kandinsky5 model/pipeline review

[hlky]

kandinsky5 model/pipeline review

Commit tested: 0f1abc4ae8b0eb2a3b40e82a310507281144c423

Review performed against the repository review rules.

Duplicate search performed against huggingface/diffusers Issues and PRs for kandinsky5, affected class names, output fields, prompt embedding batching, I2I tensor inputs, docs examples, return_dict, dtype/offload behavior, _no_split_modules, and slow-test coverage. Existing related items are noted inline. No GitHub issue was created before this CREATE ISSUE request.

Issue 1: Precomputed prompt embeddings break batching and CFG

Affected code:

diffusers/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py

Lines 788 to 842 in 0f1abc4

	if prompt is not None and isinstance(prompt, str):
	batch_size = 1
	prompt = [prompt]
	elif prompt is not None and isinstance(prompt, list):
	batch_size = len(prompt)
	else:
	batch_size = prompt_embeds_qwen.shape[0]
	# 3. Encode input prompt
	if prompt_embeds_qwen is None:
	prompt_embeds_qwen, prompt_embeds_clip, prompt_cu_seqlens = self.encode_prompt(
	prompt=prompt,
	max_sequence_length=max_sequence_length,
	device=device,
	dtype=dtype,
	)
	if self.guidance_scale > 1.0:
	if negative_prompt is None:
	negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"
	if isinstance(negative_prompt, str):
	negative_prompt = [negative_prompt] * len(prompt) if prompt is not None else [negative_prompt]
	elif len(negative_prompt) != len(prompt):
	raise ValueError(
	f"`negative_prompt` must have same length as `prompt`. Got {len(negative_prompt)} vs {len(prompt)}."
	)
	if negative_prompt_embeds_qwen is None:
	negative_prompt_embeds_qwen, negative_prompt_embeds_clip, negative_prompt_cu_seqlens = (
	self.encode_prompt(
	prompt=negative_prompt,
	max_sequence_length=max_sequence_length,
	device=device,
	dtype=dtype,
	)
	)
	# 4. Prepare timesteps
	self.scheduler.set_timesteps(num_inference_steps, device=device)
	timesteps = self.scheduler.timesteps
	# 5. Prepare latent variables
	num_channels_latents = self.transformer.config.in_visual_dim
	latents = self.prepare_latents(
	batch_size * num_videos_per_prompt,
	num_channels_latents,
	height,
	width,
	num_frames,
	dtype,
	device,
	generator,
	latents,
	)

diffusers/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky_i2v.py

Lines 866 to 923 in 0f1abc4

	if prompt is not None and isinstance(prompt, str):
	batch_size = 1
	prompt = [prompt]
	elif prompt is not None and isinstance(prompt, list):
	batch_size = len(prompt)
	else:
	batch_size = prompt_embeds_qwen.shape[0]
	# 3. Encode input prompt
	if prompt_embeds_qwen is None:
	prompt_embeds_qwen, prompt_embeds_clip, prompt_cu_seqlens = self.encode_prompt(
	prompt=prompt,
	num_videos_per_prompt=num_videos_per_prompt,
	max_sequence_length=max_sequence_length,
	device=device,
	dtype=dtype,
	)
	if self.guidance_scale > 1.0:
	if negative_prompt is None:
	negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"
	if isinstance(negative_prompt, str):
	negative_prompt = [negative_prompt] * len(prompt) if prompt is not None else [negative_prompt]
	elif len(negative_prompt) != len(prompt):
	raise ValueError(
	f"`negative_prompt` must have same length as `prompt`. Got {len(negative_prompt)} vs {len(prompt)}."
	)
	if negative_prompt_embeds_qwen is None:
	negative_prompt_embeds_qwen, negative_prompt_embeds_clip, negative_prompt_cu_seqlens = (
	self.encode_prompt(
	prompt=negative_prompt,
	num_videos_per_prompt=num_videos_per_prompt,
	max_sequence_length=max_sequence_length,
	device=device,
	dtype=dtype,
	)
	)
	# 4. Prepare timesteps
	self.scheduler.set_timesteps(num_inference_steps, device=device)
	timesteps = self.scheduler.timesteps
	# 5. Prepare latent variables with image conditioning
	num_channels_latents = self.transformer.config.in_visual_dim
	latents = self.prepare_latents(
	image=image,
	batch_size=batch_size * num_videos_per_prompt,
	num_channels_latents=num_channels_latents,
	height=height,
	width=width,
	num_frames=num_frames,
	dtype=dtype,
	device=device,
	generator=generator,
	latents=latents,
	)

diffusers/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky_t2i.py

Lines 640 to 695 in 0f1abc4

	if prompt is not None and isinstance(prompt, str):
	batch_size = 1
	prompt = [prompt]
	elif prompt is not None and isinstance(prompt, list):
	batch_size = len(prompt)
	else:
	batch_size = prompt_embeds_qwen.shape[0]
	# 3. Encode input prompt
	if prompt_embeds_qwen is None:
	prompt_embeds_qwen, prompt_embeds_clip, prompt_cu_seqlens = self.encode_prompt(
	prompt=prompt,
	num_images_per_prompt=num_images_per_prompt,
	max_sequence_length=max_sequence_length,
	device=device,
	dtype=dtype,
	)
	if self.guidance_scale > 1.0:
	if negative_prompt is None:
	negative_prompt = ""
	if isinstance(negative_prompt, str):
	negative_prompt = [negative_prompt] * len(prompt) if prompt is not None else [negative_prompt]
	elif len(negative_prompt) != len(prompt):
	raise ValueError(
	f"`negative_prompt` must have same length as `prompt`. Got {len(negative_prompt)} vs {len(prompt)}."
	)
	if negative_prompt_embeds_qwen is None:
	negative_prompt_embeds_qwen, negative_prompt_embeds_clip, negative_prompt_cu_seqlens = (
	self.encode_prompt(
	prompt=negative_prompt,
	num_images_per_prompt=num_images_per_prompt,
	max_sequence_length=max_sequence_length,
	device=device,
	dtype=dtype,
	)
	)
	# 4. Prepare timesteps
	self.scheduler.set_timesteps(num_inference_steps, device=device)
	timesteps = self.scheduler.timesteps
	# 5. Prepare latent variables
	num_channels_latents = self.transformer.config.in_visual_dim
	latents = self.prepare_latents(
	batch_size=batch_size * num_images_per_prompt,
	num_channels_latents=num_channels_latents,
	height=height,
	width=width,
	dtype=dtype,
	device=device,
	generator=generator,
	latents=latents,
	)

diffusers/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky_i2i.py

Lines 678 to 737 in 0f1abc4

	# 2. Define call parameters
	if prompt is not None and isinstance(prompt, str):
	batch_size = 1
	prompt = [prompt]
	elif prompt is not None and isinstance(prompt, list):
	batch_size = len(prompt)
	else:
	batch_size = prompt_embeds_qwen.shape[0]
	# 3. Encode input prompt
	if prompt_embeds_qwen is None:
	prompt_embeds_qwen, prompt_embeds_clip, prompt_cu_seqlens = self.encode_prompt(
	prompt=prompt,
	image=image,
	num_images_per_prompt=num_images_per_prompt,
	max_sequence_length=max_sequence_length,
	device=device,
	dtype=dtype,
	)
	if self.guidance_scale > 1.0:
	if negative_prompt is None:
	negative_prompt = ""
	if isinstance(negative_prompt, str):
	negative_prompt = [negative_prompt] * len(prompt) if prompt is not None else [negative_prompt]
	elif len(negative_prompt) != len(prompt):
	raise ValueError(
	f"`negative_prompt` must have same length as `prompt`. Got {len(negative_prompt)} vs {len(prompt)}."
	)
	if negative_prompt_embeds_qwen is None:
	negative_prompt_embeds_qwen, negative_prompt_embeds_clip, negative_prompt_cu_seqlens = (
	self.encode_prompt(
	prompt=negative_prompt,
	image=image,
	num_images_per_prompt=num_images_per_prompt,
	max_sequence_length=max_sequence_length,
	device=device,
	dtype=dtype,
	)
	)
	# 4. Prepare timesteps
	self.scheduler.set_timesteps(num_inference_steps, device=device)
	timesteps = self.scheduler.timesteps
	# 5. Prepare latent variables with image conditioning
	num_channels_latents = self.transformer.config.in_visual_dim
	latents = self.prepare_latents(
	image=image,
	batch_size=batch_size * num_images_per_prompt,
	num_channels_latents=num_channels_latents,
	height=height,
	width=width,
	dtype=dtype,
	device=device,
	generator=generator,
	latents=latents,
	)

Problem:
When prompt_embeds_qwen is supplied, the pipelines skip encode_prompt, so embeddings and prompt_cu_seqlens are not expanded for num_images_per_prompt / num_videos_per_prompt, but latents are. CFG also sizes default/string negative prompts from len(prompt), which fails when prompt=None and embeddings are used.

Impact:
Valid precomputed-embedding calls either crash with batch mismatches or fail before denoising. This affects all four Kandinsky5 pipelines.

Reproduction:

import torch
from types import SimpleNamespace
from diffusers import FlowMatchEulerDiscreteScheduler, Kandinsky5T2IPipeline

class ReproPipeline(Kandinsky5T2IPipeline):
 @property
 def _execution_device(self):
 return torch.device("cpu")

class DummyModule(torch.nn.Module):
 @property
 def dtype(self):
 return torch.float32

class DummyTransformer(DummyModule):
 config = SimpleNamespace(in_visual_dim=4)
 visual_cond = False
 def forward(self, hidden_states, encoder_hidden_states, **kwargs):
 assert hidden_states.shape[0] == encoder_hidden_states.shape[0], (
 hidden_states.shape,
 encoder_hidden_states.shape,
 )
 return SimpleNamespace(sample=torch.zeros_like(hidden_states[..., :4]))

class DummyVAE(DummyModule):
 config = SimpleNamespace(scaling_factor=1.0)

pipe = ReproPipeline(DummyTransformer(), DummyVAE(), DummyModule(), None, DummyModule(), None, FlowMatchEulerDiscreteScheduler())
pipe.resolutions = [(64, 64)]
pipe.set_progress_bar_config(disable=True)

emb = torch.zeros(2, 4, 8)
pooled = torch.zeros(2, 6)
cu = torch.tensor([0, 4, 8], dtype=torch.int32)

pipe(
 prompt=None,
 prompt_embeds_qwen=emb,
 prompt_embeds_clip=pooled,
 prompt_cu_seqlens=cu,
 negative_prompt_embeds_qwen=emb,
 negative_prompt_embeds_clip=pooled,
 negative_prompt_cu_seqlens=cu,
 height=64,
 width=64,
 guidance_scale=4.0,
 num_images_per_prompt=2,
 num_inference_steps=1,
 output_type="latent",
)

Relevant precedent:

diffusers/src/diffusers/pipelines/qwenimage/pipeline_qwenimage.py

Lines 615 to 631 in 0f1abc4

	prompt_embeds, prompt_embeds_mask = self.encode_prompt(
	prompt=prompt,
	prompt_embeds=prompt_embeds,
	prompt_embeds_mask=prompt_embeds_mask,
	device=device,
	num_images_per_prompt=num_images_per_prompt,
	max_sequence_length=max_sequence_length,
	)
	if do_true_cfg:
	negative_prompt_embeds, negative_prompt_embeds_mask = self.encode_prompt(
	prompt=negative_prompt,
	prompt_embeds=negative_prompt_embeds,
	prompt_embeds_mask=negative_prompt_embeds_mask,
	device=device,
	num_images_per_prompt=num_images_per_prompt,
	max_sequence_length=max_sequence_length,
	)

Suggested fix:

# Route provided embeds through a helper that mirrors encode_prompt's repeat logic.
if prompt_embeds_qwen is not None:
 prompt_embeds_qwen, prompt_embeds_clip, prompt_cu_seqlens = self._repeat_prompt_embeds(
 prompt_embeds_qwen, prompt_embeds_clip, prompt_cu_seqlens, num_images_per_prompt, device
 )

negative_batch_size = batch_size
if isinstance(negative_prompt, str):
 negative_prompt = [negative_prompt] * negative_batch_size
elif negative_prompt is not None and len(negative_prompt) != negative_batch_size:
 raise ValueError(...)

Issue 2: Image pipelines return .image instead of the standard .images

Affected code:

diffusers/src/diffusers/pipelines/kandinsky5/pipeline_output.py

Lines 23 to 35 in 0f1abc4

	@dataclass
	class KandinskyImagePipelineOutput(BaseOutput):
	r"""
	Output class for kandinsky image pipelines.
	Args:
	image (`torch.Tensor`, `np.ndarray`, or list[PIL.Image.Image]):
	List of image outputs - It can be a nested list of length `batch_size,` with each sub-list containing
	denoised PIL image. It can also be a NumPy array or Torch tensor of shape `(batch_size, channels, height,
	width)`.
	"""
	image: torch.Tensor

diffusers/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky_t2i.py

Lines 813 to 816 in 0f1abc4

	if not return_dict:
	return (image,)
	return KandinskyImagePipelineOutput(image=image)

diffusers/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky_i2i.py

Lines 858 to 861 in 0f1abc4

	if not return_dict:
	return (image,)
	return KandinskyImagePipelineOutput(image=image)

Problem:
Kandinsky5 image pipelines return KandinskyImagePipelineOutput(image=...). Diffusers image pipelines conventionally return an images field. The source docstring examples also call .frames[0], which is neither the actual field nor the image-pipeline convention.

Impact:
User code expecting standard Diffusers output (pipe(...).images) fails, and generated docs from source examples are misleading.

Reproduction:

import torch
from diffusers.pipelines.kandinsky5.pipeline_output import KandinskyImagePipelineOutput

out = KandinskyImagePipelineOutput(image=torch.zeros(1, 3, 4, 4))
print(list(out.keys()))
print(hasattr(out, "images"), hasattr(out, "frames"))

Relevant precedent:

diffusers/src/diffusers/pipelines/pipeline_utils.py

Lines 121 to 131 in 0f1abc4

	class ImagePipelineOutput(BaseOutput):
	"""
	Output class for image pipelines.
	Args:
	images (`List[PIL.Image.Image]` or `np.ndarray`)
	List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
	num_channels)`.
	"""
	images: list[PIL.Image.Image] | np.ndarray

Suggested fix:

from ..pipeline_utils import ImagePipelineOutput

# T2I / I2I return path
return ImagePipelineOutput(images=image)

Update tests and docs to use .images[0].

Issue 3: I2I advertises PipelineImageInput but only handles PIL images in prompt encoding

Affected code:

diffusers/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky_i2i.py

Lines 184 to 212 in 0f1abc4

	def _encode_prompt_qwen(
	self,
	prompt: list[str],
	image: PipelineImageInput | None = None,
	device: torch.device | None = None,
	max_sequence_length: int = 1024,
	dtype: torch.dtype | None = None,
	):
	"""
	Encode prompt using Qwen2.5-VL text encoder.
	This method processes the input prompt through the Qwen2.5-VL model to generate text embeddings suitable for
	image generation.
	Args:
	prompt list[str]: Input list of prompts
	image (PipelineImageInput): Input list of images to condition the generation on
	device (torch.device): Device to run encoding on
	max_sequence_length (int): Maximum sequence length for tokenization
	dtype (torch.dtype): Data type for embeddings
	Returns:
	tuple[torch.Tensor, torch.Tensor]: Text embeddings and cumulative sequence lengths
	"""
	device = device or self._execution_device
	dtype = dtype or self.text_encoder.dtype
	if not isinstance(image, list):
	image = [image]
	image = [i.resize((i.size[0] // 2, i.size[1] // 2)) for i in image]

diffusers/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky_i2i.py

Lines 650 to 651 in 0f1abc4

	if height is None and width is None:
	width, height = image[0].size if isinstance(image, list) else image.size

Problem:
Kandinsky5I2IPipeline.__call__ and _encode_prompt_qwen use PIL-only .size and .resize access. The public type is PipelineImageInput, and the docstring says tensors are accepted, but tensor/NumPy inputs fail before preprocessing can normalize them.

Impact:
Valid Diffusers image input types are rejected for I2I, despite being accepted by the VAE image processor path.

Reproduction:

import torch
from diffusers import Kandinsky5I2IPipeline

pipe = object.__new__(Kandinsky5I2IPipeline)
pipe._encode_prompt_qwen(
 prompt=["edit it"],
 image=torch.zeros(1, 3, 64, 64),
 device=torch.device("cpu"),
 dtype=torch.float32,
)

Relevant precedent:
VaeImageProcessor.preprocess is already used later for the VAE path; the Qwen image path should normalize the same public input types before resizing.

Suggested fix:

if not isinstance(image, list):
 image = [image]

# Normalize non-PIL inputs before calling Qwen processor.
image = [self.image_processor.numpy_to_pil(i)[0] if not hasattr(i, "resize") else i for i in image]
image = [i.resize((i.size[0] // 2, i.size[1] // 2)) for i in image]

Issue 4: Transformer return, dtype, and device-map contracts are inconsistent

Affected code:

diffusers/src/diffusers/models/transformers/transformer_kandinsky.py

Lines 309 to 312 in 0f1abc4

	def apply_rotary(x, rope):
	x_ = x.reshape(*x.shape[:-1], -1, 1, 2).to(torch.float32)
	x_out = (rope * x_).sum(dim=-1)
	return x_out.reshape(*x.shape).to(torch.bfloat16)

diffusers/src/diffusers/models/transformers/transformer_kandinsky.py

Lines 522 to 527 in 0f1abc4

	_repeated_blocks = [
	"Kandinsky5TransformerEncoderBlock",
	"Kandinsky5TransformerDecoderBlock",
	]
	_keep_in_fp32_modules = ["time_embeddings", "modulation", "visual_modulation", "text_modulation"]
	_supports_gradient_checkpointing = True

diffusers/src/diffusers/models/transformers/transformer_kandinsky.py

Lines 665 to 668 in 0f1abc4

	if not return_dict:
	return x
	return Transformer2DModelOutput(sample=x)

Problem:
return_dict=False returns a bare tensor instead of a one-element tuple. The rotary helper hard-casts through torch.bfloat16, quantizing non-bf16 runs. The class also declares repeated blocks but no _no_split_modules.

Impact:
The public model return contract differs from related transformers, dtype behavior is not clean for fp32/fp16, and device_map="auto" lacks block no-split guidance.

Reproduction:

import torch
from diffusers import Kandinsky5Transformer3DModel

model = Kandinsky5Transformer3DModel(
 in_visual_dim=4, in_text_dim=8, in_text_dim2=6, time_dim=8, out_visual_dim=4,
 patch_size=(1, 1, 1), model_dim=8, ff_dim=16, num_text_blocks=1,
 num_visual_blocks=1, axes_dims=(2, 2, 4), attention_type="regular",
).eval()

out = model(
 hidden_states=torch.randn(1, 1, 2, 2, 4),
 encoder_hidden_states=torch.randn(1, 3, 8),
 timestep=torch.tensor([1.0]),
 pooled_projections=torch.randn(1, 6),
 visual_rope_pos=[torch.arange(1), torch.arange(2), torch.arange(2)],
 text_rope_pos=torch.arange(3),
 return_dict=False,
)
print(type(out), getattr(out, "shape", None))
print(getattr(Kandinsky5Transformer3DModel, "_no_split_modules", None))

Relevant precedent:

diffusers/src/diffusers/models/transformers/transformer_flux.py

Lines 775 to 778 in 0f1abc4

	if not return_dict:
	return (output,)
	return Transformer2DModelOutput(sample=output)

diffusers/src/diffusers/models/transformers/transformer_qwenimage.py

Lines 990 to 993 in 0f1abc4

	if not return_dict:
	return (output,)
	return Transformer2DModelOutput(sample=output)

diffusers/src/diffusers/models/transformers/transformer_wan.py

Lines 705 to 708 in 0f1abc4

	if not return_dict:
	return (output,)
	return Transformer2DModelOutput(sample=output)

Duplicate status:
The dtype/no-split parts overlap with #13597 and the older dtype/autocast fix in #12814 / #12809. The return_dict=False tuple issue was not found as a duplicate.

Suggested fix:

# Rotary helper
orig_dtype = x.dtype
x_ = x.reshape(*x.shape[:-1], -1, 1, 2).float()
x_out = (rope * x_).sum(dim=-1)
return x_out.reshape(*x.shape).to(orig_dtype)

# Class metadata
_no_split_modules = ["Kandinsky5TransformerEncoderBlock", "Kandinsky5TransformerDecoderBlock"]

# return_dict=False
if not return_dict:
 return (x,)

Issue 5: I2V documentation example is not runnable

Affected code:

diffusers/docs/source/en/api/pipelines/kandinsky5_video.md

Lines 171 to 204 in 0f1abc4

	```python
	import torch
	from diffusers import Kandinsky5T2VPipeline
	from diffusers.utils import export_to_video
	# Load the pipeline
	model_id = "kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers"
	pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
	pipe = pipe.to("cuda")
	pipeline.transformer.set_attention_backend("flex") # <--- Set attention bakend to Flex
	pipeline.enable_model_cpu_offload() # <--- Enable cpu offloading for single GPU inference
	pipeline.transformer.compile(mode="max-autotune-no-cudagraphs", dynamic=True) # <--- Compile with max-autotune-no-cudagraphs
	# Generate video
	image = load_image(
	"https://huggingface.co/kandinsky-community/kandinsky-3/resolve/main/assets/title.jpg?download=true"
	)
	height = 896
	width = 896
	image = image.resize((width, height))
	prompt = "An funny furry creture smiles happily and holds a sign that says 'Kandinsky'"
	negative_prompt = ""
	output = pipe(
	prompt=prompt,
	negative_prompt=negative_prompt,
	height=height,
	width=width,
	num_frames=121, # ~5 seconds at 24fps
	num_inference_steps=50,
	guidance_scale=5.0,
	).frames[0]

Problem:
The I2V example imports and instantiates Kandinsky5T2VPipeline for an I2V checkpoint, uses pipeline.* even though the variable is named pipe, uses load_image without importing it, and never passes image=image to the call.

Impact:
Users following the docs cannot run image-to-video inference.

Reproduction:

from pathlib import Path

text = Path("docs/source/en/api/pipelines/kandinsky5_video.md").read_text()
section = text.split("### Basic Image-to-Video Generation", 1)[1].split("## Kandinsky5T2VPipeline", 1)[0]

assert "from diffusers import Kandinsky5I2VPipeline" in section
assert "from diffusers.utils import export_to_video, load_image" in section
assert "pipe.enable_model_cpu_offload()" in section
assert "image=image" in section

Relevant precedent:
The source docstring for Kandinsky5I2VPipeline uses the correct pipeline class and imports:

diffusers/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky_i2v.py

Lines 61 to 88 in 0f1abc4

	```python
	>>> import torch
	>>> from diffusers import Kandinsky5I2VPipeline
	>>> from diffusers.utils import export_to_video, load_image
	>>> # Available models:
	>>> # kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers
	>>> model_id = "kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers"
	>>> pipe = Kandinsky5I2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
	>>> pipe = pipe.to("cuda")
	>>> image = load_image(
	... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
	... )
	>>> prompt = "An astronaut floating in space with Earth in the background, cinematic shot"
	>>> negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"
	>>> output = pipe(
	... image=image,
	... prompt=prompt,
	... negative_prompt=negative_prompt,
	... height=512,
	... width=768,
	... num_frames=121,
	... num_inference_steps=50,
	... guidance_scale=5.0,
	... ).frames[0]

Suggested fix:

from diffusers import Kandinsky5I2VPipeline
from diffusers.utils import export_to_video, load_image

pipe = Kandinsky5I2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipe.to("cuda")
pipe.transformer.set_attention_backend("flex")
pipe.enable_model_cpu_offload()

output = pipe(
 image=image,
 prompt=prompt,
 negative_prompt=negative_prompt,
 height=height,
 width=width,
 num_frames=121,
 num_inference_steps=50,
 guidance_scale=5.0,
).frames[0]

Issue 6: Slow tests are missing and several fast coverage paths are skipped

Affected code:

diffusers/tests/pipelines/kandinsky5/test_kandinsky5.py

Lines 200 to 209 in 0f1abc4

	@unittest.skip("Only SDPA or NABLA (flex)")
	def test_xformers_memory_efficient_attention(self):
	pass
	@unittest.skip("TODO:Test does not work")
	def test_encode_prompt_works_in_isolation(self):
	pass
	@unittest.skip("TODO: revisit")
	def test_inference_batch_single_identical(self):

diffusers/tests/pipelines/kandinsky5/test_kandinsky5_i2v.py

Lines 201 to 210 in 0f1abc4

	@unittest.skip("TODO:Test does not work")
	def test_encode_prompt_works_in_isolation(self):
	pass
	@unittest.skip("TODO: revisit")
	def test_callback_inputs(self):
	pass
	@unittest.skip("TODO: revisit")
	def test_inference_batch_single_identical(self):

diffusers/tests/pipelines/kandinsky5/test_kandinsky5_i2i.py

Lines 195 to 212 in 0f1abc4

	@unittest.skip("TODO: Test does not work")
	def test_encode_prompt_works_in_isolation(self):
	pass
	@unittest.skip("TODO: revisit, Batch isnot yet supported in this pipeline")
	def test_num_images_per_prompt(self):
	pass
	@unittest.skip("TODO: revisit, Batch isnot yet supported in this pipeline")
	def test_inference_batch_single_identical(self):
	pass
	@unittest.skip("TODO: revisit, Batch isnot yet supported in this pipeline")
	def test_inference_batch_consistent(self):
	pass
	@unittest.skip("TODO: revisit, not working")
	def test_float16_inference(self):

diffusers/tests/pipelines/kandinsky5/test_kandinsky5_t2i.py

Lines 193 to 207 in 0f1abc4

	@unittest.skip("Test not supported")
	def test_attention_slicing_forward_pass(self):
	pass
	@unittest.skip("Only SDPA or NABLA (flex)")
	def test_xformers_memory_efficient_attention(self):
	pass
	@unittest.skip("All encoders are needed")
	def test_encode_prompt_works_in_isolation(self):
	pass
	@unittest.skip("Meant for eiter FP32 or BF16 inference")
	def test_float16_inference(self):
	pass

Problem:
There are fast tests for all four pipelines, but no @slow or integration tests for Kandinsky5. Multiple fast tests for encode-prompt isolation, callback inputs, batch behavior, num_images_per_prompt, and float16 are skipped.

Impact:
The public failures above are not covered. In particular, embedding-only calls, tensor I2I inputs, callback behavior, and official checkpoint smoke tests are missing.

Reproduction:

from pathlib import Path

for path in sorted(Path("tests/pipelines/kandinsky5").glob("test_*.py")):
 text = path.read_text(encoding="utf-8")
 print(path.as_posix(), "slow=", "@slow" in text, "nightly=", "@nightly" in text, "skip=", "@unittest.skip" in text)

print([
 p.as_posix()
 for p in Path("tests/models").rglob("test_*.py")
 if "Kandinsky5Transformer3DModel" in p.read_text(errors="ignore")
])

Relevant precedent:
Existing Kandinsky families include slow/integration coverage:

diffusers/tests/pipelines/kandinsky3/test_kandinsky3.py

Lines 174 to 199 in 0f1abc4

	class Kandinsky3PipelineIntegrationTests(unittest.TestCase):
	def setUp(self):
	# clean up the VRAM before each test
	super().setUp()
	gc.collect()
	backend_empty_cache(torch_device)
	def tearDown(self):
	# clean up the VRAM after each test
	super().tearDown()
	gc.collect()
	backend_empty_cache(torch_device)
	def test_kandinskyV3(self):
	pipe = AutoPipelineForText2Image.from_pretrained(
	"kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16
	)
	pipe.enable_model_cpu_offload(device=torch_device)
	pipe.set_progress_bar_config(disable=None)
	prompt = "A photograph of the inside of a subway train. There are raccoons sitting on the seats. One of them is reading a newspaper. The window shows the city in the background."
	generator = torch.Generator(device="cpu").manual_seed(0)
	image = pipe(prompt, num_inference_steps=5, generator=generator).images[0]

diffusers/tests/pipelines/kandinsky2_2/test_kandinsky.py

Lines 227 to 262 in 0f1abc4

	class KandinskyV22PipelineIntegrationTests(unittest.TestCase):
	def setUp(self):
	# clean up the VRAM before each test
	super().setUp()
	gc.collect()
	backend_empty_cache(torch_device)
	def tearDown(self):
	# clean up the VRAM after each test
	super().tearDown()
	gc.collect()
	backend_empty_cache(torch_device)
	def test_kandinsky_text2img(self):
	expected_image = load_numpy(
	"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
	"/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"
	)
	pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
	"kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
	)
	pipe_prior.enable_model_cpu_offload(device=torch_device)
	pipeline = KandinskyV22Pipeline.from_pretrained(
	"kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
	)
	pipeline.enable_model_cpu_offload(device=torch_device)
	pipeline.set_progress_bar_config(disable=None)
	prompt = "red cat, 4k photo"
	generator = torch.Generator(device="cpu").manual_seed(0)
	image_emb, zero_image_emb = pipe_prior(
	prompt,
	generator=generator,

Suggested fix:
Add slow smoke tests for the published T2V, I2V, T2I, and I2I checkpoints, and unskip or replace the fast tests for encode-prompt isolation, callbacks, batching, num_images_per_prompt, and dtype behavior. Local .venv fast-test execution currently fails at collection because the installed torch lacks torch._C._distributed_c10d, so I could not run the shared PipelineTesterMixin suite end to end in this environment.