Upload extensions_built_in/diffusion_models/wan22/wan22_pipeline.py with huggingface_hub

extensions_built_in/diffusion_models/wan22/wan22_pipeline.py

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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
+from diffusers.image_processor import PipelineImageInput
+
+
+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
+            
+        if num_frames % self.vae_scale_factor_temporal != 1:
+            num_frames = num_frames // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1
+        num_frames = max(num_frames, 1)
+        
+
+        width = width // (self.vae.config.scale_factor_spatial * 2) * (self.vae.config.scale_factor_spatial * 2)
+        height = height // (self.vae.config.scale_factor_spatial * 2) * (self.vae.config.scale_factor_spatial * 2)
+
+        # 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
+        
+        conditioning = None # wan2.2 i2v conditioning
+        # check shape of latents to see if it is first frame conditioned for 2.2 14b i2v
+        if latents is not None:
+            if latents.shape[1] == 36:
+                # first 16 channels are latent. other 20 are conditioning
+                conditioning = latents[:, 16:]
+                latents = latents[:, :16]
+                
+                # we need to trick the in_channls to think it is only 16 channels
+                num_channels_latents = 16
+                
+        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])
+                
+                pre_condition_latent_model_input = latent_model_input.clone()
+                
+                if conditioning is not None:
+                    # conditioning is first frame conditioning for 2.2 i2v
+                    latent_model_input = torch.cat(
+                        [latent_model_input, conditioning], dim=1)
+
+                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 = (pre_condition_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)