#
Wan

Wan 2.1 by the Alibaba Wan Team.

##
Generating Videos with Wan 2.1

We will first need to install some addtional dependencies.

    pip install -u ftfy imageio-ffmpeg imageio

###
Text to Video Generation

The following example requires 11GB VRAM to run and uses the smaller `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` model. You can switch it out for the larger `Wan2.1-I2V-14B-720P-Diffusers` or `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` if you have at least 35GB VRAM available.

    from diffusers import WanPipeline
    from diffusers.utils import export_to_video

    # Available models: Wan-AI/Wan2.1-I2V-14B-720P-Diffusers or Wan-AI/Wan2.1-I2V-14B-480P-Diffusers
    model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"

    pipe = WanPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
    pipe.enable_model_cpu_offload()

    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"
    num_frames = 33

    frames = pipe(prompt=prompt, negative_prompt=negative_prompt, num_frames=num_frames).frames[0]
    export_to_video(frames, "wan-t2v.mp4", fps=16)

You can improve the quality of the generated video by running the decoding step in full precision.

    from diffusers import WanPipeline, AutoencoderKLWan
    from diffusers.utils import export_to_video

    model_id = "Wan-AI/Wan2.1-T2V-1.3B-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)

    # replace this with pipe.to("cuda") if you have sufficient VRAM
    pipe.enable_model_cpu_offload()

    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"
    num_frames = 33

    frames = pipe(prompt=prompt, num_frames=num_frames).frames[0]
    export_to_video(frames, "wan-t2v.mp4", fps=16)

###
Image to Video Generation

The Image to Video pipeline requires loading the `AutoencoderKLWan` and the `CLIPVisionModel` components in full precision. The following example will need at least 35GB of VRAM to run.

    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
    )

    # replace this with pipe.to("cuda") if you have sufficient VRAM
    pipe.enable_model_cpu_offload()

    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"

    num_frames = 33

    output = pipe(
        image=image,
        prompt=prompt,
        negative_prompt=negative_prompt,
        height=height,
        width=width,
        num_frames=num_frames,
        guidance_scale=5.0,
    ).frames[0]
    export_to_video(output, "wan-i2v.mp4", fps=16)

###
Video to Video Generation

    import torch
    from diffusers.utils import load_video, export_to_video
    from diffusers import AutoencoderKLWan, WanVideoToVideoPipeline, 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
    )
    # change to pipe.to("cuda") if you have sufficient VRAM
    pipe.enable_model_cpu_offload()

    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=512,
        guidance_scale=7.0,
        strength=0.7,
    ).frames[0]

    export_to_video(output, "wan-v2v.mp4", fps=16)

##
Memory Optimizations for Wan 2.1

Base inference with the large 14B Wan 2.1 models can take up to 35GB of VRAM when generating videos at 720p resolution. We’ll outline a few memory optimizations we can apply to reduce the VRAM required to run the model.

We’ll use `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers` model in these examples to demonstrate the memory savings, but the techniques are applicable to all model checkpoints.

###
Group Offloading the Transformer and UMT5 Text Encoder

Find more information about group offloading here

####
Block Level Group Offloading

We can reduce our VRAM requirements by applying group offloading to the larger model components of the pipeline; the `WanTransformer3DModel` and `UMT5EncoderModel`. Group offloading will break up the individual modules of a model and offload/onload them onto your GPU as needed during inference. In this example, we’ll apply `block_level` offloading, which will group the modules in a model into blocks of size `num_blocks_per_group` and offload/onload them to GPU. Moving to between CPU and GPU does add latency to the inference process. You can trade off between latency and memory savings by increasing or decreasing the `num_blocks_per_group`.

The following example will now only require 14GB of VRAM to run, but will take approximately 30 minutes to generate a video.

    import torch
    import numpy as np
    from diffusers import AutoencoderKLWan, WanTransformer3DModel, WanImageToVideoPipeline
    from diffusers.hooks.group_offloading import apply_group_offloading
    from diffusers.utils import export_to_video, load_image
    from transformers import UMT5EncoderModel, 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-720P-Diffusers"
    image_encoder = CLIPVisionModel.from_pretrained(
        model_id, subfolder="image_encoder", torch_dtype=torch.float32
    )

    text_encoder = UMT5EncoderModel.from_pretrained(model_id, subfolder="text_encoder", torch_dtype=torch.bfloat16)
    vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
    transformer = WanTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16)

    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="block_level",
        num_blocks_per_group=4,
    )
    pipe = WanImageToVideoPipeline.from_pretrained(
        model_id,
        vae=vae,
        transformer=transformer,
        text_encoder=text_encoder,
        image_encoder=image_encoder,
        torch_dtype=torch.bfloat16
    )
    # Since we've offloaded the larger models alrady, we can move the rest of the model components to GPU
    pipe.to("cuda")

    image = load_image(
        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
    )

    max_area = 720 * 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"

    num_frames = 33

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

    export_to_video(output, "wan-i2v.mp4", fps=16)

####
Block Level Group Offloading with CUDA Streams

We can speed up group offloading inference, by enabling the use of CUDA streams. However, using CUDA streams requires moving the model parameters into pinned memory. This allocation is handled by Pytorch under the hood, and can result in a significant spike in CPU RAM usage. Please consider this option if your CPU RAM is atleast 2X the size of the model you are group offloading.

In the following example we will use CUDA streams when group offloading the `WanTransformer3DModel`. When testing on an A100, this example will require 14GB of VRAM, 52GB of CPU RAM, but will generate a video in approximately 9 minutes.

    import torch
    import numpy as np
    from diffusers import AutoencoderKLWan, WanTransformer3DModel, WanImageToVideoPipeline
    from diffusers.hooks.group_offloading import apply_group_offloading
    from diffusers.utils import export_to_video, load_image
    from transformers import UMT5EncoderModel, 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-720P-Diffusers"
    image_encoder = CLIPVisionModel.from_pretrained(
        model_id, subfolder="image_encoder", torch_dtype=torch.float32
    )

    text_encoder = UMT5EncoderModel.from_pretrained(model_id, subfolder="text_encoder", torch_dtype=torch.bfloat16)
    vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
    transformer = WanTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16)

    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
    )
    pipe = WanImageToVideoPipeline.from_pretrained(
        model_id,
        vae=vae,
        transformer=transformer,
        text_encoder=text_encoder,
        image_encoder=image_encoder,
        torch_dtype=torch.bfloat16
    )
    # Since we've offloaded the larger models alrady, we can move the rest of the model components to GPU
    pipe.to("cuda")

    image = load_image(
        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
    )

    max_area = 720 * 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"

    num_frames = 33

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

    export_to_video(output, "wan-i2v.mp4", fps=16)

###
Applying Layerwise Casting to the Transformer

Find more information about layerwise casting here

In this example, we will model offloading with layerwise casting. Layerwise casting will downcast each layer’s weights to `torch.float8_e4m3fn`, temporarily upcast to `torch.bfloat16` during the forward pass of the layer, then revert to `torch.float8_e4m3fn` afterward. This approach reduces memory requirements by approximately 50% while introducing a minor quality reduction in the generated video due to the precision trade-off.

This example will require 20GB of VRAM.

    import torch
    import numpy as np
    from diffusers import AutoencoderKLWan, WanTransformer3DModel, WanImageToVideoPipeline
    from diffusers.hooks.group_offloading import apply_group_offloading
    from diffusers.utils import export_to_video, load_image
    from transformers import UMT5EncoderModel, CLIPVisionModel

    model_id = "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers"
    image_encoder = CLIPVisionModel.from_pretrained(
        model_id, subfolder="image_encoder", torch_dtype=torch.float32
    )
    text_encoder = UMT5EncoderModel.from_pretrained(model_id, subfolder="text_encoder", torch_dtype=torch.bfloat16)
    vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)

    transformer = WanTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16)
    transformer.enable_layerwise_casting(storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16)

    pipe = WanImageToVideoPipeline.from_pretrained(
        model_id,
        vae=vae,
        transformer=transformer,
        text_encoder=text_encoder,
        image_encoder=image_encoder,
        torch_dtype=torch.bfloat16
    )
    pipe.enable_model_cpu_offload()
    image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg")

    max_area = 720 * 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"
    num_frames = 33

    output = pipe(
        image=image,
        prompt=prompt,
        negative_prompt=negative_prompt,
        height=height,
        width=width,
        num_frames=num_frames,
        num_inference_steps=50,
        guidance_scale=5.0,
    ).frames[0]
    export_to_video(output, "wan-i2v.mp4", fps=16)

##
Using a Custom Scheduler

Wan can be used with many different schedulers, each with their own benefits regarding speed and generation quality. By default, Wan uses the `UniPCMultistepScheduler(prediction_type="flow_prediction", use_flow_sigmas=True, flow_shift=3.0)` scheduler. You can use a different scheduler as follows:

    from diffusers import FlowMatchEulerDiscreteScheduler, UniPCMultistepScheduler, WanPipeline

    scheduler_a = FlowMatchEulerDiscreteScheduler(shift=5.0)
    scheduler_b = UniPCMultistepScheduler(prediction_type="flow_prediction", use_flow_sigmas=True, flow_shift=4.0)

    pipe = WanPipeline.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", scheduler=<CUSTOM_SCHEDULER_HERE>)

    # or,
    pipe.scheduler = <CUSTOM_SCHEDULER_HERE>

##
Using Single File Loading with Wan 2.1

The `WanTransformer3DModel` and `AutoencoderKLWan` models support loading checkpoints in their original format via the `from_single_file` loading method.

    import torch
    from diffusers import WanPipeline, WanTransformer3DModel

    ckpt_path = "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/diffusion_models/wan2.1_t2v_1.3B_bf16.safetensors"
    transformer = WanTransformer3DModel.from_single_file(ckpt_path, torch_dtype=torch.bfloat16)

    pipe = WanPipeline.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", transformer=transformer)

##
Recommendations for Inference

- Keep `AutencoderKLWan` in `torch.float32` for better decoding quality.
- `num_frames` should satisfy the following constraint: `(num_frames - 1) % 4 == 0`
- For smaller resolution videos, try lower values of `shift` (between `2.0` to `5.0`) in the Scheduler. For larger resolution videos, try higher values (between `7.0` and `12.0`). The default value is `3.0` for Wan.

##
WanPipeline

###
class diffusers.WanPipeline

< source >

( tokenizer: AutoTokenizer text_encoder: UMT5EncoderModel transformer: WanTransformer3DModel vae: AutoencoderKLWan scheduler: FlowMatchEulerDiscreteScheduler )

Parameters

- tokenizer (`T5Tokenizer`) — Tokenizer from T5, specifically the google/umt5-xxl variant.
- text_encoder (`T5EncoderModel`) — T5, specifically the google/umt5-xxl variant.
- transformer (WanTransformer3DModel) — Conditional Transformer to denoise the input latents.
- scheduler (UniPCMultistepScheduler) — A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
- vae (AutoencoderKLWan) — Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.

Pipeline for text-to-video generation using Wan.

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None negative_prompt: typing.Union[str, typing.List[str]] = None height: int = 480 width: int = 832 num_frames: int = 81 num_inference_steps: int = 50 guidance_scale: float = 5.0 num_videos_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.Tensor] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None output_type: typing.Optional[str] = 'np' return_dict: bool = True attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None callback_on_step_end: typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] max_sequence_length: int = 512 ) →
`~WanPipelineOutput` or `tuple`

Parameters

- 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.
- height (`int`, defaults to `480`) — The height in pixels of the generated image.
- width (`int`, defaults to `832`) — The width in pixels of the generated image.
- num_frames (`int`, defaults to `81`) — The number of frames in the generated video.
- num_inference_steps (`int`, defaults to `50`) — 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 `5.0`) — Guidance scale as defined in Classifier-Free Diffusion Guidance. `guidance_scale` is defined as `w` of equation 2. of Imagen Paper. 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.
- 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` 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 `"pil"`) — 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.
- 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.
- autocast_dtype (`torch.dtype`, optional, defaults to `torch.bfloat16`) — The dtype to use for the torch.amp.autocast.

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.

The call function to the pipeline for generation.

Examples:

    >>> 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)

####
encode_prompt

< source >

( prompt: typing.Union[str, typing.List[str]] negative_prompt: typing.Union[str, typing.List[str], NoneType] = None do_classifier_free_guidance: bool = True num_videos_per_prompt: int = 1 prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None max_sequence_length: int = 226 device: typing.Optional[torch.device] = None dtype: typing.Optional[torch.dtype] = None )

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

Encodes the prompt into text encoder hidden states.

##
WanImageToVideoPipeline

###
class diffusers.WanImageToVideoPipeline

< source >

( tokenizer: AutoTokenizer text_encoder: UMT5EncoderModel image_encoder: CLIPVisionModel image_processor: CLIPImageProcessor transformer: WanTransformer3DModel vae: AutoencoderKLWan scheduler: FlowMatchEulerDiscreteScheduler )

Parameters

- tokenizer (`T5Tokenizer`) — Tokenizer from T5, specifically the google/umt5-xxl variant.
- text_encoder (`T5EncoderModel`) — T5, specifically the google/umt5-xxl variant.
- image_encoder (`CLIPVisionModel`) — CLIP, specifically the clip-vit-huge-patch14 variant.
- transformer (WanTransformer3DModel) — Conditional Transformer to denoise the input latents.
- scheduler (UniPCMultistepScheduler) — A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
- vae (AutoencoderKLWan) — Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.

Pipeline for image-to-video generation using Wan.

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).

####
__call__

< source >

( image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] prompt: typing.Union[str, typing.List[str]] = None negative_prompt: typing.Union[str, typing.List[str]] = None height: int = 480 width: int = 832 num_frames: int = 81 num_inference_steps: int = 50 guidance_scale: float = 5.0 num_videos_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.Tensor] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None output_type: typing.Optional[str] = 'np' return_dict: bool = True attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None callback_on_step_end: typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] max_sequence_length: int = 512 ) →
`~WanPipelineOutput` or `tuple`

Parameters

- image (`PipelineImageInput`) — The input image to condition the generation on. Must be an image, a list of images or a `torch.Tensor`.
- 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`).
- height (`int`, defaults to `480`) — The height of the generated video.
- width (`int`, defaults to `832`) — The width of the generated video.
- num_frames (`int`, defaults to `81`) — The number of frames in the generated video.
- num_inference_steps (`int`, defaults to `50`) — 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 `5.0`) — Guidance scale as defined in Classifier-Free Diffusion Guidance. `guidance_scale` is defined as `w` of equation 2. of Imagen Paper. 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.
- 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` 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 `"pil"`) — 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.
- 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`, optional, defaults to `512`) — The maximum sequence length of the prompt.
- shift (`float`, optional, defaults to `5.0`) — The shift of the flow.
- autocast_dtype (`torch.dtype`, optional, defaults to `torch.bfloat16`) — The dtype to use for the torch.amp.autocast.

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.

The call function to the pipeline for generation.

Examples:

    >>> 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)

####
encode_prompt

< source >

( prompt: typing.Union[str, typing.List[str]] negative_prompt: typing.Union[str, typing.List[str], NoneType] = None do_classifier_free_guidance: bool = True num_videos_per_prompt: int = 1 prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None max_sequence_length: int = 226 device: typing.Optional[torch.device] = None dtype: typing.Optional[torch.dtype] = None )

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

Encodes the prompt into text encoder hidden states.

##
WanPipelineOutput

###
class diffusers.pipelines.wan.pipeline_output.WanPipelineOutput

< source >

( frames: Tensor )

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)`.

Output class for Wan pipelines.

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