mirror of
https://github.com/ostris/ai-toolkit.git
synced 2026-01-26 16:39:47 +00:00
541 lines
20 KiB
Python
541 lines
20 KiB
Python
# WIP, coming soon ish
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from functools import partial
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import torch
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import yaml
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from toolkit.accelerator import unwrap_model
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from toolkit.basic import flush
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from toolkit.config_modules import GenerateImageConfig, ModelConfig
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from toolkit.prompt_utils import PromptEmbeds
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from toolkit.paths import REPOS_ROOT
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from transformers import AutoTokenizer, UMT5EncoderModel
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from diffusers import AutoencoderKLWan, WanImageToVideoPipeline, WanTransformer3DModel
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import os
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import sys
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import weakref
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import torch
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import yaml
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from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO
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from toolkit.config_modules import GenerateImageConfig, ModelConfig
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from toolkit.prompt_utils import PromptEmbeds
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import os
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import copy
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from toolkit.config_modules import ModelConfig, GenerateImageConfig
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import torch
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from diffusers import FlowMatchEulerDiscreteScheduler, UniPCMultistepScheduler
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from transformers import CLIPVisionModel, CLIPImageProcessor
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import torch.nn.functional as F
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from diffusers.pipelines.wan.pipeline_output import WanPipelineOutput
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from diffusers.pipelines.wan.pipeline_wan import XLA_AVAILABLE
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from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
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from typing import Any, Callable, Dict, List, Optional, Union
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from diffusers.video_processor import VideoProcessor
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from diffusers.image_processor import PipelineImageInput
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from PIL import Image
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from .wan21 import \
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scheduler_configUniPC, \
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scheduler_config, \
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Wan21
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class AggressiveWanI2VUnloadPipeline(WanImageToVideoPipeline):
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def __init__(
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self,
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tokenizer: AutoTokenizer,
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text_encoder: UMT5EncoderModel,
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image_encoder: CLIPVisionModel,
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image_processor: CLIPImageProcessor,
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transformer: WanTransformer3DModel,
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vae: AutoencoderKLWan,
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scheduler: FlowMatchEulerDiscreteScheduler,
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device: torch.device = torch.device("cuda"),
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):
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super().__init__(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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image_encoder=image_encoder,
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transformer=transformer,
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scheduler=scheduler,
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image_processor=image_processor,
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)
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self._exec_device = device
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@property
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def _execution_device(self):
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return self._exec_device
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@torch.no_grad()
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def __call__(
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self,
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image: PipelineImageInput,
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prompt: Union[str, List[str]] = None,
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negative_prompt: Union[str, List[str]] = None,
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height: int = 480,
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width: int = 832,
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num_frames: int = 81,
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num_inference_steps: int = 50,
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guidance_scale: float = 5.0,
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num_videos_per_prompt: Optional[int] = 1,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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latents: Optional[torch.Tensor] = None,
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prompt_embeds: Optional[torch.Tensor] = None,
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negative_prompt_embeds: Optional[torch.Tensor] = None,
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output_type: Optional[str] = "np",
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return_dict: bool = True,
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attention_kwargs: Optional[Dict[str, Any]] = None,
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callback_on_step_end: Optional[
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Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
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] = None,
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callback_on_step_end_tensor_inputs: List[str] = ["latents"],
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max_sequence_length: int = 512,
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):
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if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
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callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
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# unload vae and transformer
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device = self.transformer.device
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self.text_encoder.to(device)
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self.vae.to('cpu')
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self.image_encoder.to('cpu')
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flush()
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# 1. Check inputs. Raise error if not correct
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self.check_inputs(
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prompt,
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negative_prompt,
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image,
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height,
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width,
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prompt_embeds,
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negative_prompt_embeds,
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callback_on_step_end_tensor_inputs,
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)
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self._guidance_scale = guidance_scale
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self._attention_kwargs = attention_kwargs
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self._current_timestep = None
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self._interrupt = False
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# 2. Define call parameters
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if prompt is not None and isinstance(prompt, str):
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batch_size = 1
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elif prompt is not None and isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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# 3. Encode input prompt
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prompt_embeds, negative_prompt_embeds = self.encode_prompt(
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prompt=prompt,
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negative_prompt=negative_prompt,
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do_classifier_free_guidance=self.do_classifier_free_guidance,
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num_videos_per_prompt=num_videos_per_prompt,
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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max_sequence_length=max_sequence_length,
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device=device,
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)
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# unload text encoder
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print("Unloading text encoder")
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self.text_encoder.to("cpu")
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self.transformer.to(device)
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flush()
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# Encode image embedding
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transformer_dtype = self.transformer.dtype
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prompt_embeds = prompt_embeds.to(transformer_dtype)
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if negative_prompt_embeds is not None:
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negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype)
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self.image_encoder.to(device)
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self.vae.to(device)
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image_embeds = self.encode_image(image)
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image_embeds = image_embeds.repeat(batch_size, 1, 1)
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image_embeds = image_embeds.to(transformer_dtype)
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# 4. Prepare timesteps
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self.scheduler.set_timesteps(num_inference_steps, device=device)
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timesteps = self.scheduler.timesteps
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# 5. Prepare latent variables
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num_channels_latents = self.vae.config.z_dim
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image = self.video_processor.preprocess(image, height=height, width=width).to(device, dtype=torch.float32)
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latents, condition = self.prepare_latents(
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image,
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batch_size * num_videos_per_prompt,
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num_channels_latents,
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height,
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width,
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num_frames,
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torch.bfloat16,
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device,
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generator,
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latents,
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)
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self.image_encoder.to('cpu')
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self.vae.to('cpu')
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flush()
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# 6. Denoising loop
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num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
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self._num_timesteps = len(timesteps)
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with self.progress_bar(total=num_inference_steps) as progress_bar:
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for i, t in enumerate(timesteps):
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if self.interrupt:
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continue
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self._current_timestep = t
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latent_model_input = torch.cat([latents, condition], dim=1).to(transformer_dtype)
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timestep = t.expand(latents.shape[0])
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noise_pred = self.transformer(
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hidden_states=latent_model_input,
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timestep=timestep,
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encoder_hidden_states=prompt_embeds,
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encoder_hidden_states_image=image_embeds,
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attention_kwargs=attention_kwargs,
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return_dict=False,
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)[0]
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if self.do_classifier_free_guidance:
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noise_uncond = self.transformer(
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hidden_states=latent_model_input,
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timestep=timestep,
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encoder_hidden_states=negative_prompt_embeds,
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encoder_hidden_states_image=image_embeds,
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attention_kwargs=attention_kwargs,
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return_dict=False,
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)[0]
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noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)
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# compute the previous noisy sample x_t -> x_t-1
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latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
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if callback_on_step_end is not None:
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callback_kwargs = {}
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for k in callback_on_step_end_tensor_inputs:
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callback_kwargs[k] = locals()[k]
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callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
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latents = callback_outputs.pop("latents", latents)
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prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
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negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
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# call the callback, if provided
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if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
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progress_bar.update()
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if XLA_AVAILABLE:
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xm.mark_step()
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self._current_timestep = None
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self.vae.to(device)
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if not output_type == "latent":
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latents = latents.to(self.vae.dtype)
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latents_mean = (
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torch.tensor(self.vae.config.latents_mean)
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.view(1, self.vae.config.z_dim, 1, 1, 1)
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.to(latents.device, latents.dtype)
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)
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latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
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latents.device, latents.dtype
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)
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latents = latents / latents_std + latents_mean
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video = self.vae.decode(latents, return_dict=False)[0]
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video = self.video_processor.postprocess_video(video, output_type=output_type)
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else:
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video = latents
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# Offload all models
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self.maybe_free_model_hooks()
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if not return_dict:
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return (video,)
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return WanPipelineOutput(frames=video)
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def encode_image(self, image: PipelineImageInput):
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image = self.image_processor(images=image, return_tensors="pt")
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image = {k: v.to(self.image_encoder.device, dtype=self.image_encoder.dtype) for k, v in image.items()}
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image_embeds = self.image_encoder(**image, output_hidden_states=True)
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return image_embeds.hidden_states[-2]
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class Wan21I2V(Wan21):
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arch = 'wan21_i2v'
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def __init__(
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self,
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device,
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model_config: ModelConfig,
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dtype='bf16',
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custom_pipeline=None,
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noise_scheduler=None,
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**kwargs
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):
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super().__init__(
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device, model_config, dtype,
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custom_pipeline, noise_scheduler, **kwargs
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)
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self.is_flow_matching = True
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self.is_transformer = True
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self.target_lora_modules = ['WanTransformer3DModel']
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self.image_encoder: CLIPVisionModel = None
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self.image_processor: CLIPImageProcessor = None
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def load_model(self):
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# call the super class to load most of the model
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super().load_model()
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if self.model_config.low_vram:
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# unload text encoder
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self.text_encoder.to("cpu")
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# all the base stuff is loaded. We now need to load the vision encoder stuff
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dtype = self.torch_dtype
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try:
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self.image_processor = CLIPImageProcessor.from_pretrained(
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self.model_config.extras_name_or_path ,
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subfolder="image_processor"
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)
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self.image_encoder = CLIPVisionModel.from_pretrained(
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self.model_config.extras_name_or_path,
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subfolder="image_encoder",
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torch_dtype=dtype,
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)
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except Exception as e:
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# load from name_or_path
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self.image_processor = CLIPImageProcessor.from_pretrained(
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self.model_config.name_or_path_original,
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subfolder="image_processor"
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)
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self.image_encoder = CLIPVisionModel.from_pretrained(
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self.model_config.name_or_path_original,
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subfolder="image_encoder",
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torch_dtype=dtype,
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)
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self.image_encoder.to(self.device_torch, dtype=dtype)
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self.image_encoder.eval()
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self.image_encoder.requires_grad_(False)
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if self.model_config.low_vram:
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# unload image encoder
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self.image_encoder.to("cpu")
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# rebuild the pipeline
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self.pipeline = self.get_generation_pipeline()
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flush()
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def generate_images(
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self,
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image_configs,
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sampler=None,
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pipeline=None,
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):
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# will oom on 24gb vram if we dont unload vision encoder first
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if self.model_config.low_vram:
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# unload image encoder
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self.image_encoder.to("cpu")
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self.vae.to("cpu")
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self.transformer.to("cpu")
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flush()
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super().generate_images(
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image_configs,
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sampler=sampler,
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pipeline=pipeline,
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)
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def set_device_state_preset(self, *args, **kwargs):
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# set the device state to cpu for the image encoder
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if self.model_config.low_vram:
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return
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super().set_device_state_preset(*args, **kwargs)
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def get_generation_pipeline(self):
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scheduler = UniPCMultistepScheduler(**scheduler_configUniPC)
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if self.model_config.low_vram:
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pipeline = AggressiveWanI2VUnloadPipeline(
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vae=self.vae,
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transformer=self.model,
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text_encoder=self.text_encoder,
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tokenizer=self.tokenizer,
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scheduler=scheduler,
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image_encoder=self.image_encoder,
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image_processor=self.image_processor,
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device=self.device_torch
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)
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else:
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pipeline = WanImageToVideoPipeline(
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vae=self.vae,
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transformer=self.unet,
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text_encoder=self.text_encoder,
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tokenizer=self.tokenizer,
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scheduler=scheduler,
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image_encoder=self.image_encoder,
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image_processor=self.image_processor,
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)
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# pipeline = pipeline.to(self.device_torch)
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return pipeline
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def generate_single_image(
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self,
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pipeline: WanImageToVideoPipeline,
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gen_config: GenerateImageConfig,
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conditional_embeds: PromptEmbeds,
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unconditional_embeds: PromptEmbeds,
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generator: torch.Generator,
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extra: dict,
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):
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# reactivate progress bar since this is slooooow
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pipeline.set_progress_bar_config(disable=False)
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# pipeline = pipeline.to(self.device_torch)
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if gen_config.ctrl_img is None:
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raise ValueError("I2V samples must have a control image")
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control_img = Image.open(gen_config.ctrl_img).convert("RGB")
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height = gen_config.height
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width = gen_config.width
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# make sure they are divisible by 16
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height = height // 16 * 16
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width = width // 16 * 16
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# resize the control image
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control_img = control_img.resize((width, height), Image.LANCZOS)
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output = pipeline(
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image=control_img,
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prompt_embeds=conditional_embeds.text_embeds.to(
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self.device_torch, dtype=self.torch_dtype),
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negative_prompt_embeds=unconditional_embeds.text_embeds.to(
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self.device_torch, dtype=self.torch_dtype),
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height=height,
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width=width,
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num_inference_steps=gen_config.num_inference_steps,
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guidance_scale=gen_config.guidance_scale,
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latents=gen_config.latents,
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num_frames=gen_config.num_frames,
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generator=generator,
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return_dict=False,
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output_type="pil",
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**extra
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)[0]
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# shape = [1, frames, channels, height, width]
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batch_item = output[0] # list of pil images
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if gen_config.num_frames > 1:
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return batch_item # return the frames.
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else:
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# get just the first image
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img = batch_item[0]
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return img
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def preprocess_clip_image(self, image_n1p1):
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# tensor shape: (bs, ch, height, width) with values in range [-1, 1]
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# Convert from [-1, 1] to [0, 1] range
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tensor = (image_n1p1 + 1) / 2
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# Resize to 224x224 (using bilinear interpolation, which is resample=3 in PIL)
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if tensor.shape[2] != 224 or tensor.shape[3] != 224:
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tensor = F.interpolate(tensor, size=(224, 224), mode='bilinear', align_corners=False)
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# Normalize with mean and std
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mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).view(1, 3, 1, 1).to(tensor.device)
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std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).view(1, 3, 1, 1).to(tensor.device)
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tensor = (tensor - mean) / std
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return tensor
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def get_noise_prediction(
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self,
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latent_model_input: torch.Tensor,
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timestep: torch.Tensor, # 0 to 1000 scale
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text_embeddings: PromptEmbeds,
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batch: DataLoaderBatchDTO,
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**kwargs
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):
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# videos come in (bs, num_frames, channels, height, width)
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# images come in (bs, channels, height, width)
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with torch.no_grad():
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frames = batch.tensor
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if len(frames.shape) == 4:
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first_frames = frames
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elif len(frames.shape) == 5:
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first_frames = frames[:, 0]
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else:
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raise ValueError(f"Unknown frame shape {frames.shape}")
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# first_frames shape is (bs, channels, height, width), -1 to 1
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preprocessed_frames = self.preprocess_clip_image(first_frames)
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preprocessed_frames = preprocessed_frames.to(self.device_torch, dtype=self.torch_dtype)
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# preprocessed_frame shape is (bs, 3, 224, 224)
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self.image_encoder.to(self.device_torch)
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image_embeds_full = self.image_encoder(preprocessed_frames, output_hidden_states=True)
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image_embeds = image_embeds_full.hidden_states[-2]
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image_embeds = image_embeds.to(self.device_torch, dtype=self.torch_dtype)
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# condition latent
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# first_frames shape is (bs, channels, height, width)
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# wan needs latends in (bs, channels, num_frames, height, width)
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first_frames = first_frames.unsqueeze(2)
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# video condition is first frame is the frame, the rest are zeros
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num_frames = frames.shape[1]
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|
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zero_frame = torch.zeros_like(first_frames)
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video_condition = torch.cat([
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first_frames,
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*[zero_frame for _ in range(num_frames - 1)]
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], dim=2)
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|
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# our vae encoder expects (bs, num_frames, channels, height, width)
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# permute to (bs, channels, num_frames, height, width)
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video_condition = video_condition.permute(0, 2, 1, 3, 4)
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|
|
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latent_condition = self.encode_images(
|
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video_condition,
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device=self.device_torch,
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dtype=self.torch_dtype,
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|
)
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|
latent_condition = latent_condition.to(self.device_torch, dtype=self.torch_dtype)
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|
|
|
batch_size = frames.shape[0]
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|
latent_height = latent_condition.shape[3]
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latent_width = latent_condition.shape[4]
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|
|
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mask_lat_size = torch.ones(batch_size, 1, num_frames, latent_height, latent_width)
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mask_lat_size[:, :, list(range(1, num_frames))] = 0
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|
first_frame_mask = mask_lat_size[:, :, 0:1]
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|
first_frame_mask = torch.repeat_interleave(first_frame_mask, dim=2, repeats=self.pipeline.vae_scale_factor_temporal)
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|
mask_lat_size = torch.concat([first_frame_mask, mask_lat_size[:, :, 1:, :]], dim=2)
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|
mask_lat_size = mask_lat_size.view(batch_size, -1, self.pipeline.vae_scale_factor_temporal, latent_height, latent_width)
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|
mask_lat_size = mask_lat_size.transpose(1, 2)
|
|
mask_lat_size = mask_lat_size.to(self.device_torch, dtype=self.torch_dtype)
|
|
|
|
# return latents, torch.concat([mask_lat_size, latent_condition], dim=1)
|
|
first_frame_condition = torch.concat([mask_lat_size, latent_condition], dim=1)
|
|
conditioned_latent = torch.cat([latent_model_input, first_frame_condition], dim=1)
|
|
|
|
noise_pred = self.model(
|
|
hidden_states=conditioned_latent,
|
|
timestep=timestep,
|
|
encoder_hidden_states=text_embeddings.text_embeds,
|
|
encoder_hidden_states_image=image_embeds,
|
|
return_dict=False,
|
|
**kwargs
|
|
)[0]
|
|
return noise_pred
|