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Improve Wan docstrings (#11689)

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* improve docstrings for wan

* Apply suggestions from code review

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* make style

---------

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
This commit is contained in:
Aryan
2025-06-12 01:18:40 +05:30
committed by GitHub
parent 91545666e0
commit f3e09114f2
4 changed files with 46 additions and 16 deletions
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11
src/diffusers/pipelines/wan/pipeline_wan.py
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@@ -388,8 +388,10 @@ class WanPipeline(DiffusionPipeline, WanLoraLoaderMixin):
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
The prompt or prompts to guide the image generation. If not defined, pass `prompt_embeds` instead.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to avoid during image generation. If not defined, pass `negative_prompt_embeds`
instead. Ignored when not using guidance (`guidance_scale` < `1`).
height (`int`, defaults to `480`):
The height in pixels of the generated image.
width (`int`, defaults to `832`):
@@ -434,8 +436,9 @@ class WanPipeline(DiffusionPipeline, WanLoraLoaderMixin):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
autocast_dtype (`torch.dtype`, *optional*, defaults to `torch.bfloat16`):
The dtype to use for the torch.amp.autocast.
max_sequence_length (`int`, defaults to `512`):
The maximum sequence length of the text encoder. If the prompt is longer than this, it will be
truncated. If the prompt is shorter, it will be padded to this length.
Examples:

10
src/diffusers/pipelines/wan/pipeline_wan_i2v.py
View File

@@ -562,12 +562,10 @@ class WanImageToVideoPipeline(DiffusionPipeline, WanLoraLoaderMixin):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
max_sequence_length (`int`, *optional*, defaults to `512`):
The maximum sequence length of the prompt.
shift (`float`, *optional*, defaults to `5.0`):
The shift of the flow.
autocast_dtype (`torch.dtype`, *optional*, defaults to `torch.bfloat16`):
The dtype to use for the torch.amp.autocast.
max_sequence_length (`int`, defaults to `512`):
The maximum sequence length of the text encoder. If the prompt is longer than this, it will be
truncated. If the prompt is shorter, it will be padded to this length.
Examples:
Returns:

32
src/diffusers/pipelines/wan/pipeline_wan_vace.py
View File

@@ -687,8 +687,33 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`
instead.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
video (`List[PIL.Image.Image]`, *optional*):
The input video or videos to be used as a starting point for the generation. The video should be a list
of PIL images, a numpy array, or a torch tensor. Currently, the pipeline only supports generating one
video at a time.
mask (`List[PIL.Image.Image]`, *optional*):
The input mask defines which video regions to condition on and which to generate. Black areas in the
mask indicate conditioning regions, while white areas indicate regions for generation. The mask should
be a list of PIL images, a numpy array, or a torch tensor. Currently supports generating a single video
at a time.
reference_images (`List[PIL.Image.Image]`, *optional*):
A list of one or more reference images as extra conditioning for the generation. For example, if you
are trying to inpaint a video to change the character, you can pass reference images of the new
character here. Refer to the Diffusers [examples](https://github.com/huggingface/diffusers/pull/11582)
and original [user
guide](https://github.com/ali-vilab/VACE/blob/0897c6d055d7d9ea9e191dce763006664d9780f8/UserGuide.md)
for a full list of supported tasks and use cases.
conditioning_scale (`float`, `List[float]`, `torch.Tensor`, defaults to `1.0`):
The conditioning scale to be applied when adding the control conditioning latent stream to the
denoising latent stream in each control layer of the model. If a float is provided, it will be applied
uniformly to all layers. If a list or tensor is provided, it should have the same length as the number
of control layers in the model (`len(transformer.config.vace_layers)`).
height (`int`, defaults to `480`):
The height in pixels of the generated image.
width (`int`, defaults to `832`):
@@ -733,8 +758,9 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
autocast_dtype (`torch.dtype`, *optional*, defaults to `torch.bfloat16`):
The dtype to use for the torch.amp.autocast.
max_sequence_length (`int`, defaults to `512`):
The maximum sequence length of the text encoder. If the prompt is longer than this, it will be
truncated. If the prompt is shorter, it will be padded to this length.
Examples:

9
src/diffusers/pipelines/wan/pipeline_wan_video2video.py
View File

@@ -508,7 +508,7 @@ class WanVideoToVideoPipeline(DiffusionPipeline, WanLoraLoaderMixin):
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`
instead.
height (`int`, defaults to `480`):
The height in pixels of the generated image.
@@ -525,6 +525,8 @@ class WanVideoToVideoPipeline(DiffusionPipeline, WanLoraLoaderMixin):
of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
`guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
the text `prompt`, usually at the expense of lower image quality.
strength (`float`, defaults to `0.8`):
Higher strength leads to more differences between original image and generated video.
num_videos_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
@@ -554,8 +556,9 @@ class WanVideoToVideoPipeline(DiffusionPipeline, WanLoraLoaderMixin):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
autocast_dtype (`torch.dtype`, *optional*, defaults to `torch.bfloat16`):
The dtype to use for the torch.amp.autocast.
max_sequence_length (`int`, defaults to `512`):
The maximum sequence length of the text encoder. If the prompt is longer than this, it will be
truncated. If the prompt is shorter, it will be padded to this length.
Examples: