# Copyright 2025 The Wan Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import html
from typing import Any, Callable, Dict, List, Optional, Union
import ftfy
import regex as re
import torch
from transformers import AutoTokenizer, UMT5EncoderModel
from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
from diffusers.loaders import WanLoraLoaderMixin
from diffusers.models import AutoencoderKLWan, WanTransformer3DModel
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
from diffusers.utils import is_torch_xla_available, logging, replace_example_docstring
from diffusers.utils.torch_utils import randn_tensor
from diffusers.video_processor import VideoProcessor
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.wan.pipeline_output import WanPipelineOutput
if is_torch_xla_available():
import torch_xla.core.xla_model as xm
XLA_AVAILABLE = True
else:
XLA_AVAILABLE = False
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
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)
```
"""
@torch.cuda.amp.autocast(dtype=torch.float32)
def optimized_scale(positive_flat, negative_flat):
# Calculate dot production
dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
# Squared norm of uncondition
squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
# st_star = v_cond^T * v_uncond / ||v_uncond||^2
st_star = dot_product / squared_norm
return st_star
def basic_clean(text):
text = ftfy.fix_text(text)
text = html.unescape(html.unescape(text))
return text.strip()
def whitespace_clean(text):
text = re.sub(r"\s+", " ", text)
text = text.strip()
return text
def prompt_clean(text):
text = whitespace_clean(basic_clean(text))
return text
class WanPipeline(DiffusionPipeline, WanLoraLoaderMixin):
r"""
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.).
Args:
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`]):
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.
"""
model_cpu_offload_seq = "text_encoder->transformer->vae"
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__(
self,
tokenizer: AutoTokenizer,
text_encoder: UMT5EncoderModel,
transformer: WanTransformer3DModel,
vae: AutoencoderKLWan,
scheduler: FlowMatchEulerDiscreteScheduler,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
transformer=transformer,
scheduler=scheduler,
)
self.vae_scale_factor_temporal = 2 ** sum(self.vae.temperal_downsample) if getattr(self, "vae", None) else 4
self.vae_scale_factor_spatial = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
def _get_t5_prompt_embeds(
self,
prompt: Union[str, List[str]] = None,
num_videos_per_prompt: int = 1,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
):
device = device or self._execution_device
dtype = dtype or self.text_encoder.dtype
prompt = [prompt] if isinstance(prompt, str) else prompt
prompt = [prompt_clean(u) for u in prompt]
batch_size = len(prompt)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
add_special_tokens=True,
return_attention_mask=True,
return_tensors="pt",
)
text_input_ids, mask = text_inputs.input_ids, text_inputs.attention_mask
seq_lens = mask.gt(0).sum(dim=1).long()
prompt_embeds = self.text_encoder(text_input_ids.to(device), mask.to(device)).last_hidden_state
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
prompt_embeds = [u[:v] for u, v in zip(prompt_embeds, seq_lens)]
prompt_embeds = torch.stack(
[torch.cat([u, u.new_zeros(max_sequence_length - u.size(0), u.size(1))]) for u in prompt_embeds], dim=0
)
# duplicate text embeddings for each generation per prompt, using mps friendly method
_, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
return prompt_embeds
def encode_prompt(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
do_classifier_free_guidance: bool = True,
num_videos_per_prompt: int = 1,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
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
"""
device = device or self._execution_device
prompt = [prompt] if isinstance(prompt, str) else prompt
if prompt is not None:
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
prompt_embeds = self._get_t5_prompt_embeds(
prompt=prompt,
num_videos_per_prompt=num_videos_per_prompt,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
if do_classifier_free_guidance and negative_prompt_embeds is None:
negative_prompt = negative_prompt or ""
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
if prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
negative_prompt_embeds = self._get_t5_prompt_embeds(
prompt=negative_prompt,
num_videos_per_prompt=num_videos_per_prompt,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
return prompt_embeds, negative_prompt_embeds
def check_inputs(
self,
prompt,
negative_prompt,
height,
width,
prompt_embeds=None,
negative_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
if height % 16 != 0 or width % 16 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`: {negative_prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
elif negative_prompt is not None and (
not isinstance(negative_prompt, str) and not isinstance(negative_prompt, list)
):
raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
def prepare_latents(
self,
batch_size: int,
num_channels_latents: int = 16,
height: int = 480,
width: int = 832,
num_frames: int = 81,
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if latents is not None:
return latents.to(device=device, dtype=dtype)
num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
shape = (
batch_size,
num_channels_latents,
num_latent_frames,
int(height) // self.vae_scale_factor_spatial,
int(width) // self.vae_scale_factor_spatial,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
return latents
@property
def guidance_scale(self):
return self._guidance_scale
@property
def do_classifier_free_guidance(self):
return self._guidance_scale > 1.0
@property
def num_timesteps(self):
return self._num_timesteps
@property
def current_timestep(self):
return self._current_timestep
@property
def interrupt(self):
return self._interrupt
@property
def attention_kwargs(self):
return self._attention_kwargs
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
negative_prompt: Union[str, 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: Optional[int] = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
output_type: Optional[str] = "np",
return_dict: bool = True,
attention_kwargs: Optional[Dict[str, Any]] = None,
callback_on_step_end: Optional[
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
max_sequence_length: int = 512,
use_cfg_zero_star: Optional[bool] = False,
use_zero_init: Optional[bool] = True,
zero_steps: Optional[int] = 0,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
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](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). 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`](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 `"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](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.
autocast_dtype (`torch.dtype`, *optional*, defaults to `torch.bfloat16`):
The dtype to use for the torch.amp.autocast.
Examples:
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.
"""
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
negative_prompt,
height,
width,
prompt_embeds,
negative_prompt_embeds,
callback_on_step_end_tensor_inputs,
)
self._guidance_scale = guidance_scale
self._attention_kwargs = attention_kwargs
self._current_timestep = None
self._interrupt = False
device = self._execution_device
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt=prompt,
negative_prompt=negative_prompt,
do_classifier_free_guidance=self.do_classifier_free_guidance,
num_videos_per_prompt=num_videos_per_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
max_sequence_length=max_sequence_length,
device=device,
)
transformer_dtype = self.transformer.dtype
prompt_embeds = prompt_embeds.to(transformer_dtype)
if negative_prompt_embeds is not None:
negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.transformer.config.in_channels
latents = self.prepare_latents(
batch_size * num_videos_per_prompt,
num_channels_latents,
height,
width,
num_frames,
torch.float32,
device,
generator,
latents,
)
# 6. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
self._num_timesteps = len(timesteps)
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
if self.interrupt:
continue
self._current_timestep = t
latent_model_input = latents.to(transformer_dtype)
timestep = t.expand(latents.shape[0])
noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
if self.do_classifier_free_guidance:
noise_pred_uncond = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=negative_prompt_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
noise_pred_text = noise_pred
if use_cfg_zero_star:
positive_flat = noise_pred_text.view(batch_size, -1)
negative_flat = noise_pred_uncond.view(batch_size, -1)
alpha = optimized_scale(positive_flat,negative_flat)
alpha = alpha.view(batch_size, 1, 1, 1)
if (i <= zero_steps) and use_zero_init:
noise_pred = noise_pred_text*0.
else:
noise_pred = noise_pred_uncond * alpha + guidance_scale * (noise_pred_text - noise_pred_uncond * alpha)
else:
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if XLA_AVAILABLE:
xm.mark_step()
self._current_timestep = None
if not output_type == "latent":
latents = latents.to(self.vae.dtype)
latents_mean = (
torch.tensor(self.vae.config.latents_mean)
.view(1, self.vae.config.z_dim, 1, 1, 1)
.to(latents.device, latents.dtype)
)
latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
latents.device, latents.dtype
)
latents = latents / latents_std + latents_mean
video = self.vae.decode(latents, return_dict=False)[0]
video = self.video_processor.postprocess_video(video, output_type=output_type)
else:
video = latents
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (video,)
return WanPipelineOutput(frames=video)