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https://github.com/ostris/ai-toolkit.git
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272 lines
10 KiB
Python
272 lines
10 KiB
Python
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import torch
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from toolkit.basic import flush
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from transformers import AutoTokenizer, UMT5EncoderModel
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from diffusers import WanPipeline, WanTransformer3DModel, AutoencoderKLWan
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import torch
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from diffusers import FlowMatchEulerDiscreteScheduler
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from typing import List
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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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class Wan22Pipeline(WanPipeline):
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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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transformer: WanTransformer3DModel,
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vae: AutoencoderKLWan,
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scheduler: FlowMatchEulerDiscreteScheduler,
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transformer_2: Optional[WanTransformer3DModel] = None,
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boundary_ratio: Optional[float] = None,
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expand_timesteps: bool = False, # Wan2.2 ti2v
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device: torch.device = torch.device("cuda"),
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aggressive_offload: bool = False,
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):
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super().__init__(
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tokenizer=tokenizer,
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text_encoder=text_encoder,
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transformer=transformer,
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transformer_2=transformer_2,
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boundary_ratio=boundary_ratio,
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expand_timesteps=expand_timesteps,
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vae=vae,
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scheduler=scheduler,
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)
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self._aggressive_offload = aggressive_offload
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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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def __call__(
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self: WanPipeline,
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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,
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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],
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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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noise_mask: Optional[torch.Tensor] = None,
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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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vae_device = self.vae.device
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transformer_device = self.transformer.device
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text_encoder_device = self.text_encoder.device
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device = self.transformer.device
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if self._aggressive_offload:
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print("Unloading vae")
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self.vae.to("cpu")
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self.text_encoder.to(device)
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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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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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if self._aggressive_offload:
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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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transformer_dtype = self.transformer.dtype
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prompt_embeds = prompt_embeds.to(device, transformer_dtype)
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if negative_prompt_embeds is not None:
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negative_prompt_embeds = negative_prompt_embeds.to(
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device, 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.transformer.config.in_channels
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latents = self.prepare_latents(
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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.float32,
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device,
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generator,
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latents,
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)
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mask = noise_mask
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if mask is None:
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mask = torch.ones(latents.shape, dtype=torch.float32, device=device)
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# 6. Denoising loop
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num_warmup_steps = len(timesteps) - \
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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 = latents.to(device, transformer_dtype)
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if self.config.expand_timesteps:
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# seq_len: num_latent_frames * latent_height//2 * latent_width//2
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temp_ts = (mask[0][0][:, ::2, ::2] * t).flatten()
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# batch_size, seq_len
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timestep = temp_ts.unsqueeze(0).expand(latents.shape[0], -1)
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else:
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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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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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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 * \
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(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(
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noise_pred, t, latents, return_dict=False)[0]
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# apply i2v mask
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latents = (latent_model_input * (1 - mask)) + (
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latents * mask
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)
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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(
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self, i, t, callback_kwargs)
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latents = callback_outputs.pop("latents", latents)
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prompt_embeds = callback_outputs.pop(
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"prompt_embeds", prompt_embeds)
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negative_prompt_embeds = callback_outputs.pop(
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"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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if self._aggressive_offload:
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# unload transformer
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print("Unloading transformer")
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self.transformer.to("cpu")
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if self.transformer_2 is not None:
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self.transformer_2.to("cpu")
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# load vae
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print("Loading Vae")
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self.vae.to(vae_device)
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flush()
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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(
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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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# move transformer back to device
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if self._aggressive_offload:
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print("Moving transformer back to device")
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self.transformer.to(self._execution_device)
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if self.transformer_2 is not None:
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self.transformer_2.to(self._execution_device)
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flush()
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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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