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[WIP]Add Wan2.2 Animate Pipeline (Continuation of #12442 by tolgacangoz) (#12526)

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---------

Co-authored-by: Tolga Cangöz <mtcangoz@gmail.com>
Co-authored-by: Tolga Cangöz <46008593+tolgacangoz@users.noreply.github.com>
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2025-11-12 18:52:31 -08:00
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docs/source/en/_toctree.yml
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@@ -387,6 +387,8 @@
title: Transformer2DModel
- local: api/models/transformer_temporal
title: TransformerTemporalModel
- local: api/models/wan_animate_transformer_3d
title: WanAnimateTransformer3DModel
- local: api/models/wan_transformer_3d
title: WanTransformer3DModel
title: Transformers
docs/source/en/api/models/wan_animate_transformer_3d.md
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<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License. -->
# WanAnimateTransformer3DModel
A Diffusion Transformer model for 3D video-like data was introduced in [Wan Animate](https://github.com/Wan-Video/Wan2.2) by the Alibaba Wan Team.
The model can be loaded with the following code snippet.
```python
from diffusers import WanAnimateTransformer3DModel
transformer = WanAnimateTransformer3DModel.from_pretrained("Wan-AI/Wan2.2-Animate-14B-720P-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
```
## WanAnimateTransformer3DModel
[[autodoc]] WanAnimateTransformer3DModel
## Transformer2DModelOutput
[[autodoc]] models.modeling_outputs.Transformer2DModelOutput
docs/source/en/api/pipelines/wan.md
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@@ -40,6 +40,7 @@ The following Wan models are supported in Diffusers:
- [Wan 2.2 T2V 14B](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers)
- [Wan 2.2 I2V 14B](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers)
- [Wan 2.2 TI2V 5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers)
- [Wan 2.2 Animate 14B](https://huggingface.co/Wan-AI/Wan2.2-Animate-14B-Diffusers)
> [!TIP]
> Click on the Wan models in the right sidebar for more examples of video generation.
@@ -95,15 +96,15 @@ pipeline = WanPipeline.from_pretrained(
pipeline.to("cuda")
prompt = """
The camera rushes from far to near in a low-angle shot,
revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in
for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground.
Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic
The camera rushes from far to near in a low-angle shot,
revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in
for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground.
Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic
shadows and warm highlights. Medium composition, front view, low angle, with depth of field.
"""
negative_prompt = """
Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
"""
@@ -150,15 +151,15 @@ pipeline.transformer = torch.compile(
)
prompt = """
The camera rushes from far to near in a low-angle shot,
revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in
for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground.
Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic
The camera rushes from far to near in a low-angle shot,
revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in
for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground.
Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic
shadows and warm highlights. Medium composition, front view, low angle, with depth of field.
"""
negative_prompt = """
Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
"""
@@ -249,6 +250,220 @@ The code snippets available in [this](https://github.com/huggingface/diffusers/p
The general rule of thumb to keep in mind when preparing inputs for the VACE pipeline is that the input images, or frames of a video that you want to use for conditioning, should have a corresponding mask that is black in color. The black mask signifies that the model will not generate new content for that area, and only use those parts for conditioning the generation process. For parts/frames that should be generated by the model, the mask should be white in color.
</hfoption>
</hfoptions>
### Wan-Animate: Unified Character Animation and Replacement with Holistic Replication
[Wan-Animate](https://huggingface.co/papers/2509.14055) by the Wan Team.
*We introduce Wan-Animate, a unified framework for character animation and replacement. Given a character image and a reference video, Wan-Animate can animate the character by precisely replicating the expressions and movements of the character in the video to generate high-fidelity character videos. Alternatively, it can integrate the animated character into the reference video to replace the original character, replicating the scene's lighting and color tone to achieve seamless environmental integration. Wan-Animate is built upon the Wan model. To adapt it for character animation tasks, we employ a modified input paradigm to differentiate between reference conditions and regions for generation. This design unifies multiple tasks into a common symbolic representation. We use spatially-aligned skeleton signals to replicate body motion and implicit facial features extracted from source images to reenact expressions, enabling the generation of character videos with high controllability and expressiveness. Furthermore, to enhance environmental integration during character replacement, we develop an auxiliary Relighting LoRA. This module preserves the character's appearance consistency while applying the appropriate environmental lighting and color tone. Experimental results demonstrate that Wan-Animate achieves state-of-the-art performance. We are committed to open-sourcing the model weights and its source code.*
The project page: https://humanaigc.github.io/wan-animate
This model was mostly contributed by [M. Tolga Cangöz](https://github.com/tolgacangoz).
#### Usage
The Wan-Animate pipeline supports two modes of operation:
1. **Animation Mode** (default): Animates a character image based on motion and expression from reference videos
2. **Replacement Mode**: Replaces a character in a background video with a new character while preserving the scene
##### Prerequisites
Before using the pipeline, you need to preprocess your reference video to extract:
- **Pose video**: Contains skeletal keypoints representing body motion
- **Face video**: Contains facial feature representations for expression control
For replacement mode, you additionally need:
- **Background video**: The original video containing the scene
- **Mask video**: A mask indicating where to generate content (white) vs. preserve original (black)
> [!NOTE]
> The preprocessing tools are available in the original Wan-Animate repository. Integration of these preprocessing steps into Diffusers is planned for a future release.
The example below demonstrates how to use the Wan-Animate pipeline:
<hfoptions id="Animate usage">
<hfoption id="Animation mode">
```python
import numpy as np
import torch
from diffusers import AutoencoderKLWan, WanAnimatePipeline
from diffusers.utils import export_to_video, load_image, load_video
from transformers import CLIPVisionModel
model_id = "Wan-AI/Wan2.2-Animate-14B-Diffusers"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanAnimatePipeline.from_pretrained(
model_id, vae=vae, torch_dtype=torch.bfloat16
)
pipe.to("cuda")
# Load character image and preprocessed videos
image = load_image("path/to/character.jpg")
pose_video = load_video("path/to/pose_video.mp4") # Preprocessed skeletal keypoints
face_video = load_video("path/to/face_video.mp4") # Preprocessed facial features
# Resize image to match VAE constraints
def aspect_ratio_resize(image, pipe, max_area=720 * 1280):
aspect_ratio = image.height / image.width
mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
image = image.resize((width, height))
return image, height, width
image, height, width = aspect_ratio_resize(image, pipe)
prompt = "A person dancing energetically in a studio with dynamic lighting and professional camera work"
negative_prompt = "blurry, low quality, distorted, deformed, static, poorly drawn"
# Generate animated video
output = pipe(
image=image,
pose_video=pose_video,
face_video=face_video,
prompt=prompt,
negative_prompt=negative_prompt,
height=height,
width=width,
num_frames=81,
guidance_scale=5.0,
mode="animation", # Animation mode (default)
).frames[0]
export_to_video(output, "animated_character.mp4", fps=16)
```
</hfoption>
<hfoption id="Replacement mode">
```python
import numpy as np
import torch
from diffusers import AutoencoderKLWan, WanAnimatePipeline
from diffusers.utils import export_to_video, load_image, load_video
from transformers import CLIPVisionModel
model_id = "Wan-AI/Wan2.2-Animate-14B-Diffusers"
image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float16)
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanAnimatePipeline.from_pretrained(
model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16
)
pipe.to("cuda")
# Load all required inputs for replacement mode
image = load_image("path/to/new_character.jpg")
pose_video = load_video("path/to/pose_video.mp4") # Preprocessed skeletal keypoints
face_video = load_video("path/to/face_video.mp4") # Preprocessed facial features
background_video = load_video("path/to/background_video.mp4") # Original scene
mask_video = load_video("path/to/mask_video.mp4") # Black: preserve, White: generate
# Resize image to match video dimensions
def aspect_ratio_resize(image, pipe, max_area=720 * 1280):
aspect_ratio = image.height / image.width
mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
image = image.resize((width, height))
return image, height, width
image, height, width = aspect_ratio_resize(image, pipe)
prompt = "A person seamlessly integrated into the scene with consistent lighting and environment"
negative_prompt = "blurry, low quality, inconsistent lighting, floating, disconnected from scene"
# Replace character in background video
output = pipe(
image=image,
pose_video=pose_video,
face_video=face_video,
background_video=background_video,
mask_video=mask_video,
prompt=prompt,
negative_prompt=negative_prompt,
height=height,
width=width,
num_frames=81,
guidance_scale=5.0,
mode="replacement", # Replacement mode
).frames[0]
export_to_video(output, "character_replaced.mp4", fps=16)
```
</hfoption>
<hfoption id="Advanced options">
```python
import numpy as np
import torch
from diffusers import AutoencoderKLWan, WanAnimatePipeline
from diffusers.utils import export_to_video, load_image, load_video
from transformers import CLIPVisionModel
model_id = "Wan-AI/Wan2.2-Animate-14B-Diffusers"
image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float16)
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanAnimatePipeline.from_pretrained(
model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16
)
pipe.to("cuda")
image = load_image("path/to/character.jpg")
pose_video = load_video("path/to/pose_video.mp4")
face_video = load_video("path/to/face_video.mp4")
def aspect_ratio_resize(image, pipe, max_area=720 * 1280):
aspect_ratio = image.height / image.width
mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
image = image.resize((width, height))
return image, height, width
image, height, width = aspect_ratio_resize(image, pipe)
prompt = "A person dancing energetically in a studio"
negative_prompt = "blurry, low quality"
# Advanced: Use temporal guidance and custom callback
def callback_fn(pipe, step_index, timestep, callback_kwargs):
# You can modify latents or other tensors here
print(f"Step {step_index}, Timestep {timestep}")
return callback_kwargs
output = pipe(
image=image,
pose_video=pose_video,
face_video=face_video,
prompt=prompt,
negative_prompt=negative_prompt,
height=height,
width=width,
num_frames=81,
num_inference_steps=50,
guidance_scale=5.0,
num_frames_for_temporal_guidance=5, # Use 5 frames for temporal guidance (1 or 5 recommended)
callback_on_step_end=callback_fn,
callback_on_step_end_tensor_inputs=["latents"],
).frames[0]
export_to_video(output, "animated_advanced.mp4", fps=16)
```
</hfoption>
</hfoptions>
#### Key Parameters
- **mode**: Choose between `"animation"` (default) or `"replacement"`
- **num_frames_for_temporal_guidance**: Number of frames for temporal guidance (1 or 5 recommended). Using 5 provides better temporal consistency but requires more memory
- **guidance_scale**: Controls how closely the output follows the text prompt. Higher values (5-7) produce results more aligned with the prompt
- **num_frames**: Total number of frames to generate. Should be divisible by `vae_scale_factor_temporal` (default: 4)
## Notes
- Wan2.1 supports LoRAs with [`~loaders.WanLoraLoaderMixin.load_lora_weights`].
@@ -281,10 +496,10 @@ The general rule of thumb to keep in mind when preparing inputs for the VACE pip
# use "steamboat willie style" to trigger the LoRA
prompt = """
steamboat willie style, golden era animation, The camera rushes from far to near in a low-angle shot,
revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in
for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground.
Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic
steamboat willie style, golden era animation, The camera rushes from far to near in a low-angle shot,
revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in
for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground.
Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic
shadows and warm highlights. Medium composition, front view, low angle, with depth of field.
"""
@@ -359,6 +574,12 @@ The general rule of thumb to keep in mind when preparing inputs for the VACE pip
- all
- __call__
## WanAnimatePipeline
[[autodoc]] WanAnimatePipeline
- all
- __call__
## WanPipelineOutput
[[autodoc]] pipelines.wan.pipeline_output.WanPipelineOutput
[[autodoc]] pipelines.wan.pipeline_output.WanPipelineOutput