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# Wan

[Wan-2.1](https://huggingface.co/papers/2503.20314) by the Wan Team.

*This report presents Wan, a comprehensive and open suite of video foundation models designed to push the boundaries of video generation. Built upon the mainstream diffusion transformer paradigm, Wan achieves significant advancements in generative capabilities through a series of innovations, including our novel VAE, scalable pre-training strategies, large-scale data curation, and automated evaluation metrics. These contributions collectively enhance the model's performance and versatility. Specifically, Wan is characterized by four key features: Leading Performance: The 14B model of Wan, trained on a vast dataset comprising billions of images and videos, demonstrates the scaling laws of video generation with respect to both data and model size. It consistently outperforms the existing open-source models as well as state-of-the-art commercial solutions across multiple internal and external benchmarks, demonstrating a clear and significant performance superiority. Comprehensiveness: Wan offers two capable models, i.e., 1.3B and 14B parameters, for efficiency and effectiveness respectively. It also covers multiple downstream applications, including image-to-video, instruction-guided video editing, and personal video generation, encompassing up to eight tasks. Consumer-Grade Efficiency: The 1.3B model demonstrates exceptional resource efficiency, requiring only 8.19 GB VRAM, making it compatible with a wide range of consumer-grade GPUs. Openness: We open-source the entire series of Wan, including source code and all models, with the goal of fostering the growth of the video generation community. This openness seeks to significantly expand the creative possibilities of video production in the industry and provide academia with high-quality video foundation models. All the code and models are available at [this https URL](https://github.com/Wan-Video/Wan2.1).*

You can find all the original Wan2.1 checkpoints under the [Wan-AI](https://huggingface.co/Wan-AI) organization.

The following Wan models are supported in Diffusers:

- [Wan 2.1 T2V 1.3B](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers)
- [Wan 2.1 T2V 14B](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B-Diffusers)
- [Wan 2.1 I2V 14B - 480P](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P-Diffusers)
- [Wan 2.1 I2V 14B - 720P](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P-Diffusers)
- [Wan 2.1 FLF2V 14B - 720P](https://huggingface.co/Wan-AI/Wan2.1-FLF2V-14B-720P-diffusers)
- [Wan 2.1 VACE 1.3B](https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B-diffusers)
- [Wan 2.1 VACE 14B](https://huggingface.co/Wan-AI/Wan2.1-VACE-14B-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.

### Text-to-Video Generation

The example below demonstrates how to generate a video from text optimized for memory or inference speed.

Refer to the [Reduce memory usage](../../optimization/memory) guide for more details about the various memory saving techniques.

The Wan2.1 text-to-video model below requires ~13GB of VRAM.

```py
# pip install ftfy
import torch
import numpy as np
from diffusers import AutoModel, WanPipeline
from diffusers.quantizers import PipelineQuantizationConfig
from diffusers.hooks.group_offloading import apply_group_offloading
from diffusers.utils import export_to_video, load_image
from transformers import UMT5EncoderModel

text_encoder = UMT5EncoderModel.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="text_encoder", torch_dtype=torch.bfloat16)
vae = AutoModel.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="vae", torch_dtype=torch.float32)
transformer = AutoModel.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)

# group-offloading
onload_device = torch.device("cuda")
offload_device = torch.device("cpu")
apply_group_offloading(text_encoder,
    onload_device=onload_device,
    offload_device=offload_device,
    offload_type="block_level",
    num_blocks_per_group=4
)
transformer.enable_group_offload(
    onload_device=onload_device,
    offload_device=offload_device,
    offload_type="leaf_level",
    use_stream=True
)

pipeline = WanPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-14B-Diffusers",
    vae=vae,
    transformer=transformer,
    text_encoder=text_encoder,
    torch_dtype=torch.bfloat16
)
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
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,
misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
"""

output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_frames=81,
    guidance_scale=5.0,
).frames[0]
export_to_video(output, "output.mp4", fps=16)
```

[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster. [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.

```py
# pip install ftfy
import torch
import numpy as np
from diffusers import AutoModel, WanPipeline
from diffusers.hooks.group_offloading import apply_group_offloading
from diffusers.utils import export_to_video, load_image
from transformers import UMT5EncoderModel

text_encoder = UMT5EncoderModel.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="text_encoder", torch_dtype=torch.bfloat16)
vae = AutoModel.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="vae", torch_dtype=torch.float32)
transformer = AutoModel.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)

pipeline = WanPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-14B-Diffusers",
    vae=vae,
    transformer=transformer,
    text_encoder=text_encoder,
    torch_dtype=torch.bfloat16
)
pipeline.to("cuda")

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

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
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,
misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
"""

output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_frames=81,
    guidance_scale=5.0,
).frames[0]
export_to_video(output, "output.mp4", fps=16)
```

### First-Last-Frame-to-Video Generation

The example below demonstrates how to use the image-to-video pipeline to generate a video using a text description, a starting frame, and an ending frame.

```python
import numpy as np
import torch
import torchvision.transforms.functional as TF
from diffusers import AutoencoderKLWan, WanImageToVideoPipeline
from diffusers.utils import export_to_video, load_image
from transformers import CLIPVisionModel

model_id = "Wan-AI/Wan2.1-FLF2V-14B-720P-diffusers"
image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32)
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanImageToVideoPipeline.from_pretrained(
    model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16
)
pipe.to("cuda")

first_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_first_frame.png")
last_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_last_frame.png")

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

def center_crop_resize(image, height, width):
    # Calculate resize ratio to match first frame dimensions
    resize_ratio = max(width / image.width, height / image.height)

    # Resize the image
    width = round(image.width * resize_ratio)
    height = round(image.height * resize_ratio)
    size = [width, height]
    image = TF.center_crop(image, size)

    return image, height, width

first_frame, height, width = aspect_ratio_resize(first_frame, pipe)
if last_frame.size != first_frame.size:
    last_frame, _, _ = center_crop_resize(last_frame, height, width)

prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."

output = pipe(
    image=first_frame, last_image=last_frame, prompt=prompt, height=height, width=width, guidance_scale=5.5
).frames[0]
export_to_video(output, "output.mp4", fps=16)
```

### Any-to-Video Controllable Generation

Wan VACE supports various generation techniques which achieve controllable video generation. Some of the capabilities include:
- Control to Video (Depth, Pose, Sketch, Flow, Grayscale, Scribble, Layout, Boundary Box, etc.). Recommended library for preprocessing videos to obtain control videos: [huggingface/controlnet_aux]()
- Image/Video to Video (first frame, last frame, starting clip, ending clip, random clips)
- Inpainting and Outpainting
- Subject to Video (faces, object, characters, etc.)
- Composition to Video (reference anything, animate anything, swap anything, expand anything, move anything, etc.)

The code snippets available in [this](https://github.com/huggingface/diffusers/pull/11582) pull request demonstrate some examples of how videos can be generated with controllability signals.

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.

### 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:

```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)
```

```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)
```

```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)
```

#### 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 [load_lora_weights()](/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.WanLoraLoaderMixin.load_lora_weights).

  
  Show example code

  ```py
  # pip install ftfy
  import torch
  from diffusers import AutoModel, WanPipeline
  from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
  from diffusers.utils import export_to_video

  vae = AutoModel.from_pretrained(
      "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="vae", torch_dtype=torch.float32
  )
  pipeline = WanPipeline.from_pretrained(
      "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", vae=vae, torch_dtype=torch.bfloat16
  )
  pipeline.scheduler = UniPCMultistepScheduler.from_config(
      pipeline.scheduler.config, flow_shift=5.0
  )
  pipeline.to("cuda")

  pipeline.load_lora_weights("benjamin-paine/steamboat-willie-1.3b", adapter_name="steamboat-willie")
  pipeline.set_adapters("steamboat-willie")

  pipeline.enable_model_cpu_offload()

  # 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
  shadows and warm highlights. Medium composition, front view, low angle, with depth of field.
  """

  output = pipeline(
      prompt=prompt,
      num_frames=81,
      guidance_scale=5.0,
  ).frames[0]
  export_to_video(output, "output.mp4", fps=16)
  ```

  

- [WanTransformer3DModel](/docs/diffusers/main/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel) and [AutoencoderKLWan](/docs/diffusers/main/en/api/models/autoencoder_kl_wan#diffusers.AutoencoderKLWan) supports loading from single files with [from_single_file()](/docs/diffusers/main/en/api/loaders/single_file#diffusers.loaders.FromSingleFileMixin.from_single_file).

  
  Show example code

  ```py
  # pip install ftfy
  import torch
  from diffusers import WanPipeline, WanTransformer3DModel, AutoencoderKLWan

  vae = AutoencoderKLWan.from_single_file(
      "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors"
  )
  transformer = WanTransformer3DModel.from_single_file(
      "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/diffusion_models/wan2.1_t2v_1.3B_bf16.safetensors",
      torch_dtype=torch.bfloat16
  )
  pipeline = WanPipeline.from_pretrained(
      "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
      vae=vae,
      transformer=transformer,
      torch_dtype=torch.bfloat16
  )
  ```

  

- Set the [AutoencoderKLWan](/docs/diffusers/main/en/api/models/autoencoder_kl_wan#diffusers.AutoencoderKLWan) dtype to `torch.float32` for better decoding quality.

- The number of frames per second (fps) or `k` should be calculated by `4 * k + 1`.

- Try lower `shift` values (`2.0` to `5.0`) for lower resolution videos and higher `shift` values (`7.0` to `12.0`) for higher resolution images.

- Wan 2.1 and 2.2 support using [LightX2V LoRAs](https://huggingface.co/Kijai/WanVideo_comfy/tree/main/Lightx2v) to speed up inference. Using them on Wan 2.2 is slightly more involed. Refer to [this code snippet](https://github.com/huggingface/diffusers/pull/12040#issuecomment-3144185272) to learn more.

- Wan 2.2 has two denoisers. By default, LoRAs are only loaded into the first denoiser. One can set `load_into_transformer_2=True` to load LoRAs into the second denoiser. Refer to [this](https://github.com/huggingface/diffusers/pull/12074#issue-3292620048) and [this](https://github.com/huggingface/diffusers/pull/12074#issuecomment-3155896144) examples to learn more.

## WanPipeline[[diffusers.WanPipeline]]

#### diffusers.WanPipeline[[diffusers.WanPipeline]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/wan/pipeline_wan.py#L95)

Pipeline for text-to-video generation using Wan.

This model inherits from [DiffusionPipeline](/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline). Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).

__call__diffusers.WanPipeline.__call__https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/wan/pipeline_wan.py#L380[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "height", "val": ": int = 480"}, {"name": "width", "val": ": int = 832"}, {"name": "num_frames", "val": ": int = 81"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "guidance_scale", "val": ": float = 5.0"}, {"name": "guidance_scale_2", "val": ": typing.Optional[float] = None"}, {"name": "num_videos_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'np'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 512"}]- **prompt** (`str` or `List[str]`, *optional*) --
  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`. Higher guidance scale encourages to generate images that are closely linked to
  the text `prompt`, usually at the expense of lower image quality.
- **guidance_scale_2** (`float`, *optional*, defaults to `None`) --
  Guidance scale for the low-noise stage transformer (`transformer_2`). If `None` and the pipeline's
  `boundary_ratio` is not None, uses the same value as `guidance_scale`. Only used when `transformer_2`
  and the pipeline's `boundary_ratio` are not None.
- **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*) --
  A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
  generation deterministic.
- **latents** (`torch.Tensor`, *optional*) --
  Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
  generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
  tensor is generated by sampling using the supplied random `generator`.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
  provided, text embeddings are generated from the `prompt` input argument.
- **output_type** (`str`, *optional*, defaults to `"np"`) --
  The output format of the generated image. Choose between `PIL.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `WanPipelineOutput` instead of a plain tuple.
- **attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
  DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
  list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
- **callback_on_step_end_tensor_inputs** (`List`, *optional*) --
  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`, 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.0`~WanPipelineOutput` or `tuple`If `return_dict` is `True`, `WanPipelineOutput` is returned, otherwise a `tuple` is returned where
the first element is a list with the generated images and the second element is a list of `bool`s
indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.

The call function to the pipeline for generation.

Examples:
```python
>>> import torch
>>> from diffusers.utils import export_to_video
>>> from diffusers import AutoencoderKLWan, WanPipeline
>>> from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler

>>> # Available models: Wan-AI/Wan2.1-T2V-14B-Diffusers, Wan-AI/Wan2.1-T2V-1.3B-Diffusers
>>> model_id = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
>>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
>>> pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
>>> flow_shift = 5.0  # 5.0 for 720P, 3.0 for 480P
>>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
>>> pipe.to("cuda")

>>> prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window."
>>> 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, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"

>>> output = pipe(
...     prompt=prompt,
...     negative_prompt=negative_prompt,
...     height=720,
...     width=1280,
...     num_frames=81,
...     guidance_scale=5.0,
... ).frames[0]
>>> export_to_video(output, "output.mp4", fps=16)
```

**Parameters:**

tokenizer (`T5Tokenizer`) : Tokenizer from [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Tokenizer), specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.

text_encoder (`T5EncoderModel`) : [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.

transformer ([WanTransformer3DModel](/docs/diffusers/main/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel)) : Conditional Transformer to denoise the input latents.

scheduler ([UniPCMultistepScheduler](/docs/diffusers/main/en/api/schedulers/unipc#diffusers.UniPCMultistepScheduler)) : A scheduler to be used in combination with `transformer` to denoise the encoded image latents.

vae ([AutoencoderKLWan](/docs/diffusers/main/en/api/models/autoencoder_kl_wan#diffusers.AutoencoderKLWan)) : Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.

transformer_2 ([WanTransformer3DModel](/docs/diffusers/main/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel), *optional*) : Conditional Transformer to denoise the input latents during the low-noise stage. If provided, enables two-stage denoising where `transformer` handles high-noise stages and `transformer_2` handles low-noise stages. If not provided, only `transformer` is used.

boundary_ratio (`float`, *optional*, defaults to `None`) : Ratio of total timesteps to use as the boundary for switching between transformers in two-stage denoising. The actual boundary timestep is calculated as `boundary_ratio * num_train_timesteps`. When provided, `transformer` handles timesteps >= boundary_timestep and `transformer_2` handles timesteps  1`. Higher guidance scale encourages to generate images that are closely linked to
  the text `prompt`, usually at the expense of lower image quality.
- **guidance_scale_2** (`float`, *optional*, defaults to `None`) --
  Guidance scale for the low-noise stage transformer (`transformer_2`). If `None` and the pipeline's
  `boundary_ratio` is not None, uses the same value as `guidance_scale`. Only used when `transformer_2`
  and the pipeline's `boundary_ratio` are not None.
- **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*) --
  A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
  generation deterministic.
- **latents** (`torch.Tensor`, *optional*) --
  Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
  generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
  tensor is generated by sampling using the supplied random `generator`.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
  provided, text embeddings are generated from the `prompt` input argument.
- **negative_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
  provided, text embeddings are generated from the `negative_prompt` input argument.
- **image_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated image embeddings. Can be used to easily tweak image inputs (weighting). If not provided,
  image embeddings are generated from the `image` input argument.
- **output_type** (`str`, *optional*, defaults to `"np"`) --
  The output format of the generated image. Choose between `PIL.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `WanPipelineOutput` instead of a plain tuple.
- **attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
  DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
  list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
- **callback_on_step_end_tensor_inputs** (`List`, *optional*) --
  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`, 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.0`~WanPipelineOutput` or `tuple`If `return_dict` is `True`, `WanPipelineOutput` is returned, otherwise a `tuple` is returned where
the first element is a list with the generated images and the second element is a list of `bool`s
indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.

The call function to the pipeline for generation.

Examples:
```python
>>> import torch
>>> import numpy as np
>>> from diffusers import AutoencoderKLWan, WanImageToVideoPipeline
>>> from diffusers.utils import export_to_video, load_image
>>> from transformers import CLIPVisionModel

>>> # Available models: Wan-AI/Wan2.1-I2V-14B-480P-Diffusers, Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
>>> model_id = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
>>> image_encoder = CLIPVisionModel.from_pretrained(
...     model_id, subfolder="image_encoder", torch_dtype=torch.float32
... )
>>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
>>> pipe = WanImageToVideoPipeline.from_pretrained(
...     model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16
... )
>>> pipe.to("cuda")

>>> image = load_image(
...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
... )
>>> max_area = 480 * 832
>>> 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))
>>> prompt = (
...     "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in "
...     "the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot."
... )
>>> 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, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"

>>> output = pipe(
...     image=image,
...     prompt=prompt,
...     negative_prompt=negative_prompt,
...     height=height,
...     width=width,
...     num_frames=81,
...     guidance_scale=5.0,
... ).frames[0]
>>> export_to_video(output, "output.mp4", fps=16)
```

**Parameters:**

tokenizer (`T5Tokenizer`) : Tokenizer from [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Tokenizer), specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.

text_encoder (`T5EncoderModel`) : [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.

image_encoder (`CLIPVisionModel`) : [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModel), specifically the [clip-vit-huge-patch14](https://github.com/mlfoundations/open_clip/blob/main/docs/PRETRAINED.md#vit-h14-xlm-roberta-large) variant.

transformer ([WanTransformer3DModel](/docs/diffusers/main/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel)) : Conditional Transformer to denoise the input latents.

scheduler ([UniPCMultistepScheduler](/docs/diffusers/main/en/api/schedulers/unipc#diffusers.UniPCMultistepScheduler)) : A scheduler to be used in combination with `transformer` to denoise the encoded image latents.

vae ([AutoencoderKLWan](/docs/diffusers/main/en/api/models/autoencoder_kl_wan#diffusers.AutoencoderKLWan)) : Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.

transformer_2 ([WanTransformer3DModel](/docs/diffusers/main/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel), *optional*) : Conditional Transformer to denoise the input latents during the low-noise stage. In two-stage denoising, `transformer` handles high-noise stages and `transformer_2` handles low-noise stages. If not provided, only `transformer` is used.

boundary_ratio (`float`, *optional*, defaults to `None`) : Ratio of total timesteps to use as the boundary for switching between transformers in two-stage denoising. The actual boundary timestep is calculated as `boundary_ratio * num_train_timesteps`. When provided, `transformer` handles timesteps >= boundary_timestep and `transformer_2` handles timesteps  1`. Higher guidance scale encourages to generate images that are closely linked to
  the text `prompt`, usually at the expense of lower image quality.
- **guidance_scale_2** (`float`, *optional*, defaults to `None`) --
  Guidance scale for the low-noise stage transformer (`transformer_2`). If `None` and the pipeline's
  `boundary_ratio` is not None, uses the same value as `guidance_scale`. Only used when `transformer_2`
  and the pipeline's `boundary_ratio` are not None.
- **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*) --
  A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
  generation deterministic.
- **latents** (`torch.Tensor`, *optional*) --
  Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
  generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
  tensor is generated by sampling using the supplied random `generator`.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
  provided, text embeddings are generated from the `prompt` input argument.
- **output_type** (`str`, *optional*, defaults to `"np"`) --
  The output format of the generated image. Choose between `PIL.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `WanPipelineOutput` instead of a plain tuple.
- **attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
  DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
  list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
- **callback_on_step_end_tensor_inputs** (`List`, *optional*) --
  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`, 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.0`~WanPipelineOutput` or `tuple`If `return_dict` is `True`, `WanPipelineOutput` is returned, otherwise a `tuple` is returned where
the first element is a list with the generated images and the second element is a list of `bool`s
indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.

The call function to the pipeline for generation.

Examples:
```python
>>> import torch
>>> import PIL.Image
>>> from diffusers import AutoencoderKLWan, WanVACEPipeline
>>> from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
>>> from diffusers.utils import export_to_video, load_image
def prepare_video_and_mask(first_img: PIL.Image.Image, last_img: PIL.Image.Image, height: int, width: int, num_frames: int):
    first_img = first_img.resize((width, height))
    last_img = last_img.resize((width, height))
    frames = []
    frames.append(first_img)
    # Ideally, this should be 127.5 to match original code, but they perform computation on numpy arrays
    # whereas we are passing PIL images. If you choose to pass numpy arrays, you can set it to 127.5 to
    # match the original code.
    frames.extend([PIL.Image.new("RGB", (width, height), (128, 128, 128))] * (num_frames - 2))
    frames.append(last_img)
    mask_black = PIL.Image.new("L", (width, height), 0)
    mask_white = PIL.Image.new("L", (width, height), 255)
    mask = [mask_black, *[mask_white] * (num_frames - 2), mask_black]
    return frames, mask

>>> # Available checkpoints: Wan-AI/Wan2.1-VACE-1.3B-diffusers, Wan-AI/Wan2.1-VACE-14B-diffusers
>>> model_id = "Wan-AI/Wan2.1-VACE-1.3B-diffusers"
>>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
>>> pipe = WanVACEPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
>>> flow_shift = 3.0  # 5.0 for 720P, 3.0 for 480P
>>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
>>> pipe.to("cuda")

>>> prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
>>> 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, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
>>> first_frame = load_image(
...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_first_frame.png"
... )
>>> last_frame = load_image(
...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_last_frame.png>>> "
... )

>>> height = 512
>>> width = 512
>>> num_frames = 81
>>> video, mask = prepare_video_and_mask(first_frame, last_frame, height, width, num_frames)

>>> output = pipe(
...     video=video,
...     mask=mask,
...     prompt=prompt,
...     negative_prompt=negative_prompt,
...     height=height,
...     width=width,
...     num_frames=num_frames,
...     num_inference_steps=30,
...     guidance_scale=5.0,
...     generator=torch.Generator().manual_seed(42),
... ).frames[0]
>>> export_to_video(output, "output.mp4", fps=16)
```

**Parameters:**

tokenizer (`T5Tokenizer`) : Tokenizer from [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Tokenizer), specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.

text_encoder (`T5EncoderModel`) : [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.

vae ([AutoencoderKLWan](/docs/diffusers/main/en/api/models/autoencoder_kl_wan#diffusers.AutoencoderKLWan)) : Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.

scheduler ([UniPCMultistepScheduler](/docs/diffusers/main/en/api/schedulers/unipc#diffusers.UniPCMultistepScheduler)) : A scheduler to be used in combination with `transformer` to denoise the encoded image latents.

transformer (`WanVACETransformer3DModel`, *optional*) : Conditional Transformer to denoise the input latents during the high-noise stage. In two-stage denoising, `transformer` handles high-noise stages and `transformer_2` handles low-noise stages. At least one of `transformer` or `transformer_2` must be provided.

transformer_2 (`WanVACETransformer3DModel`, *optional*) : Conditional Transformer to denoise the input latents during the low-noise stage. In two-stage denoising, `transformer` handles high-noise stages and `transformer_2` handles low-noise stages. At least one of `transformer` or `transformer_2` must be provided.

boundary_ratio (`float`, *optional*, defaults to `None`) : Ratio of total timesteps to use as the boundary for switching between transformers in two-stage denoising. The actual boundary timestep is calculated as `boundary_ratio * num_train_timesteps`. When provided, `transformer` handles timesteps >= boundary_timestep and `transformer_2` handles timesteps  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*) --
  A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
  generation deterministic.
- **latents** (`torch.Tensor`, *optional*) --
  Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
  generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
  tensor is generated by sampling using the supplied random `generator`.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
  provided, text embeddings are generated from the `prompt` input argument.
- **output_type** (`str`, *optional*, defaults to `"np"`) --
  The output format of the generated image. Choose between `PIL.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `WanPipelineOutput` instead of a plain tuple.
- **attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
  DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
  list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
- **callback_on_step_end_tensor_inputs** (`List`, *optional*) --
  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`, 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.0`~WanPipelineOutput` or `tuple`If `return_dict` is `True`, `WanPipelineOutput` is returned, otherwise a `tuple` is returned where
the first element is a list with the generated images and the second element is a list of `bool`s
indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.

The call function to the pipeline for generation.

Examples:
```python
>>> import torch
>>> from diffusers.utils import export_to_video, load_video
>>> from diffusers import AutoencoderKLWan, WanVideoToVideoPipeline
>>> from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler

>>> # Available models: Wan-AI/Wan2.1-T2V-14B-Diffusers, Wan-AI/Wan2.1-T2V-1.3B-Diffusers
>>> model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
>>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
>>> pipe = WanVideoToVideoPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
>>> flow_shift = 3.0  # 5.0 for 720P, 3.0 for 480P
>>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
>>> pipe.to("cuda")

>>> prompt = "A robot standing on a mountain top. The sun is setting in the background"
>>> 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, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
>>> video = load_video(
...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hiker.mp4"
... )
>>> output = pipe(
...     video=video,
...     prompt=prompt,
...     negative_prompt=negative_prompt,
...     height=480,
...     width=720,
...     guidance_scale=5.0,
...     strength=0.7,
... ).frames[0]
>>> export_to_video(output, "output.mp4", fps=16)
```

**Parameters:**

tokenizer (`T5Tokenizer`) : Tokenizer from [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Tokenizer), specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.

text_encoder (`T5EncoderModel`) : [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.

transformer ([WanTransformer3DModel](/docs/diffusers/main/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel)) : Conditional Transformer to denoise the input latents.

scheduler ([UniPCMultistepScheduler](/docs/diffusers/main/en/api/schedulers/unipc#diffusers.UniPCMultistepScheduler)) : A scheduler to be used in combination with `transformer` to denoise the encoded image latents.

vae ([AutoencoderKLWan](/docs/diffusers/main/en/api/models/autoencoder_kl_wan#diffusers.AutoencoderKLWan)) : Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.

**Returns:**

``~WanPipelineOutput` or `tuple``

If `return_dict` is `True`, `WanPipelineOutput` is returned, otherwise a `tuple` is returned where
the first element is a list with the generated images and the second element is a list of `bool`s
indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.
#### encode_prompt[[diffusers.WanVideoToVideoPipeline.encode_prompt]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/wan/pipeline_wan_video2video.py#L264)

Encodes the prompt into text encoder hidden states.

**Parameters:**

prompt (`str` or `List[str]`, *optional*) : prompt to be encoded

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`).

do_classifier_free_guidance (`bool`, *optional*, defaults to `True`) : Whether to use classifier free guidance or not.

num_videos_per_prompt (`int`, *optional*, defaults to 1) : Number of videos that should be generated per prompt. torch device to place the resulting embeddings on

prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument.

negative_prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.

device : (`torch.device`, *optional*): torch device

dtype : (`torch.dtype`, *optional*): torch dtype

## WanAnimatePipeline[[diffusers.WanAnimatePipeline]]

#### diffusers.WanAnimatePipeline[[diffusers.WanAnimatePipeline]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/wan/pipeline_wan_animate.py#L150)

Pipeline for unified character animation and replacement using Wan-Animate.

WanAnimatePipeline takes a character image, pose video, and face video as input, and generates a video in two
modes:

1. **Animation mode**: The model generates a video of the character image that mimics the human motion in the input
   pose and face videos. The character is animated based on the provided motion controls, creating a new animated
   video of the character.

2. **Replacement mode**: The model replaces a character in a background video with the provided character image,
   using the pose and face videos for motion control. This mode requires additional `background_video` and
   `mask_video` inputs. The mask video should have black regions where the original content should be preserved and
   white regions where the new character should be generated.

This model inherits from [DiffusionPipeline](/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline). Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).

The pipeline also inherits the following loading methods:
- [load_lora_weights()](/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.WanLoraLoaderMixin.load_lora_weights) for loading LoRA weights

__call__diffusers.WanAnimatePipeline.__call__https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/wan/pipeline_wan_animate.py#L760[{"name": "image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]]"}, {"name": "pose_video", "val": ": typing.List[PIL.Image.Image]"}, {"name": "face_video", "val": ": typing.List[PIL.Image.Image]"}, {"name": "background_video", "val": ": typing.Optional[typing.List[PIL.Image.Image]] = None"}, {"name": "mask_video", "val": ": typing.Optional[typing.List[PIL.Image.Image]] = None"}, {"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "height", "val": ": int = 720"}, {"name": "width", "val": ": int = 1280"}, {"name": "segment_frame_length", "val": ": int = 77"}, {"name": "num_inference_steps", "val": ": int = 20"}, {"name": "mode", "val": ": str = 'animate'"}, {"name": "prev_segment_conditioning_frames", "val": ": int = 1"}, {"name": "motion_encode_batch_size", "val": ": typing.Optional[int] = None"}, {"name": "guidance_scale", "val": ": float = 1.0"}, {"name": "num_videos_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "image_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'np'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 512"}]- **image** (`PipelineImageInput`) --
  The input character image to condition the generation on. Must be an image, a list of images or a
  `torch.Tensor`.
- **pose_video** (`List[PIL.Image.Image]`) --
  The input pose video to condition the generation on. Must be a list of PIL images.
- **face_video** (`List[PIL.Image.Image]`) --
  The input face video to condition the generation on. Must be a list of PIL images.
- **background_video** (`List[PIL.Image.Image]`, *optional*) --
  When mode is `"replace"`, the input background video to condition the generation on. Must be a list of
  PIL images.
- **mask_video** (`List[PIL.Image.Image]`, *optional*) --
  When mode is `"replace"`, the input mask video to condition the generation on. Must be a list of PIL
  images.
- **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.
- **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`).
- **mode** (`str`, defaults to `"animation"`) --
  The mode of the generation. Choose between `"animate"` and `"replace"`.
- **prev_segment_conditioning_frames** (`int`, defaults to `1`) --
  The number of frames from the previous video segment to be used for temporal guidance. Recommended to
  be 1 or 5. In general, should be 4N + 1, where N is a non-negative integer.
- **motion_encode_batch_size** (`int`, *optional*) --
  The batch size for batched encoding of the face video via the motion encoder. This allows trading off
  inference speed for lower memory usage by setting a smaller batch size. Will default to
  `self.transformer.config.motion_encoder_batch_size` if not set.
- **height** (`int`, defaults to `720`) --
  The height of the generated video.
- **width** (`int`, defaults to `1280`) --
  The width of the generated video.
- **segment_frame_length** (`int`, defaults to `77`) --
  The number of frames in each generated video segment. The total frames of video generated will be equal
  to the number of frames in `pose_video`; we will generate the video in segments until we have hit this
  length. In general, should be 4N + 1, where N is a non-negative integer.
- **num_inference_steps** (`int`, defaults to `20`) --
  The number of denoising steps. More denoising steps usually lead to a higher quality image at the
  expense of slower inference.
- **guidance_scale** (`float`, defaults to `1.0`) --
  Guidance scale as defined in [Classifier-Free Diffusion
  Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
  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. By default, CFG is not used in Wan
  Animate inference.
- **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*) --
  A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
  generation deterministic.
- **latents** (`torch.Tensor`, *optional*) --
  Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
  generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
  tensor is generated by sampling using the supplied random `generator`.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
  provided, text embeddings are generated from the `prompt` input argument.
- **negative_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
  provided, text embeddings are generated from the `negative_prompt` input argument.
- **image_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated image embeddings. Can be used to easily tweak image inputs (weighting). If not provided,
  image embeddings are generated from the `image` input argument.
- **output_type** (`str`, *optional*, defaults to `"np"`) --
  The output format of the generated image. Choose between `PIL.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `WanPipelineOutput` instead of a plain tuple.
- **attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
  DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
  list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
- **callback_on_step_end_tensor_inputs** (`List`, *optional*) --
  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`, 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.0`~WanPipelineOutput` or `tuple`If `return_dict` is `True`, `WanPipelineOutput` is returned, otherwise a `tuple` is returned where
the first element is a list with the generated images and the second element is a list of `bool`s
indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.

The call function to the pipeline for generation.

Examples:
```python
>>> import torch
>>> import numpy as np
>>> from diffusers import WanAnimatePipeline
>>> from diffusers.utils import export_to_video, load_image, load_video

>>> model_id = "Wan-AI/Wan2.2-Animate-14B-Diffusers"
>>> pipe = WanAnimatePipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
>>> # Optionally upcast the Wan VAE to FP32
>>> pipe.vae.to(torch.float32)
>>> pipe.to("cuda")

>>> # Load the reference character image
>>> image = load_image(
...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
... )

>>> # Load pose and face videos (preprocessed from reference video)
>>> # Note: Videos should be preprocessed to extract pose keypoints and face features
>>> # Refer to the Wan-Animate preprocessing documentation for details
>>> pose_video = load_video("path/to/pose_video.mp4")
>>> face_video = load_video("path/to/face_video.mp4")

>>> # CFG is generally not used for Wan Animate
>>> prompt = (
...     "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in "
...     "the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot."
... )

>>> # Animation mode: Animate the character with the motion from pose/face videos
>>> output = pipe(
...     image=image,
...     pose_video=pose_video,
...     face_video=face_video,
...     prompt=prompt,
...     height=height,
...     width=width,
...     segment_frame_length=77,  # Frame length of each inference segment
...     guidance_scale=1.0,
...     num_inference_steps=20,
...     mode="animate",
... ).frames[0]
>>> export_to_video(output, "output_animation.mp4", fps=30)

>>> # Replacement mode: Replace a character in the background video
>>> # Requires additional background_video and mask_video inputs
>>> background_video = load_video("path/to/background_video.mp4")
>>> mask_video = load_video("path/to/mask_video.mp4")  # Black areas preserved, white areas generated
>>> output = pipe(
...     image=image,
...     pose_video=pose_video,
...     face_video=face_video,
...     background_video=background_video,
...     mask_video=mask_video,
...     prompt=prompt,
...     height=height,
...     width=width,
...     segment_frame_length=77,  # Frame length of each inference segment
...     guidance_scale=1.0,
...     num_inference_steps=20,
...     mode="replace",
... ).frames[0]
>>> export_to_video(output, "output_replacement.mp4", fps=30)
```

**Parameters:**

tokenizer (`T5Tokenizer`) : Tokenizer from [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Tokenizer), specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.

text_encoder (`T5EncoderModel`) : [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.

image_encoder (`CLIPVisionModel`) : [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModel), specifically the [clip-vit-huge-patch14](https://github.com/mlfoundations/open_clip/blob/main/docs/PRETRAINED.md#vit-h14-xlm-roberta-large) variant.

transformer ([WanAnimateTransformer3DModel](/docs/diffusers/main/en/api/models/wan_animate_transformer_3d#diffusers.WanAnimateTransformer3DModel)) : Conditional Transformer to denoise the input latents.

scheduler ([UniPCMultistepScheduler](/docs/diffusers/main/en/api/schedulers/unipc#diffusers.UniPCMultistepScheduler)) : A scheduler to be used in combination with `transformer` to denoise the encoded image latents.

vae ([AutoencoderKLWan](/docs/diffusers/main/en/api/models/autoencoder_kl_wan#diffusers.AutoencoderKLWan)) : Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.

image_processor (`CLIPImageProcessor`) : Image processor for preprocessing images before encoding.

**Returns:**

``~WanPipelineOutput` or `tuple``

If `return_dict` is `True`, `WanPipelineOutput` is returned, otherwise a `tuple` is returned where
the first element is a list with the generated images and the second element is a list of `bool`s
indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.
#### encode_prompt[[diffusers.WanAnimatePipeline.encode_prompt]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/wan/pipeline_wan_animate.py#L288)

Encodes the prompt into text encoder hidden states.

**Parameters:**

prompt (`str` or `List[str]`, *optional*) : prompt to be encoded

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`).

do_classifier_free_guidance (`bool`, *optional*, defaults to `True`) : Whether to use classifier free guidance or not.

num_videos_per_prompt (`int`, *optional*, defaults to 1) : Number of videos that should be generated per prompt. torch device to place the resulting embeddings on

prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument.

negative_prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.

device : (`torch.device`, *optional*): torch device

dtype : (`torch.dtype`, *optional*): torch dtype
#### pad_video_frames[[diffusers.WanAnimatePipeline.pad_video_frames]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/wan/pipeline_wan_animate.py#L715)

Pads an array-like video `frames` to `num_target_frames` using a "reflect"-like strategy. The frame dimension
is assumed to be the first dimension. In the 1D case, we can visualize this strategy as follows:

pad_video_frames([1, 2, 3, 4, 5], 10) -> [1, 2, 3, 4, 5, 4, 3, 2, 1, 2]

## WanPipelineOutput[[diffusers.pipelines.wan.pipeline_output.WanPipelineOutput]]

#### diffusers.pipelines.wan.pipeline_output.WanPipelineOutput[[diffusers.pipelines.wan.pipeline_output.WanPipelineOutput]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/wan/pipeline_output.py#L9)

Output class for Wan pipelines.

**Parameters:**

frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]) : List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape `(batch_size, num_frames, channels, height, width)`.

