120,7 @@ def run_ideogram4_denoise( z = z + v * (s_val - t_val) if step_callback is not None: - step_callback(num_steps - i, num_steps, z) + # Hand the callback the current estimate in packed grid form so it can render a preview. + step_callback(num_steps - i, num_steps, pack_latents_to_grid(z, grid_h, grid_w)) return pack_latents_to_grid(z, grid_h, grid_w) From 089035ee6129f75c06f7ab56aa335ae028e8f61f Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Sat, 25 Jul 2026 15:51:41 +0200 Subject: [PATCH 27/33] feat(ideogram4): avoid safety-filter false-positives + step previews + caption visibility MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The main fix: Ideogram 4's built-in safety filter (baked into the model weights) false-positives and returns an "Image blocked by safety filter" placeholder for "degenerate" captions — empirically, an empty `compositional_deconstruction.elements` list, or a single full-frame [0,0,1000,1000] element whose desc just repeats the high_level_description. Our assembly produced empty elements whenever the user drew no regions, so plain prompts were blocked. - Caption assembly (build_ideogram4_caption): - Always emit a structured JSON caption; never bare plain text (the filter false-positives far more on plain text). Raw-JSON pastes still pass through. - When there are no regions, synthesize one default element describing the whole scene from the prompt with a *partial* (non-full-frame) bbox [100,100,900,900]. This never yields an empty/degenerate elements list. Verified end-to-end against the model: the previously-blocked "golden retriever on a skateboard" now renders. - Metadata: always wire the caption builder's output to the ideogram4_caption metadata field, so the viewer's "Structured Caption" row shows the exact JSON that was encoded (not just the raw prompt) for every generation. - Denoise: emit a low-res progress preview each step (unpatchify + FLUX.2 latent->RGB factors, since Ideogram uses a FLUX.2-style 32-channel VAE) so the forming image is visible during generation, like the other denoise nodes. - Docs: document the model's built-in safety filter in models.mdx (it's not Invoke's NSFW checker, can't be disabled from Invoke, and false-positives). Updates the caption/graph tests accordingly (also fixes latent tsc errors in the graph-builder test's core_metadata / ideogram4_caption comparisons). --- invokeai/app/invocations/ideogram4_caption.py | 5 +-- invokeai/backend/ideogram4/caption.py | 28 +++++++++++---- .../generation/buildIdeogram4Graph.test.ts | 34 +++++++++---------- .../graph/generation/buildIdeogram4Graph.ts | 13 +++---- tests/backend/ideogram4/test_caption.py | 29 ++++++++++++++-- 5 files changed, 71 insertions(+), 38 deletions(-) diff --git a/invokeai/app/invocations/ideogram4_caption.py b/invokeai/app/invocations/ideogram4_caption.py index 2eb3661ee66..08414bef5c9 100644 --- a/invokeai/app/invocations/ideogram4_caption.py +++ b/invokeai/app/invocations/ideogram4_caption.py @@ -34,13 +34,14 @@ class Ideogram4CaptionBuilderInvocation(BaseInvocation): The caption is built here (not in the graph builder) so the batch-injectable global `prompt` — which dynamic prompts and prompt batching vary — is folded into the encoded caption. The regions and color palette are fixed per generation and supplied as inputs. If the prompt is already a JSON object it is - passed through verbatim; with no regions or palette it falls back to the plain prompt. + passed through verbatim; otherwise it is always wrapped in the structured JSON schema (never bare + plain text — Ideogram's safety filter false-positives far more on plain text). """ prompt: str = InputField( default="", description="The global prompt (becomes `high_level_description`, or is used verbatim if it is " - "already a JSON caption, or as plain text).", + "already a JSON caption).", ui_component=UIComponent.Textarea, ) regions: list[Ideogram4Region] = InputField( diff --git a/invokeai/backend/ideogram4/caption.py b/invokeai/backend/ideogram4/caption.py index cb5db9af2c8..a8c1582498d 100644 --- a/invokeai/backend/ideogram4/caption.py +++ b/invokeai/backend/ideogram4/caption.py @@ -21,6 +21,13 @@ _HEX_COLOR_RE = re.compile(r"#[0-9A-F]{6}") +# Centered, deliberately sub-full-frame bbox ([y_min, x_min, y_max, x_max], 0–1000) for the element we +# auto-synthesize when the user drew no regions. Empirically, Ideogram 4's built-in safety filter blocks +# "degenerate" captions — an empty ``elements`` list, or a single *full-frame* [0,0,1000,1000] element +# whose desc merely repeats ``high_level_description``. A partial bbox (or a distinct desc) avoids the +# block; since a no-region prompt only gives us one description, we make the default element partial. +_DEFAULT_SCENE_BBOX = (100, 100, 900, 900) + def build_ideogram4_caption( global_prompt: str, @@ -31,8 +38,10 @@ def build_ideogram4_caption( - Raw-JSON passthrough: if the trimmed global prompt already starts with ``{`` it is returned verbatim (the user controls the JSON; regions/palette are ignored). - - With regions and/or a palette: a structured JSON caption is built. - - Otherwise: the plain (trimmed) global prompt is returned — the model accepts plain text. + - Otherwise a structured JSON caption is always built — regions become ``obj`` elements and a + palette becomes ``style_description.color_palette``. A prompt with neither still becomes a + minimal caption (just ``high_level_description``), never bare plain text: Ideogram is trained on + the JSON schema and its safety filter false-positives far more on plain text. ``regions`` is a list of ``(description, bbox)`` where bbox is ``[y_min, x_min, y_max, x_max]`` or None. Regions with a blank description are dropped. @@ -52,14 +61,21 @@ def build_ideogram4_caption( else: elements.append({"type": "obj", "desc": description}) + # No usable regions: synthesize one default element describing the whole scene from the prompt, with + # a partial (non-full-frame) bbox. An empty `elements` list — or a full-frame element repeating the + # prompt — trips Ideogram's safety filter (verified empirically); a partial default box avoids it. + if not elements: + elements = [{"type": "obj", "bbox": list(_DEFAULT_SCENE_BBOX), "desc": trimmed}] + # Normalize the palette to uppercase #RRGGBB (the schema's required hex form); drop invalid entries. # Mirror the JS order: uppercase first, then validate. palette = [c.upper() for c in color_palette if _HEX_COLOR_RE.fullmatch(c.upper())] - # Nothing structured to encode — fall back to the plain prompt (documented to work). - if not elements and not palette: - return trimmed - + # Always emit a structured caption (never bare plain text): Ideogram 4 is trained on the JSON schema + # and its built-in safety filter false-positives far more often on plain-text prompts than on + # structured JSON. A prompt with no regions/palette becomes a minimal caption carrying just the + # high_level_description (with an empty compositional_deconstruction). Raw-JSON pastes still pass + # through verbatim above. compositional_deconstruction = {"background": "", "elements": elements} if palette: caption = { diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.test.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.test.ts index dd17c6514b8..f04a6bf581c 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.test.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.test.ts @@ -128,30 +128,28 @@ describe('buildIdeogram4Graph', () => { expect(captionBuilder.color_palette).toEqual(['#FF0000']); }); + // `core_metadata` and the `ideogram4_caption` metadata field are outside Graph's strict typed unions + // (the metadata node is excluded from AnyInvocation), so compare their string values via casts. + const captionEdge = (g: Awaited>['g']) => { + const captionBuilderId = g.getNodes().find((n) => (n.type as string) === 'ideogram4_caption_builder')?.id; + return g + .getEdges() + .find((e) => e.source.node_id === captionBuilderId && (e.destination.field as string) === 'ideogram4_caption'); + }; + it('records the runtime-assembled caption in metadata via an edge (structured inputs)', async () => { const { g } = await buildIdeogram4Graph(buildArg()); - const captionBuilder = g.getNodes().find((n) => n.type === 'ideogram4_caption_builder'); - const metadataNode = g.getNodes().find((n) => n.type === 'core_metadata'); - expect(metadataNode).toBeDefined(); // The caption is wired from the runtime builder into metadata, so each batch item records its own. - expect( - g.getEdges().some( - (e) => - e.source.node_id === captionBuilder!.id && - e.source.field === 'value' && - e.destination.node_id === metadataNode!.id && - e.destination.field === 'ideogram4_caption' - ) - ).toBe(true); + const edge = captionEdge(g); + expect(edge).toBeDefined(); + expect(edge!.source.field).toBe('value'); }); - it('does not record a caption edge when there are no structured inputs (plain prompt)', async () => { + it('always records the caption edge, even for a plain prompt (so the encoded JSON is visible)', async () => { + // Plain prompts are now wrapped in the JSON schema at runtime, so the caption differs from the raw + // positive_prompt and must be recorded too — otherwise the metadata viewer only shows the raw text. promptInputs = { globalPrompt: 'just plain text', regions: [], colorPalette: [] }; const { g } = await buildIdeogram4Graph(buildArg()); - const edges = g.getEdges(); - expect(edges.some((e) => e.destination.field === 'ideogram4_caption')).toBe(false); - // The prompt still flows through the caption builder (which passes plain text through at runtime). - const captionBuilder = g.getNodes().find((n) => n.type === 'ideogram4_caption_builder'); - expect(captionBuilder).toBeDefined(); + expect(captionEdge(g)).toBeDefined(); }); }); diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts index 85fb8aaae52..3e3bd6f1d8e 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Graph.ts @@ -53,9 +53,6 @@ export const buildIdeogram4Graph = async (arg: GraphBuilderArg): Promise 0 || colorPalette.length > 0; const g = new Graph(getPrefixedId('ideogram4_graph')); @@ -139,12 +136,10 @@ export const buildIdeogram4Graph = async (arg: GraphBuilderArg): Promise the default full-scene element is synthesized (elements is never empty). + assert parsed["compositional_deconstruction"]["elements"] == [ + {"type": "obj", "bbox": [100, 100, 900, 900], "desc": "a scene"} + ] def test_compact_separators_and_non_ascii_preserved(): From 69b815dc63ce91d7af057f1f04bd8e76e9ea8436 Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Sat, 25 Jul 2026 17:02:30 +0200 Subject: [PATCH 28/33] Chore typegen + openapi + Ruff --- invokeai/app/invocations/ideogram4_denoise.py | 4 +- invokeai/frontend/web/openapi.json | 154 +++++++++++++++++- .../frontend/web/src/services/api/schema.ts | 5 +- 3 files changed, 158 insertions(+), 5 deletions(-) diff --git a/invokeai/app/invocations/ideogram4_denoise.py b/invokeai/app/invocations/ideogram4_denoise.py index e2e8c5c6fd6..f9bc9ea855d 100644 --- a/invokeai/app/invocations/ideogram4_denoise.py +++ b/invokeai/app/invocations/ideogram4_denoise.py @@ -134,7 +134,9 @@ def step_callback(step: int, total: int, packed_latents: torch.Tensor) -> None: try: # packed_latents: (1, LATENT_DIM, grid_h, grid_w) -> VAE latent (1, 32, H/8, W/8). vae_latent = unpatchify_and_denormalize( - packed_latents.float(), latent_shift.to(packed_latents.device), latent_scale.to(packed_latents.device) + packed_latents.float(), + latent_shift.to(packed_latents.device), + latent_scale.to(packed_latents.device), ) preview = sample_to_lowres_estimated_image( samples=vae_latent, diff --git a/invokeai/frontend/web/openapi.json b/invokeai/frontend/web/openapi.json index 6f688a3fa0e..f6642f778dc 100644 --- a/invokeai/frontend/web/openapi.json +++ b/invokeai/frontend/web/openapi.json @@ -20000,6 +20000,23 @@ "orig_default": null, "orig_required": false }, + "ideogram4_caption": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The structured JSON caption encoded for Ideogram 4 inference", + "field_kind": "input", + "input": "any", + "orig_default": null, + "orig_required": false, + "title": "Ideogram4 Caption" + }, "hrf_enabled": { "anyOf": [ { @@ -29668,6 +29685,9 @@ { "$ref": "#/components/schemas/IdealSizeInvocation" }, + { + "$ref": "#/components/schemas/Ideogram4CaptionBuilderInvocation" + }, { "$ref": "#/components/schemas/Ideogram4DenoiseInvocation" }, @@ -32530,6 +32550,91 @@ "title": "IdealSizeOutput", "type": "object" }, + "Ideogram4CaptionBuilderInvocation": { + "category": "conditioning", + "class": "invocation", + "classification": "prototype", + "description": "Assembles the Ideogram 4 structured JSON caption at generation time.\n\nThe caption is built here (not in the graph builder) so the batch-injectable global `prompt` \u2014 which\ndynamic prompts and prompt batching vary \u2014 is folded into the encoded caption. The regions and color\npalette are fixed per generation and supplied as inputs. If the prompt is already a JSON object it is\npassed through verbatim; otherwise it is always wrapped in the structured JSON schema (never bare\nplain text \u2014 Ideogram's safety filter false-positives far more on plain text).", + "node_pack": "invokeai", + "properties": { + "id": { + "description": "The id of this instance of an invocation. Must be unique among all instances of invocations.", + "field_kind": "node_attribute", + "title": "Id", + "type": "string" + }, + "is_intermediate": { + "default": false, + "description": "Whether or not this is an intermediate invocation.", + "field_kind": "node_attribute", + "input": "direct", + "orig_required": true, + "title": "Is Intermediate", + "type": "boolean", + "ui_hidden": false, + "ui_type": "IsIntermediate" + }, + "use_cache": { + "default": true, + "description": "Whether or not to use the cache", + "field_kind": "node_attribute", + "title": "Use Cache", + "type": "boolean" + }, + "prompt": { + "default": "", + "description": "The global prompt (becomes `high_level_description`, or is used verbatim if it is already a JSON caption).", + "field_kind": "input", + "input": "any", + "orig_default": "", + "orig_required": false, + "title": "Prompt", + "type": "string", + "ui_component": "textarea" + }, + "regions": { + "default": [], + "description": "Regional descriptions and bounding boxes assembled from Canvas Regional Guidance layers.", + "field_kind": "input", + "input": "any", + "items": { + "$ref": "#/components/schemas/Ideogram4Region" + }, + "orig_default": [], + "orig_required": false, + "title": "Regions", + "type": "array" + }, + "color_palette": { + "default": [], + "description": "Optional color palette as hex colors (#RRGGBB).", + "field_kind": "input", + "input": "any", + "items": { + "type": "string" + }, + "orig_default": [], + "orig_required": false, + "title": "Color Palette", + "type": "array" + }, + "type": { + "const": "ideogram4_caption_builder", + "default": "ideogram4_caption_builder", + "field_kind": "node_attribute", + "title": "type", + "type": "string" + } + }, + "required": ["type", "id"], + "tags": ["prompt", "ideogram4"], + "title": "Caption Builder - Ideogram 4", + "type": "object", + "version": "1.0.0", + "output": { + "$ref": "#/components/schemas/StringOutput" + } + }, "Ideogram4ConditioningField": { "description": "An Ideogram 4 conditioning tensor primitive value", "properties": { @@ -32674,7 +32779,7 @@ "anyOf": [ { "maximum": 100, - "minimum": 1, + "minimum": 2, "type": "integer" }, { @@ -32682,7 +32787,7 @@ } ], "default": null, - "description": "Override the preset's step count. Leave empty to use the preset.", + "description": "Override the preset's step count (minimum 2, so a polish and a main step both exist). Leave empty to use the preset.", "field_kind": "input", "input": "any", "orig_default": null, @@ -32949,6 +33054,35 @@ "title": "Ideogram4ModelLoaderOutput", "type": "object" }, + "Ideogram4Region": { + "description": "A single region of an Ideogram 4 structured caption (description + optional bounding box).", + "properties": { + "prompt": { + "description": "The region's description (becomes the element's `desc`).", + "title": "Prompt", + "type": "string" + }, + "bbox": { + "anyOf": [ + { + "items": { + "type": "integer" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Normalized bounding box [y_min, x_min, y_max, x_max] (0\u20131000), or null for a region with no drawn content.", + "title": "Bbox" + } + }, + "required": ["prompt"], + "title": "Ideogram4Region", + "type": "object" + }, "Ideogram4TextEncoderInvocation": { "category": "conditioning", "class": "invocation", @@ -37888,6 +38022,9 @@ { "$ref": "#/components/schemas/IdealSizeInvocation" }, + { + "$ref": "#/components/schemas/Ideogram4CaptionBuilderInvocation" + }, { "$ref": "#/components/schemas/Ideogram4DenoiseInvocation" }, @@ -39062,6 +39199,9 @@ { "$ref": "#/components/schemas/IdealSizeInvocation" }, + { + "$ref": "#/components/schemas/Ideogram4CaptionBuilderInvocation" + }, { "$ref": "#/components/schemas/Ideogram4DenoiseInvocation" }, @@ -39890,6 +40030,9 @@ "ideal_size": { "$ref": "#/components/schemas/IdealSizeOutput" }, + "ideogram4_caption_builder": { + "$ref": "#/components/schemas/StringOutput" + }, "ideogram4_denoise": { "$ref": "#/components/schemas/LatentsOutput" }, @@ -40494,6 +40637,7 @@ "heuristic_resize", "i2l", "ideal_size", + "ideogram4_caption_builder", "ideogram4_denoise", "ideogram4_l2i", "ideogram4_model_loader", @@ -41022,6 +41166,9 @@ { "$ref": "#/components/schemas/IdealSizeInvocation" }, + { + "$ref": "#/components/schemas/Ideogram4CaptionBuilderInvocation" + }, { "$ref": "#/components/schemas/Ideogram4DenoiseInvocation" }, @@ -41936,6 +42083,9 @@ { "$ref": "#/components/schemas/IdealSizeInvocation" }, + { + "$ref": "#/components/schemas/Ideogram4CaptionBuilderInvocation" + }, { "$ref": "#/components/schemas/Ideogram4DenoiseInvocation" }, diff --git a/invokeai/frontend/web/src/services/api/schema.ts b/invokeai/frontend/web/src/services/api/schema.ts index ede8d81202e..f8dd26bf139 100644 --- a/invokeai/frontend/web/src/services/api/schema.ts +++ b/invokeai/frontend/web/src/services/api/schema.ts @@ -13759,7 +13759,8 @@ export type components = { * The caption is built here (not in the graph builder) so the batch-injectable global `prompt` — which * dynamic prompts and prompt batching vary — is folded into the encoded caption. The regions and color * palette are fixed per generation and supplied as inputs. If the prompt is already a JSON object it is - * passed through verbatim; with no regions or palette it falls back to the plain prompt. + * passed through verbatim; otherwise it is always wrapped in the structured JSON schema (never bare + * plain text — Ideogram's safety filter false-positives far more on plain text). */ Ideogram4CaptionBuilderInvocation: { /** @@ -13781,7 +13782,7 @@ export type components = { use_cache?: boolean; /** * Prompt - * @description The global prompt (becomes `high_level_description`, or is used verbatim if it is already a JSON caption, or as plain text). + * @description The global prompt (becomes `high_level_description`, or is used verbatim if it is already a JSON caption). * @default */ prompt?: string; From d39b537846bde263d4865a199134574aa19aa1df Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Sat, 25 Jul 2026 17:40:28 +0200 Subject: [PATCH 29/33] Fix Knit --- .../nodes/util/graph/generation/buildIdeogram4Prompt.ts | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts index 28d73ebee69..4b7c08cf728 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildIdeogram4Prompt.ts @@ -38,14 +38,14 @@ export const rectToIdeogram4Bbox = (regionRect: Rect, genBbox: Rect): Ideogram4B return [yMin, xMin, yMax, xMax]; }; -export type Ideogram4RegionInput = { +type Ideogram4RegionInput = { /** The region's positive prompt — becomes the element's `desc`. */ prompt: string; /** The region's normalized bbox, or null when the region has no drawn content. */ bbox: Ideogram4Bbox | null; }; -export type Ideogram4PromptInputs = { +type Ideogram4PromptInputs = { /** The global positive prompt (batch-injectable; becomes `high_level_description` / raw text). */ globalPrompt: string; /** Enabled Regional Guidance layers with a non-empty prompt (description + normalized bbox). */ From 62db8e06a833385fe5c4c71ca26211f3ac93a29e Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Sun, 26 Jul 2026 18:52:47 +0200 Subject: [PATCH 30/33] fix(ideogram4): enforce steps>=2 client-side and validate region bbox Address review on the Ideogram 4 PR: - The backend denoise node requires steps >= 2, but the client still accepted ideogram4_steps = 1 in three places, letting a recalled or rehydrated value build a graph that violates the backend schema. Tighten the zod schema to min(2) with `.catch(null)` (a stale/out-of-range value normalizes to null = use the preset instead of failing the whole persisted slice), normalize dispatched values through the schema in setIdeogram4Steps, and refuse an out-of-range value in the ideogram4_steps metadata recall parser. The slider was already min=2. - Ideogram4Region.bbox was an unconstrained Optional[list[int]], so a workflow/API caller could pass a wrong-length or out-of-range box that the caption builder serialized verbatim into the structured prompt. Add a field validator requiring exactly four coordinates, each in 0..1000. Add tests for both: the region bbox contract (valid/None accepted; short, long, negative, and >1000 rejected) and the ideogram4Steps normalization (valid kept, null kept, stale 1 normalized to null on both dispatch and rehydrate). --- invokeai/app/invocations/ideogram4_caption.py | 21 ++++++++++- .../controlLayers/store/paramsSlice.test.ts | 27 ++++++++++++++ .../controlLayers/store/paramsSlice.ts | 4 ++- .../src/features/controlLayers/store/types.ts | 5 ++- .../web/src/features/metadata/parsing.tsx | 3 +- .../ideogram4/test_caption_builder_node.py | 35 +++++++++++++++++++ 6 files changed, 91 insertions(+), 4 deletions(-) create mode 100644 tests/backend/ideogram4/test_caption_builder_node.py diff --git a/invokeai/app/invocations/ideogram4_caption.py b/invokeai/app/invocations/ideogram4_caption.py index 08414bef5c9..538606c6e33 100644 --- a/invokeai/app/invocations/ideogram4_caption.py +++ b/invokeai/app/invocations/ideogram4_caption.py @@ -1,6 +1,6 @@ from typing import Optional -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field, field_validator from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation from invokeai.app.invocations.fields import InputField, UIComponent @@ -8,6 +8,8 @@ from invokeai.app.services.shared.invocation_context import InvocationContext from invokeai.backend.ideogram4.caption import build_ideogram4_caption +_IDEOGRAM4_COORD_MAX = 1000 + class Ideogram4Region(BaseModel): """A single region of an Ideogram 4 structured caption (description + optional bounding box).""" @@ -19,6 +21,23 @@ class Ideogram4Region(BaseModel): "with no drawn content.", ) + @field_validator("bbox") + @classmethod + def _validate_bbox(cls, v: Optional[list[int]]) -> Optional[list[int]]: + """Enforce the model contract: exactly four coordinates, each normalized to 0..1000. + + The caption builder forwards the bbox verbatim into the structured JSON, so an ill-formed box + (wrong length or out-of-range) would emit a malformed prompt the model may misapply. Reject it + here instead of serializing it. + """ + if v is None: + return v + if len(v) != 4: + raise ValueError(f"bbox must have exactly 4 values [y_min, x_min, y_max, x_max], got {len(v)}: {v}") + if any(c < 0 or c > _IDEOGRAM4_COORD_MAX for c in v): + raise ValueError(f"bbox coordinates must each be in the range 0..{_IDEOGRAM4_COORD_MAX}, got {v}") + return v + @invocation( "ideogram4_caption_builder", diff --git a/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.test.ts b/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.test.ts index d210d2fd2ac..a7192f8595f 100644 --- a/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.test.ts +++ b/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.test.ts @@ -17,6 +17,7 @@ import { selectModelSupportsRefImages, selectModelSupportsSeed, selectModelSupportsSteps, + setIdeogram4Steps, } from './paramsSlice'; import { getInitialParamsState } from './types'; @@ -216,3 +217,29 @@ describe('paramsSlice prompt history', () => { expect(removed.positivePromptHistory).toEqual([{ positivePrompt: 'a cat', negativePrompt: 'low quality' }]); }); }); + +describe('paramsSlice ideogram4Steps normalization (backend requires >= 2)', () => { + it('keeps a valid override step count', () => { + const state = paramsSliceConfig.slice.reducer(getInitialParamsState(), setIdeogram4Steps(20)); + expect(state.ideogram4Steps).toBe(20); + }); + + it('accepts null (use the preset)', () => { + const state = paramsSliceConfig.slice.reducer(getInitialParamsState(), setIdeogram4Steps(null)); + expect(state.ideogram4Steps).toBeNull(); + }); + + it('normalizes a stale out-of-range value (1, below the backend min of 2) to null', () => { + const state = paramsSliceConfig.slice.reducer(getInitialParamsState(), setIdeogram4Steps(1)); + expect(state.ideogram4Steps).toBeNull(); + }); + + it('normalizes a stale rehydrated ideogram4Steps of 1 to null instead of failing the whole slice', () => { + const migrate = paramsSliceConfig.persistConfig?.migrate; + expect(migrate).toBeDefined(); + const rehydrated = migrate?.({ ...getInitialParamsState(), ideogram4Steps: 1 }) as ReturnType< + typeof getInitialParamsState + >; + expect(rehydrated.ideogram4Steps).toBeNull(); + }); +}); diff --git a/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts b/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts index 587123c6cd7..6c438a45296 100644 --- a/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts +++ b/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts @@ -103,7 +103,9 @@ const slice = createSlice({ state.ideogram4SamplerPreset = action.payload; }, setIdeogram4Steps: (state, action: PayloadAction) => { - state.ideogram4Steps = action.payload; + // Normalize through the schema so a stale/out-of-range value (e.g. 1, below the backend's min of 2) + // becomes null (= use preset) rather than being dispatched straight into the graph. + state.ideogram4Steps = zParamsState.shape.ideogram4Steps.parse(action.payload); }, setIdeogram4GuidanceScale: (state, action: PayloadAction) => { state.ideogram4GuidanceScale = action.payload; diff --git a/invokeai/frontend/web/src/features/controlLayers/store/types.ts b/invokeai/frontend/web/src/features/controlLayers/store/types.ts index ada1952619f..d0b5b9bafc7 100644 --- a/invokeai/frontend/web/src/features/controlLayers/store/types.ts +++ b/invokeai/frontend/web/src/features/controlLayers/store/types.ts @@ -823,7 +823,10 @@ export const zParamsState = z.object({ // Defaults make these resilient to rehydration of persisted state saved before the fields existed. ideogram4SamplerPreset: zParameterIdeogram4SamplerPreset.default('V4_QUALITY_48'), // Optional advanced overrides of the Ideogram 4 sampler preset (null = use the preset's value). - ideogram4Steps: z.number().int().min(1).max(100).nullable().default(null), + // Backend requires steps >= 2 (a polish and a main step). `.catch(null)` normalizes a stale/invalid + // persisted or recalled value (e.g. 1 from an older build) to null (= use preset) instead of letting an + // out-of-range value reach the graph or breaking the whole persisted params slice on rehydrate. + ideogram4Steps: z.number().int().min(2).max(100).nullable().catch(null).default(null), ideogram4GuidanceScale: z.number().min(1).max(20).nullable().default(null), ideogram4Mu: z.number().min(-4).max(4).nullable().default(null), // Hex colors (#RRGGBB) injected into the JSON caption's style_description.color_palette. diff --git a/invokeai/frontend/web/src/features/metadata/parsing.tsx b/invokeai/frontend/web/src/features/metadata/parsing.tsx index 8d21963e9ac..a93abc71b7d 100644 --- a/invokeai/frontend/web/src/features/metadata/parsing.tsx +++ b/invokeai/frontend/web/src/features/metadata/parsing.tsx @@ -939,7 +939,8 @@ const Ideogram4Steps: SingleMetadataHandler = { if (raw === null || raw === 'auto') { return Promise.resolve(null); } - return Promise.resolve(z.number().int().min(1).max(100).parse(raw)); + // Backend requires steps >= 2; refuse a stale/out-of-range recalled value instead of recalling it. + return Promise.resolve(z.number().int().min(2).max(100).parse(raw)); }, recall: (value, store) => { if (selectBase(store.getState()) !== 'ideogram-4') { diff --git a/tests/backend/ideogram4/test_caption_builder_node.py b/tests/backend/ideogram4/test_caption_builder_node.py new file mode 100644 index 00000000000..256dc7e41e8 --- /dev/null +++ b/tests/backend/ideogram4/test_caption_builder_node.py @@ -0,0 +1,35 @@ +"""Validation tests for the Ideogram4CaptionBuilderInvocation region bbox contract. + +The caption builder forwards each region's bbox verbatim into the structured JSON, so a malformed box +(wrong length or out-of-range coordinate) would emit a prompt the model may misapply. The Ideogram4Region +model must reject such boxes up front. +""" + +import pytest +from pydantic import ValidationError + +from invokeai.app.invocations.ideogram4_caption import Ideogram4Region + + +def test_valid_bbox_is_accepted(): + region = Ideogram4Region(prompt="a cat", bbox=[0, 0, 1000, 1000]) + assert region.bbox == [0, 0, 1000, 1000] + + +def test_none_bbox_is_accepted(): + region = Ideogram4Region(prompt="a cat", bbox=None) + assert region.bbox is None + + +@pytest.mark.parametrize( + "bbox", + [ + [0, 0, 1000], # too short + [0, 0, 1000, 1000, 7], # too long + [-1, 0, 1000, 1000], # negative + [0, 0, 1000, 1001], # above 1000 + ], +) +def test_malformed_bbox_is_rejected(bbox: list[int]): + with pytest.raises(ValidationError): + Ideogram4Region(prompt="a cat", bbox=bbox) From fa2a2ca74fff7a82fced0282d9b6277f09edb64f Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Sun, 26 Jul 2026 22:50:23 +0200 Subject: [PATCH 31/33] fix(ideogram4): block unsupported canvas modes/bbox, warn dropped region inputs, constrain bbox Address the latest review on the Ideogram 4 PR: - Canvas readiness allowed unsupported generation modes: Ideogram 4 is txt2img-only (buildIdeogram4Graph asserts it), but a raster layer or inpaint mask makes the compositor pick img2img/outpaint/inpaint, failing only at graph build. Warn in getRasterLayerWarnings/getInpaintMaskWarnings for Ideogram 4 (these already flow into canvas readiness reasons), blocking enqueue up front. - Canvas readiness had no Ideogram 4 bbox check; the backend requires multiples of 16. Add the 16-grid check mirroring the other 16-grid models so an off-grid bbox (e.g. 1025x1024) is blocked instead of failing backend validation. - getRegionalGuidanceWarnings had no Ideogram 4 branch, so a region whose only input is a negative prompt, auto-negative, or reference image looked effective while the graph silently drops it. Warn those inputs are unsupported. - Ideogram4Region.bbox now also rejects inverted boxes (y_min <= y_max, x_min <= x_max). - The advanced-settings badge selector lumped Ideogram 4 into the generic branch, showing stale VAE/clip-skip/CFG-rescale/seamless badges for controls that are hidden for Ideogram. Exclude Ideogram 4 from that branch. - Model bbox as a constrained type (exactly 4 ints, each 0..1000) so the OpenAPI schema advertises minItems/maxItems and item minimum/maximum. Add readiness tests (bbox grid, raster/inpaint blocking, empty-layer allowance, regional-guidance negative/reference-image warnings) and bbox ordering-rejection tests. --- invokeai/app/invocations/ideogram4_caption.py | 30 +++-- invokeai/frontend/web/openapi.json | 4 + invokeai/frontend/web/public/locales/en.json | 1 + .../controlLayers/store/validators.ts | 37 +++++- .../features/queue/store/readiness.test.ts | 114 ++++++++++++++++++ .../web/src/features/queue/store/readiness.ts | 46 +++++++ .../AdvancedSettingsAccordion.tsx | 26 ++-- .../ideogram4/test_caption_builder_node.py | 2 + 8 files changed, 233 insertions(+), 27 deletions(-) diff --git a/invokeai/app/invocations/ideogram4_caption.py b/invokeai/app/invocations/ideogram4_caption.py index 538606c6e33..f4e858d0363 100644 --- a/invokeai/app/invocations/ideogram4_caption.py +++ b/invokeai/app/invocations/ideogram4_caption.py @@ -1,4 +1,4 @@ -from typing import Optional +from typing import Annotated, Optional from pydantic import BaseModel, Field, field_validator @@ -10,12 +10,20 @@ _IDEOGRAM4_COORD_MAX = 1000 +# Exactly four normalized coordinates [y_min, x_min, y_max, x_max], each in 0..1000. Modeled as a +# constrained type so the generated OpenAPI schema advertises minItems/maxItems and per-item +# minimum/maximum — clients get the contract from the schema, not only from runtime validation. +Ideogram4Bbox = Annotated[ + list[Annotated[int, Field(ge=0, le=_IDEOGRAM4_COORD_MAX)]], + Field(min_length=4, max_length=4), +] + class Ideogram4Region(BaseModel): """A single region of an Ideogram 4 structured caption (description + optional bounding box).""" prompt: str = Field(description="The region's description (becomes the element's `desc`).") - bbox: Optional[list[int]] = Field( + bbox: Optional[Ideogram4Bbox] = Field( default=None, description="Normalized bounding box [y_min, x_min, y_max, x_max] (0–1000), or null for a region " "with no drawn content.", @@ -24,18 +32,20 @@ class Ideogram4Region(BaseModel): @field_validator("bbox") @classmethod def _validate_bbox(cls, v: Optional[list[int]]) -> Optional[list[int]]: - """Enforce the model contract: exactly four coordinates, each normalized to 0..1000. + """Enforce the ordering the constrained type can't express: y_min <= y_max and x_min <= x_max. - The caption builder forwards the bbox verbatim into the structured JSON, so an ill-formed box - (wrong length or out-of-range) would emit a malformed prompt the model may misapply. Reject it - here instead of serializing it. + Length (exactly 4) and range (0..1000) are enforced by the Ideogram4Bbox type. The caption + builder forwards the bbox verbatim into the structured JSON, so an inverted box would emit a + malformed prompt the model may misapply — reject it here. """ if v is None: return v - if len(v) != 4: - raise ValueError(f"bbox must have exactly 4 values [y_min, x_min, y_max, x_max], got {len(v)}: {v}") - if any(c < 0 or c > _IDEOGRAM4_COORD_MAX for c in v): - raise ValueError(f"bbox coordinates must each be in the range 0..{_IDEOGRAM4_COORD_MAX}, got {v}") + y_min, x_min, y_max, x_max = v + if y_min > y_max or x_min > x_max: + raise ValueError( + f"bbox must satisfy y_min <= y_max and x_min <= x_max, got [y_min={y_min}, x_min={x_min}, " + f"y_max={y_max}, x_max={x_max}]" + ) return v diff --git a/invokeai/frontend/web/openapi.json b/invokeai/frontend/web/openapi.json index f6642f778dc..a89094dbdb4 100644 --- a/invokeai/frontend/web/openapi.json +++ b/invokeai/frontend/web/openapi.json @@ -33066,8 +33066,12 @@ "anyOf": [ { "items": { + "maximum": 1000, + "minimum": 0, "type": "integer" }, + "maxItems": 4, + "minItems": 4, "type": "array" }, { diff --git a/invokeai/frontend/web/public/locales/en.json b/invokeai/frontend/web/public/locales/en.json index d8b76bb1e7f..9daab285eca 100644 --- a/invokeai/frontend/web/public/locales/en.json +++ b/invokeai/frontend/web/public/locales/en.json @@ -2924,6 +2924,7 @@ "rgReferenceImagesNotSupported": "regional Reference Images not supported for selected base model", "rgAutoNegativeNotSupported": "Auto-Negative not supported for selected base model", "rgNoRegion": "no region drawn", + "ideogram4Txt2ImgOnly": "Ideogram 4 is text-to-image only; raster layers and inpaint masks are not supported", "fluxFillIncompatibleWithControlLoRA": "Control LoRA is not compatible with FLUX Fill", "controlAdapterDuplicateAnimaLLLiteModel": "each Anima control model can only be used by one Control Layer", "bboxHidden": "Bounding box is hidden (shift+o to toggle)" diff --git a/invokeai/frontend/web/src/features/controlLayers/store/validators.ts b/invokeai/frontend/web/src/features/controlLayers/store/validators.ts index a497a38a5d7..91c334c8564 100644 --- a/invokeai/frontend/web/src/features/controlLayers/store/validators.ts +++ b/invokeai/frontend/web/src/features/controlLayers/store/validators.ts @@ -19,6 +19,7 @@ const WARNINGS = { RG_REFERENCE_IMAGES_NOT_SUPPORTED: 'controlLayers.warnings.rgReferenceImagesNotSupported', RG_AUTO_NEGATIVE_NOT_SUPPORTED: 'controlLayers.warnings.rgAutoNegativeNotSupported', RG_NO_REGION: 'controlLayers.warnings.rgNoRegion', + IDEOGRAM4_TXT2IMG_ONLY: 'controlLayers.warnings.ideogram4Txt2ImgOnly', IP_ADAPTER_NO_MODEL_SELECTED: 'controlLayers.warnings.ipAdapterNoModelSelected', IP_ADAPTER_INCOMPATIBLE_BASE_MODEL: 'controlLayers.warnings.ipAdapterIncompatibleBaseModel', IP_ADAPTER_NO_IMAGE_SELECTED: 'controlLayers.warnings.ipAdapterNoImageSelected', @@ -100,6 +101,21 @@ export const getRegionalGuidanceWarnings = ( } } + if (model.base === 'ideogram-4') { + // Ideogram 4 regions contribute only a positive prompt + bbox to the structured caption + // (see collectIdeogram4PromptInputs). Negative prompts, auto-negative and reference images are + // silently dropped, so warn they are unsupported rather than letting the layer look effective. + if (entity.negativePrompt !== null) { + warnings.push(WARNINGS.RG_NEGATIVE_PROMPT_NOT_SUPPORTED); + } + if (entity.autoNegative) { + warnings.push(WARNINGS.RG_AUTO_NEGATIVE_NOT_SUPPORTED); + } + if (entity.referenceImages.length > 0) { + warnings.push(WARNINGS.RG_REFERENCE_IMAGES_NOT_SUPPORTED); + } + } + entity.referenceImages.forEach(({ config }) => { if (!config.model) { // No model selected @@ -239,23 +255,32 @@ export const getControlLayerWarnings = ( }; export const getRasterLayerWarnings = ( - _entity: CanvasRasterLayerState, - _model: MainOrExternalModelConfig | null | undefined + entity: CanvasRasterLayerState, + model: MainOrExternalModelConfig | null | undefined ): WarningTKey[] => { const warnings: WarningTKey[] = []; - // There are no warnings at the moment for raster layers. + // Ideogram 4 is text-to-image only (buildIdeogram4Graph asserts txt2img). A raster layer with content + // makes the compositor pick img2img/outpaint, which the graph builder rejects only at enqueue — warn + // here so canvas readiness blocks it up front. + if (model?.base === 'ideogram-4' && entity.objects.length > 0) { + warnings.push(WARNINGS.IDEOGRAM4_TXT2IMG_ONLY); + } return warnings; }; export const getInpaintMaskWarnings = ( - _entity: CanvasInpaintMaskState, - _model: MainOrExternalModelConfig | null | undefined + entity: CanvasInpaintMaskState, + model: MainOrExternalModelConfig | null | undefined ): WarningTKey[] => { const warnings: WarningTKey[] = []; - // There are no warnings at the moment for inpaint masks. + // Ideogram 4 is text-to-image only; an inpaint mask with content makes the compositor pick inpaint, + // which the Ideogram graph builder cannot handle. Warn so canvas readiness blocks it before enqueue. + if (model?.base === 'ideogram-4' && entity.objects.length > 0) { + warnings.push(WARNINGS.IDEOGRAM4_TXT2IMG_ONLY); + } return warnings; }; diff --git a/invokeai/frontend/web/src/features/queue/store/readiness.test.ts b/invokeai/frontend/web/src/features/queue/store/readiness.test.ts index 632006050e6..98ae1a2244a 100644 --- a/invokeai/frontend/web/src/features/queue/store/readiness.test.ts +++ b/invokeai/frontend/web/src/features/queue/store/readiness.test.ts @@ -270,3 +270,117 @@ describe('FLUX.2 Klein readiness checks – canvas tab', () => { expect(hasFlux2Qwen3Reason(reasons)).toBe(true); }); }); + +const ideogram4Model = { + key: 'ideogram-4', + hash: 'h', + name: 'Ideogram 4', + base: 'ideogram-4', + type: 'main', + format: 'diffusers', +} as unknown as MainModelConfig; + +const buildIdeogram4CanvasArg = (canvasOverrides: { + bbox?: { width: number; height: number }; + rasterLayers?: unknown[]; + inpaintMasks?: unknown[]; + regionalGuidance?: unknown[]; +}) => ({ + ...buildCanvasTabArg({ model: ideogram4Model }), + canvas: { + bbox: { + scaleMethod: 'none', + rect: canvasOverrides.bbox ?? { width: 1024, height: 1024 }, + scaledSize: canvasOverrides.bbox ?? { width: 1024, height: 1024 }, + }, + controlLayers: { entities: [] }, + regionalGuidance: { entities: canvasOverrides.regionalGuidance ?? [] }, + rasterLayers: { entities: canvasOverrides.rasterLayers ?? [] }, + inpaintMasks: { entities: canvasOverrides.inpaintMasks ?? [] }, + }, +}); + +const hasReasonWith = (reasons: { content: string }[], key: string) => reasons.some((r) => r.content.includes(key)); + +describe('Ideogram 4 readiness checks - canvas tab', () => { + it('blocks a bbox whose width is not a multiple of 16', () => { + const reasons = getReasonsWhyCannotEnqueueCanvasTab( + buildIdeogram4CanvasArg({ bbox: { width: 1025, height: 1024 } }) as never + ); + expect(hasReasonWith(reasons, 'modelIncompatibleBboxWidth')).toBe(true); + }); + + it('allows a bbox that is a multiple of 16', () => { + const reasons = getReasonsWhyCannotEnqueueCanvasTab( + buildIdeogram4CanvasArg({ bbox: { width: 1024, height: 1024 } }) as never + ); + expect(hasReasonWith(reasons, 'modelIncompatibleBbox')).toBe(false); + }); + + it('blocks an enabled raster layer with content (Ideogram 4 is txt2img only)', () => { + const reasons = getReasonsWhyCannotEnqueueCanvasTab( + buildIdeogram4CanvasArg({ + rasterLayers: [{ id: 'r1', isEnabled: true, type: 'raster_layer', objects: [{}] }], + }) as never + ); + expect(hasReasonWith(reasons, 'ideogram4Txt2ImgOnly')).toBe(true); + }); + + it('blocks an enabled inpaint mask with content', () => { + const reasons = getReasonsWhyCannotEnqueueCanvasTab( + buildIdeogram4CanvasArg({ + inpaintMasks: [{ id: 'm1', isEnabled: true, type: 'inpaint_mask', objects: [{}] }], + }) as never + ); + expect(hasReasonWith(reasons, 'ideogram4Txt2ImgOnly')).toBe(true); + }); + + it('does not block an empty (fully transparent) enabled raster layer', () => { + const reasons = getReasonsWhyCannotEnqueueCanvasTab( + buildIdeogram4CanvasArg({ + rasterLayers: [{ id: 'r1', isEnabled: true, type: 'raster_layer', objects: [] }], + }) as never + ); + expect(hasReasonWith(reasons, 'ideogram4Txt2ImgOnly')).toBe(false); + }); + + it('warns a regional guidance layer whose only input is a negative prompt', () => { + const reasons = getReasonsWhyCannotEnqueueCanvasTab( + buildIdeogram4CanvasArg({ + regionalGuidance: [ + { + id: 'rg1', + isEnabled: true, + type: 'regional_guidance', + objects: [{}], + positivePrompt: null, + negativePrompt: 'no cats', + autoNegative: false, + referenceImages: [], + }, + ], + }) as never + ); + expect(hasReasonWith(reasons, 'rgNegativePromptNotSupported')).toBe(true); + }); + + it('warns a regional guidance layer whose only input is a reference image', () => { + const reasons = getReasonsWhyCannotEnqueueCanvasTab( + buildIdeogram4CanvasArg({ + regionalGuidance: [ + { + id: 'rg1', + isEnabled: true, + type: 'regional_guidance', + objects: [{}], + positivePrompt: null, + negativePrompt: null, + autoNegative: false, + referenceImages: [{ id: 'ri1', config: { model: null, image: null } }], + }, + ], + }) as never + ); + expect(hasReasonWith(reasons, 'rgReferenceImagesNotSupported')).toBe(true); + }); +}); diff --git a/invokeai/frontend/web/src/features/queue/store/readiness.ts b/invokeai/frontend/web/src/features/queue/store/readiness.ts index 1e40cc6ce18..4e13020b2df 100644 --- a/invokeai/frontend/web/src/features/queue/store/readiness.ts +++ b/invokeai/frontend/web/src/features/queue/store/readiness.ts @@ -761,6 +761,52 @@ export const getReasonsWhyCannotEnqueueCanvasTab = (arg: { } } + if (model?.base === 'ideogram-4') { + // Ideogram 4 requires bbox dimensions that are multiples of 16 (enforced by ideogram4_denoise). + const { bbox } = canvas; + const gridSize = getGridSize('ideogram-4'); + + if (bbox.scaleMethod === 'none') { + if (bbox.rect.width % gridSize !== 0) { + reasons.push({ + content: i18n.t('parameters.invoke.modelIncompatibleBboxWidth', { + model: 'Ideogram 4', + width: bbox.rect.width, + multiple: gridSize, + }), + }); + } + if (bbox.rect.height % gridSize !== 0) { + reasons.push({ + content: i18n.t('parameters.invoke.modelIncompatibleBboxHeight', { + model: 'Ideogram 4', + height: bbox.rect.height, + multiple: gridSize, + }), + }); + } + } else { + if (bbox.scaledSize.width % gridSize !== 0) { + reasons.push({ + content: i18n.t('parameters.invoke.modelIncompatibleScaledBboxWidth', { + model: 'Ideogram 4', + width: bbox.scaledSize.width, + multiple: gridSize, + }), + }); + } + if (bbox.scaledSize.height % gridSize !== 0) { + reasons.push({ + content: i18n.t('parameters.invoke.modelIncompatibleScaledBboxHeight', { + model: 'Ideogram 4', + height: bbox.scaledSize.height, + multiple: gridSize, + }), + }); + } + } + } + if (model?.base === 'qwen-image' && model.format === 'gguf_quantized') { // GGUF needs sources for VAE + encoder. Each can come from either a standalone // model or the Component Source (Diffusers). diff --git a/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx b/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx index 025e9677b79..18090657b6f 100644 --- a/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx +++ b/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx @@ -63,18 +63,22 @@ export const AdvancedSettingsAccordion = memo(() => { const selectBadges = useMemo( () => - createMemoizedSelector([selectParamsSlice, selectIsFLUX, selectIsFlux2], (params, isFLUX, isFlux2) => { - const badges: (string | number)[] = []; - // FLUX.2 has VAE built into main model - no badge needed - if (isFLUX && !isFlux2) { - if (vaeConfig) { - let vaeBadge = vaeConfig.name; - if (params.vaePrecision === 'fp16') { - vaeBadge += ` ${params.vaePrecision}`; + createMemoizedSelector( + [selectParamsSlice, selectIsFLUX, selectIsFlux2, selectIsIdeogram4], + (params, isFLUX, isFlux2, isIdeogram4) => { + const badges: (string | number)[] = []; + // FLUX.2 has VAE built into main model - no badge needed + if (isFLUX && !isFlux2) { + if (vaeConfig) { + let vaeBadge = vaeConfig.name; + if (params.vaePrecision === 'fp16') { + vaeBadge += ` ${params.vaePrecision}`; + } + badges.push(vaeBadge); } - badges.push(vaeBadge); - } - } else if (!isFlux2) { + // Ideogram 4 hides the VAE / clip skip / CFG rescale / seamless controls (they don't apply), + // so it must not advertise stale badges for them either. + } else if (!isFlux2 && !isIdeogram4) { if (vaeConfig) { let vaeBadge = vaeConfig.name; if (params.vaePrecision === 'fp16') { diff --git a/tests/backend/ideogram4/test_caption_builder_node.py b/tests/backend/ideogram4/test_caption_builder_node.py index 256dc7e41e8..a1403ffc2e1 100644 --- a/tests/backend/ideogram4/test_caption_builder_node.py +++ b/tests/backend/ideogram4/test_caption_builder_node.py @@ -28,6 +28,8 @@ def test_none_bbox_is_accepted(): [0, 0, 1000, 1000, 7], # too long [-1, 0, 1000, 1000], # negative [0, 0, 1000, 1001], # above 1000 + [900, 900, 100, 100], # inverted (y_min > y_max and x_min > x_max) + [0, 900, 1000, 100], # inverted x only (x_min > x_max) ], ) def test_malformed_bbox_is_rejected(bbox: list[int]): From 965d8dc6cdfbe132e4ef5eaa1cfa1daa6583706d Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Sun, 26 Jul 2026 23:44:33 +0200 Subject: [PATCH 32/33] fix(ideogram4): reject text encoders with weights left on the meta device MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit _load_text_encoder() builds the encoder under accelerate.init_empty_weights() and previously downgraded missing keys to a warning (both the fp8 path via load_fp8_state_dict(strict=False) and the non-fp8 path). A missing non-tied weight therefore stayed on the meta device, so a bad or mismatched encoder appeared to load and only failed later during device movement or encoding. Add _verify_encoder_fully_materialized(): call tie_weights() to materialize tied weights from their source, then hard-fail if any parameter or buffer remains on the meta device. Wire it into both the fp8 and non-fp8 (incl. bnb-nf4) paths and drop the missing-key warning — genuinely missing non-tied weights are now caught as leftover meta tensors, while tied weights are tolerated. This is a state-based, path-agnostic check. Add tests: passes when fully materialized, raises on a leftover meta tensor from a missing non-tied weight, and tolerates a tied weight resolved by tie_weights(). --- .../load/model_loaders/ideogram4.py | 38 ++++++++++--- .../ideogram4/test_text_encoder_loader.py | 57 +++++++++++++++++++ 2 files changed, 87 insertions(+), 8 deletions(-) create mode 100644 tests/backend/ideogram4/test_text_encoder_loader.py diff --git a/invokeai/backend/model_manager/load/model_loaders/ideogram4.py b/invokeai/backend/model_manager/load/model_loaders/ideogram4.py index 1ae8e2a7cad..3b430dc0b36 100644 --- a/invokeai/backend/model_manager/load/model_loaders/ideogram4.py +++ b/invokeai/backend/model_manager/load/model_loaders/ideogram4.py @@ -11,8 +11,8 @@ FLUX nf4. Both transformer branches are returned as a single ``Ideogram4TransformerPair``. """ +import itertools import json -import warnings from pathlib import Path from typing import Optional @@ -47,6 +47,29 @@ def _load_local_state_dict(folder: Path, basename: str) -> dict[str, torch.Tenso return load_file(folder / f"{basename}.safetensors") +def _verify_encoder_fully_materialized(model: torch.nn.Module, *, context: str) -> None: + """Fail if any parameter is still on the meta device after loading the text encoder. + + The encoder is built under ``accelerate.init_empty_weights()`` (every param starts on the meta + device) and then filled from the checkpoint. Missing keys are only acceptable for tied weights, which + ``transformers`` materializes via ``tie_weights()``; any other missing key leaves a meta tensor that + would pass loading but fail later during device movement or encoding. Re-tie, then hard-fail if any + meta tensor remains so a bad/mismatched encoder is rejected at load time instead. + """ + if hasattr(model, "tie_weights"): + model.tie_weights() + meta = [ + name + for name, tensor in itertools.chain(model.named_parameters(), model.named_buffers()) + if getattr(tensor, "is_meta", False) + ] + if meta: + raise RuntimeError( + f"{context}: {len(meta)} parameter(s) remain on the meta device after loading " + f"(missing or mismatched weights): {meta[:10]}" + ) + + @ModelLoaderRegistry.register(base=BaseModelType.Ideogram4, type=ModelType.Main, format=ModelFormat.Diffusers) class Ideogram4DiffusersModel(ModelLoader): """Loads Ideogram 4 main models (nf4 / fp8) bundled in diffusers layout.""" @@ -166,6 +189,7 @@ def _load_text_encoder(self, model_path: Path) -> AnyModel: model: torch.nn.Module = AutoModel.from_config(cfg) swap_linears_to_fp8(model, sd, compute_dtype=compute_dtype) load_fp8_state_dict(model, sd, device=torch.device("cpu"), dtype=compute_dtype, assign=True, strict=False) + _verify_encoder_fully_materialized(model, context="Ideogram 4 fp8 text encoder") model.eval() return model @@ -176,15 +200,13 @@ def _load_text_encoder(self, model_path: Path) -> AnyModel: if is_bnb_nf4: model = quantize_model_nf4(model, modules_to_not_convert=set(), compute_dtype=compute_dtype) - missing, unexpected = model.load_state_dict(sd, strict=False, assign=True) - # Mirror the fp8 helper's policy (load_fp8_state_dict): unexpected keys signal a wrong or - # contaminated checkpoint and must hard-fail; missing keys are expected only for tied weights - # that transformers re-ties itself, so downgrade those to a warning rather than silently - # accepting a partial load that would fail later during encoding. + _, unexpected = model.load_state_dict(sd, strict=False, assign=True) + # Unexpected keys signal a wrong or contaminated checkpoint and must hard-fail. Missing keys are + # acceptable only for tied weights (resolved by _verify_encoder_fully_materialized via + # tie_weights); any genuinely missing non-tied weight is caught there as a leftover meta tensor. if unexpected: raise RuntimeError(f"unexpected keys loading Ideogram 4 text encoder: {unexpected[:10]}") - if missing: - warnings.warn(f"missing keys loading Ideogram 4 text encoder: {missing[:10]}", stacklevel=2) + _verify_encoder_fully_materialized(model, context="Ideogram 4 text encoder") if not is_bnb_nf4: model = model.to(compute_dtype) model.eval() diff --git a/tests/backend/ideogram4/test_text_encoder_loader.py b/tests/backend/ideogram4/test_text_encoder_loader.py new file mode 100644 index 00000000000..887c70af416 --- /dev/null +++ b/tests/backend/ideogram4/test_text_encoder_loader.py @@ -0,0 +1,57 @@ +"""Tests for the Ideogram 4 text-encoder load-completeness guard. + +The encoder is built under accelerate.init_empty_weights() and filled from the checkpoint. A missing +non-tied weight would leave a tensor on the meta device — passing the load but failing later during +device movement / encoding. _verify_encoder_fully_materialized must reject that, while tolerating tied +weights that transformers resolves via tie_weights(). +""" + +import accelerate +import pytest +import torch + +from invokeai.backend.model_manager.load.model_loaders.ideogram4 import _verify_encoder_fully_materialized + + +class _UntiedModel(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.embed = torch.nn.Linear(4, 4, bias=False) + self.extra = torch.nn.Linear(4, 4, bias=False) + + +class _TiedModel(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.embed = torch.nn.Linear(4, 4, bias=False) + self.head = torch.nn.Linear(4, 4, bias=False) + + def tie_weights(self) -> None: + # Mirror transformers: the output weight is tied to (shares storage with) the input embedding. + self.head.weight = self.embed.weight + + +def test_passes_when_fully_materialized() -> None: + """A normally-initialized model (no meta tensors) is accepted.""" + _verify_encoder_fully_materialized(_UntiedModel(), context="test") + + +def test_raises_on_leftover_meta_from_missing_non_tied_weight() -> None: + """A missing non-tied weight leaves a meta tensor and must be rejected.""" + with accelerate.init_empty_weights(): + model = _UntiedModel() + # Simulate loading only `embed`; `extra` stays on the meta device (missing non-tied weight). + model.embed.weight = torch.nn.Parameter(torch.zeros(4, 4)) + with pytest.raises(RuntimeError, match="meta device"): + _verify_encoder_fully_materialized(model, context="test") + + +def test_tolerates_tied_weight_resolved_by_tie_weights() -> None: + """A tied output weight left on meta is materialized by tie_weights() and must not raise.""" + with accelerate.init_empty_weights(): + model = _TiedModel() + # Only the source embedding is "loaded"; head.weight is still meta but is tied to embed. + model.embed.weight = torch.nn.Parameter(torch.zeros(4, 4)) + _verify_encoder_fully_materialized(model, context="test") + assert model.head.weight is model.embed.weight + assert not model.head.weight.is_meta From 1399237718c1af9ecfd86d90627d5d65c4aecbc7 Mon Sep 17 00:00:00 2001 From: Lincoln Stein Date: Mon, 27 Jul 2026 16:49:56 -0400 Subject: [PATCH 33/33] chore(ui): prettier formatting for AdvancedSettingsAccordion The Ideogram 4 badge-suppression branch left the wrapped block at its old indentation, failing `pnpm lint:prettier` in frontend-checks. Formatting only. Co-Authored-By: Claude Opus 5 (1M context) --- .../AdvancedSettingsAccordion.tsx | 39 ++++++++++--------- 1 file changed, 20 insertions(+), 19 deletions(-) diff --git a/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx b/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx index 18090657b6f..29070c39d45 100644 --- a/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx +++ b/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx @@ -79,28 +79,29 @@ export const AdvancedSettingsAccordion = memo(() => { // Ideogram 4 hides the VAE / clip skip / CFG rescale / seamless controls (they don't apply), // so it must not advertise stale badges for them either. } else if (!isFlux2 && !isIdeogram4) { - if (vaeConfig) { - let vaeBadge = vaeConfig.name; - if (params.vaePrecision === 'fp16') { - vaeBadge += ` ${params.vaePrecision}`; + if (vaeConfig) { + let vaeBadge = vaeConfig.name; + if (params.vaePrecision === 'fp16') { + vaeBadge += ` ${params.vaePrecision}`; + } + badges.push(vaeBadge); + } else if (params.vaePrecision === 'fp16') { + badges.push(`VAE ${params.vaePrecision}`); + } + if (params.clipSkip) { + badges.push(`Skip ${params.clipSkip}`); + } + if (params.cfgRescaleMultiplier) { + badges.push(`Rescale ${params.cfgRescaleMultiplier}`); + } + if (params.seamlessXAxis || params.seamlessYAxis) { + badges.push('seamless'); } - badges.push(vaeBadge); - } else if (params.vaePrecision === 'fp16') { - badges.push(`VAE ${params.vaePrecision}`); - } - if (params.clipSkip) { - badges.push(`Skip ${params.clipSkip}`); - } - if (params.cfgRescaleMultiplier) { - badges.push(`Rescale ${params.cfgRescaleMultiplier}`); - } - if (params.seamlessXAxis || params.seamlessYAxis) { - badges.push('seamless'); } - } - return badges; - }), + return badges; + } + ), [vaeConfig] ); const badges = useAppSelector(selectBadges);