[refactor] Flux/Chroma single file implementation + Attention Dispatcher by a-r-r-o-w · Pull Request #11916 · huggingface/diffusers

a-r-r-o-w · 2025-07-14T02:57:20Z

This PR is a continuation of #11368.

Using set_attention_backend:

importtorchfromdiffusersimportFluxPipeline, FluxTransformer2DModel, attention_backendmodel_id="black-forest-labs/FLUX.1-dev"transformer=FluxTransformer2DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16, device_map="cuda")
transformer.set_attention_backend("sage_varlen")
pipe=FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16)
pipe.text_encoder.to("cuda")
pipe.text_encoder_2.to("cuda")
pipe.vae.to("cuda")
prompt="A cat holding a sign that says 'hello world'"image=pipe(prompt, num_inference_steps=28, guidance_scale=4.0).images[0]
image.save("output.png")

Using context manager attention_backend:

importtorchfromdiffusersimportFluxPipeline, FluxTransformer2DModel, attention_backendmodel_id="black-forest-labs/FLUX.1-dev"transformer=FluxTransformer2DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16, device_map="cuda")
pipe=FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16)
pipe.text_encoder.to("cuda")
pipe.text_encoder_2.to("cuda")
pipe.vae.to("cuda")
prompt="A cat holding a sign that says 'hello world'"withattention_backend("_native_cudnn"):
image=pipe(prompt, num_inference_steps=28, guidance_scale=4.0).images[0]
image.save("output.png")

HuggingFaceDocBuilderDev · 2025-07-14T03:06:30Z

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Co-Authored-By: Dhruv Nair <dhruv.nair@gmail.com>

a-r-r-o-w · 2025-07-15T06:34:21Z


 for name, submodule in module.named_modules():
- if not isinstance(submodule, _ATTENTION_CLASSES):
+ if not isinstance(submodule, (*_ATTENTION_CLASSES, AttentionModuleMixin)):


Marking as a TODO for myself. We will no longer need _ATTENTION_CLASSES once everything is AttentionModuleMixin

a-r-r-o-w · 2025-07-15T11:33:20Z

+ if image_rotary_emb is not None:
+ query = apply_rotary_emb(query, image_rotary_emb, sequence_dim=1)
+ key = apply_rotary_emb(key, image_rotary_emb, sequence_dim=1)
+
+ hidden_states = dispatch_attention_fn(
+ query,
+ key,
+ value,
+ attn_mask=attention_mask,
+ dropout_p=0.0,
+ is_causal=False,
+ backend=self._attention_backend,
+ )


cc @yiyixuxu as well for apply_rotary_emb related changes, and because of the attention processor rewrite and minification

Benchmarking the changes with unflatten/flatten/split_with_sizes, I see a consistent 2-4% speedup compared to main on A100

a-r-r-o-w · 2025-07-15T11:38:50Z

+ query, key, value, encoder_query, encoder_key, encoder_value = _get_qkv_projections(
+ attn, hidden_states, encoder_hidden_states
+ )
+
+ query = query.unflatten(-1, (attn.heads, -1))
+ key = key.unflatten(-1, (attn.heads, -1))
+ value = value.unflatten(-1, (attn.heads, -1))
+
+ query = attn.norm_q(query)
+ key = attn.norm_k(key)


The permutation from BSHD to BHSD for torch native SDPA has been moved to the dispatcher. This is done because most attention backend implementations available expect a BSHD tensor, so if we do the permute/transpose here, we will have to undo it in the dispatcher (for say, flash attention). This slows down the inference. Instead, if we move the permute to torch-specific dispatch methods, we keep the original SDPA performance, while also speeding up other backends significantly

Benchmarking only transformer with this change yields an average speedup of ~3% (over 5 runs) on non-pytorch attention backends (only tested xformers and flash-attn-2; but in theory it should speedup all backends that expect BSHD format) in comparison to not having this change (doing a permute and then undoing it within the backend [the older behaviour in previous dispatcher PR])

a-r-r-o-w · 2025-07-15T11:39:47Z

 logger = logging.get_logger(__name__) # pylint: disable=invalid-name


+def _get_projections(attn: "FluxAttention", hidden_states, encoder_hidden_states=None):


@DN6 I've moved these functions here instead of the attention processor. This is so we can re-use them in both IPA and normal processors. LMK if you have something else in mind

a-r-r-o-w · 2025-07-15T19:47:50Z

I think we should have FeedForward and adaptive-layernorm specialized per model too (more true to the single-file implementation). LMK what you think and I'll update accordingly.

For Chroma (which is mostly Flux), we should also do a full single file implementation too instead of importing from Flux. We can add Copied from's

DN6

Nice 👍🏽 some very small things to take care of and we can merge.

a-r-r-o-w · 2025-07-17T12:00:27Z

Failing test is unrelated (WanVACE tests are very flaky for some reason)

…her (huggingface#11916) * update * update * add coauthor Co-Authored-By: Dhruv Nair <dhruv.nair@gmail.com> * improve test * handle ip adapter params correctly * fix chroma qkv fusion test * fix fastercache implementation * fix more tests * fight more tests * add back set_attention_backend * update * update * make style * make fix-copies * make ip adapter processor compatible with attention dispatcher * refactor chroma as well * remove rmsnorm assert * minify and deprecate npu/xla processors --------- Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

okaris · 2025-07-18T20:36:03Z

@a-r-r-o-w @DN6 @tolgacangoz
I got a bit excited about this PR and wanted to give it a go. I love the syntax, both the setter function and the context, great work!

I wanted to also see if it would still compile but got the following error logs:

[t+28s648ms] 0%| | 0/30 [00:00<?, ?it/s]/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/torch/_dynamo/variables/functions.py:1601: UserWarning: Dynamo detected a call to a `functools.lru_cache`-wrapped function. Dynamo ignores the cache wrapper and directly traces the wrapped function. Silent incorrectness is only a *potential* risk, not something we have observed. Enable TORCH_LOGS="+dynamo" for a DEBUG stack trace.
[t+28s650ms] torch._dynamo.utils.warn_once(msg)
[t+28s666ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] Graph break from `Tensor.item()`, consider setting:
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] torch._dynamo.config.capture_scalar_outputs = True
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] or:
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] to include these operations in the captured graph.
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0]
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] Graph break: from user code at:
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/diffusers/models/transformers/transformer_flux.py", line 733, in forward
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] encoder_hidden_states, hidden_states = block(
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/diffusers/models/transformers/transformer_flux.py", line 456, in forward
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] attention_outputs = self.attn(
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/diffusers/models/transformers/transformer_flux.py", line 343, in forward
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] return self.processor(self, hidden_states, encoder_hidden_states, attention_mask, image_rotary_emb, **kwargs)
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/diffusers/models/transformers/transformer_flux.py", line 117, in __call__
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] hidden_states = dispatch_attention_fn(
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/diffusers/models/attention_dispatch.py", line 241, in dispatch_attention_fn
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] return backend_fn(**kwargs)
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/diffusers/models/attention_dispatch.py", line 962, in _sage_varlen_attention
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] _prepare_for_flash_attn_or_sage_varlen(
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/diffusers/models/attention_dispatch.py", line 351, in _prepare_for_flash_attn_or_sage_varlen
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] return _prepare_for_flash_attn_or_sage_varlen_without_mask(batch_size, seq_len_q, seq_len_kv, device)
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/torch/_dynamo/polyfills/__init__.py", line 253, in getattr_and_trace
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] return fn(*args[2:], **kwargs)
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/diffusers/models/attention_dispatch.py", line 321, in _prepare_for_flash_attn_or_sage_varlen_without_mask
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0] max_seqlen_q = seqlens_q.max().item()
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0]
[t+28s667ms] W0718 20:32:55.420000 1 /inferencesh/cache/gpu/uv/archive-v0/bZ7CJbyNBkaHsGR2jn6Vv/torch/_dynamo/variables/tensor.py:1048] [0/0]
[t+28s669ms] 0%| | 0/30 [00:00<?, ?it/s]
[t+28s670ms] [ERROR] Traceback (most recent call last):
[t+28s671ms] File "/server/tasks.py", line 50, in run_task
[t+28s671ms] output = await result
[t+28s671ms] ^^^^^^^^^^^^
[t+28s671ms] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/src/inference.py", line 248, in run
[t+28s671ms] result = self.pipeline(
[t+28s671ms] ^^^^^^^^^^^^^^
[t+28s671ms] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context
[t+28s671ms] return func(*args, **kwargs)
[t+28s671ms] ^^^^^^^^^^^^^^^^^^^^^
[t+28s671ms] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/diffusers/pipelines/flux/pipeline_flux_kontext.py", line 1063, in __call__
[t+28s671ms] noise_pred = self.transformer(
[t+28s671ms] ^^^^^^^^^^^^^^^^^
[t+28s671ms] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py", line 411, in __call__
[t+28s671ms] return super().__call__(*args, **kwargs)
[t+28s671ms] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[t+28s671ms] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
[t+28s671ms] return self._call_impl(*args, **kwargs)
[t+28s671ms] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[t+28s671ms] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
[t+28s671ms] return forward_call(*args, **kwargs)
[t+28s671ms] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[t+28s672ms] File "/inferencesh/apps/gpu/31s80r2z5hw6jskcs081xh8wdb/venv/3.12/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py", line 812, in compile_wrapper
[t+28s672ms] raise e.with_traceback(None) from e.__cause__ # User compiler error
[t+28s672ms] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[t+28s672ms] torch._dynamo.exc.Unsupported: Unsupported Tensor.item() call with capture_scalar_outputs=False
[t+28s672ms] Explanation: Dynamo does not support tracing `Tensor.item()` with config.capture_scalar_outputs=False.

compiling with both

self.pipeline.transformer.compile_repeated_blocks(fullgraph=True)

and

self.pipeline.transformer.to(memory_format=torch.channels_last)
self.pipeline.transformer = torch.compile(
self.pipeline.transformer, mode="max-autotune", fullgraph=True
)

yields the same result

after

self.pipeline.transformer.set_attention_backend("sage_varlen")

a-r-r-o-w added 2 commits July 14, 2025 04:47

update
d7b9e42

update
7e97e43

a-r-r-o-w requested a review from DN6 July 14, 2025 02:57

a-r-r-o-w mentioned this pull request Jul 14, 2025
[refactor] Wan single file implementation #11918
Merged

a-r-r-o-wand others added 7 commits July 14, 2025 07:38

add coauthor
ecabd2a
Co-Authored-By: Dhruv Nair <dhruv.nair@gmail.com>

improve test
ff21b7f

handle ip adapter params correctly
b8f7fe6

Merge branch 'main' into to-single-file/flux
17b678f

fix chroma qkv fusion test
0cda91d

fix fastercache implementation
bc64f12

fix more tests
a0b276d

a-r-r-o-w commented Jul 15, 2025
View reviewed changes

a-r-r-o-w added 6 commits July 15, 2025 09:49

fight more tests
c141520

add back set_attention_backend
4dcd672

update
576da52

update
e909b73

make style
1e7217f

make fix-copies
4f52e34

a-r-r-o-w changed the title ~~[refactor] Flux single file implementation~~[refactor] Flux single file implementation + Attention DispatcherJul 15, 2025

a-r-r-o-w mentioned this pull request Jul 15, 2025
Attention Dispatcher #11368
Closed

make ip adapter processor compatible with attention dispatcher
d9c1683

a-r-r-o-w commented Jul 15, 2025
View reviewed changes

refactor chroma as well
a73cb39

a-r-r-o-w changed the title ~~[refactor] Flux single file implementation + Attention Dispatcher~~[refactor] Flux/Chroma single file implementation + Attention DispatcherJul 15, 2025

DN6 reviewed Jul 16, 2025
View reviewed changes

Comment threadsrc/diffusers/models/transformers/transformer_flux.py Outdated
Comment threadsrc/diffusers/models/attention_processor.py Outdated
Comment threadsrc/diffusers/models/attention_processor.py Outdated

remove rmsnorm assert
1e6b1c5

a-r-r-o-w added 2 commits July 16, 2025 12:51

minify and deprecate npu/xla processors
251bb61

Merge branch 'main' into to-single-file/flux
84d2c84

DN6 approved these changes Jul 16, 2025
View reviewed changes

a-r-r-o-w mentioned this pull request Jul 16, 2025
Context Parallel w/ Ring & Ulysses & Unified Attention #11941
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Merge branch 'main' into to-single-file/flux
9f37b87

a-r-r-o-w added the roadmap Add to current release roadmap label Jul 16, 2025

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a-r-r-o-w merged commit 18c8f10 into mainJul 17, 2025

a-r-r-o-w deleted the to-single-file/flux branch July 17, 2025 12:00

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This was referenced Jul 18, 2025
Flash/Sage varlen does not work with torch.compile #11957
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torch.compile compatibility with varlen APIs #11970
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Kandinsky 5 is finally in Diffusers! #12478
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Add Photon model and pipeline support #12456
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[SANA-Video] Adding 5s pre-trained 480p SANA-Video inference #12584
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[refactor] Flux/Chroma single file implementation + Attention Dispatcher - #11916

[refactor] Flux/Chroma single file implementation + Attention Dispatcher#11916
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		logger = logging.get_logger(__name__) # pylint: disable=invalid-name


		def _get_projections(attn: "FluxAttention", hidden_states, encoder_hidden_states=None):

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