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import torch
from toolkit.basic import flush
from transformers import AutoTokenizer, UMT5EncoderModel
from diffusers import WanPipeline, WanTransformer3DModel, AutoencoderKLWan
import torch
from diffusers import FlowMatchEulerDiscreteScheduler
from typing import List
from diffusers.pipelines.wan.pipeline_output import WanPipelineOutput
from diffusers.pipelines.wan.pipeline_wan import XLA_AVAILABLE
from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
from typing import Any, Callable, Dict, List, Optional, Union
class Wan22Pipeline(WanPipeline):
def __init__(
self,
tokenizer: AutoTokenizer,
text_encoder: UMT5EncoderModel,
transformer: WanTransformer3DModel,
vae: AutoencoderKLWan,
scheduler: FlowMatchEulerDiscreteScheduler,
transformer_2: Optional[WanTransformer3DModel] = None,
boundary_ratio: Optional[float] = None,
expand_timesteps: bool = False, # Wan2.2 ti2v
device: torch.device = torch.device("cuda"),
aggressive_offload: bool = False,
):
super().__init__(
tokenizer=tokenizer,
text_encoder=text_encoder,
transformer=transformer,
transformer_2=transformer_2,
boundary_ratio=boundary_ratio,
expand_timesteps=expand_timesteps,
vae=vae,
scheduler=scheduler,
)
self._aggressive_offload = aggressive_offload
self._exec_device = device
@property
def _execution_device(self):
return self._exec_device
def __call__(
self: WanPipeline,
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,
guidance_scale_2: Optional[float] = None,
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,
noise_mask: Optional[torch.Tensor] = None,
):
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
# unload vae and transformer
vae_device = self.vae.device
transformer_device = self.transformer.device
text_encoder_device = self.text_encoder.device
device = self._exec_device
if self._aggressive_offload:
print("Unloading vae")
self.vae.to("cpu")
print("Unloading transformer")
self.transformer.to("cpu")
if self.transformer_2 is not None:
self.transformer_2.to("cpu")
self.text_encoder.to(device)
flush()
# 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,
guidance_scale_2
)
if self.config.boundary_ratio is not None and guidance_scale_2 is None:
guidance_scale_2 = guidance_scale
self._guidance_scale = guidance_scale
self._guidance_scale_2 = guidance_scale_2
self._attention_kwargs = attention_kwargs
self._current_timestep = None
self._interrupt = False
# 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,
)
if self._aggressive_offload:
# unload text encoder
print("Unloading text encoder")
self.text_encoder.to("cpu")
self.transformer.to(device)
flush()
transformer_dtype = self.transformer.dtype
prompt_embeds = prompt_embeds.to(device, transformer_dtype)
if negative_prompt_embeds is not None:
negative_prompt_embeds = negative_prompt_embeds.to(
device, 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,
)
mask = noise_mask
if mask is None:
mask = torch.ones(latents.shape, dtype=torch.float32, device=device)
# 6. Denoising loop
num_warmup_steps = len(timesteps) - \
num_inference_steps * self.scheduler.order
self._num_timesteps = len(timesteps)
if self.config.boundary_ratio is not None:
boundary_timestep = self.config.boundary_ratio * self.scheduler.config.num_train_timesteps
else:
boundary_timestep = None
current_model = self.transformer
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
if boundary_timestep is None or t >= boundary_timestep:
if self._aggressive_offload and current_model != self.transformer:
if self.transformer_2 is not None:
self.transformer_2.to("cpu")
self.transformer.to(device)
# wan2.1 or high-noise stage in wan2.2
current_model = self.transformer
current_guidance_scale = guidance_scale
else:
if self._aggressive_offload and current_model != self.transformer_2:
if self.transformer is not None:
self.transformer.to("cpu")
if self.transformer_2 is not None:
self.transformer_2.to(device)
# low-noise stage in wan2.2
current_model = self.transformer_2
current_guidance_scale = guidance_scale_2
latent_model_input = latents.to(device, transformer_dtype)
if self.config.expand_timesteps:
# seq_len: num_latent_frames * latent_height//2 * latent_width//2
temp_ts = (mask[0][0][:, ::2, ::2] * t).flatten()
# batch_size, seq_len
timestep = temp_ts.unsqueeze(0).expand(latents.shape[0], -1)
else:
timestep = t.expand(latents.shape[0])
noise_pred = current_model(
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_uncond = current_model(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=negative_prompt_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
noise_pred = noise_uncond + current_guidance_scale * \
(noise_pred - noise_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
noise_pred, t, latents, return_dict=False)[0]
# apply i2v mask
latents = (latent_model_input * (1 - mask)) + (
latents * mask
)
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 self._aggressive_offload:
# unload transformer
print("Unloading transformer")
self.transformer.to("cpu")
if self.transformer_2 is not None:
self.transformer_2.to("cpu")
# load vae
print("Loading Vae")
self.vae.to(vae_device)
flush()
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()
# move transformer back to device
if self._aggressive_offload:
# print("Moving transformer back to device")
# self.transformer.to(self._execution_device)
flush()
if not return_dict:
return (video,)
return WanPipelineOutput(frames=video)
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