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)