#
Wan2.1

Wan2.1 is a series of large diffusion transformer available in two versions, a high-performance 14B parameter model and a more accessible 1.3B version. Trained on billions of images and videos, it supports tasks like text-to-video (T2V) and image-to-video (I2V) while enabling features such as camera control and stylistic diversity. The Wan-VAE features better image data compression and a feature cache mechanism that encodes and decodes a video in chunks. To maintain continuity, features from previous chunks are cached and reused for processing subsequent chunks. This improves inference efficiency by reducing memory usage. Wan2.1 also uses a multilingual text encoder and the diffusion transformer models space and time relationships and text conditions with each time step to capture more complex video dynamics.

You can find all the original Wan2.1 checkpoints under the Wan-AI organization.

Click on the Wan2.1 models in the right sidebar for more examples of video generation.

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

memory
inference speed

Refer to the Reduce memory usage guide for more details about the various memory saving techniques.

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

    # 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)

##
Notes

- Wan2.1 supports LoRAs with load_lora_weights().

  Show example code

      # 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 and AutoencoderKLWan supports loading from single files with from_single_file().

  Show example code

      # pip install ftfy
      import torch
      from diffusers import WanPipeline, AutoModel

      vae = AutoModel.from_single_file(
          "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors"
      )
      transformer = AutoModel.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 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.

##
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 `"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.
- 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 image_embeds: typing.Optional[torch.Tensor] = None last_image: 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.
- 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.
- 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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