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https://github.com/Wan-Video/Wan2.2.git
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29d4a35d32
* Add Wan-Animate Codes and examples * Update Wan-Animate Release date * clean codes and add copyright
649 lines
27 KiB
Python
649 lines
27 KiB
Python
# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import logging
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import math
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import os
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import cv2
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import types
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from copy import deepcopy
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from functools import partial
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from einops import rearrange
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import numpy as np
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import torch
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import torch.distributed as dist
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from peft import set_peft_model_state_dict
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from decord import VideoReader
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from tqdm import tqdm
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import torch.nn.functional as F
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from .distributed.fsdp import shard_model
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from .distributed.sequence_parallel import sp_attn_forward, sp_dit_forward
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from .distributed.util import get_world_size
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from .modules.animate import WanAnimateModel
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from .modules.animate import CLIPModel
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from .modules.t5 import T5EncoderModel
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from .modules.vae2_1 import Wan2_1_VAE
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from .modules.animate.animate_utils import TensorList, get_loraconfig
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from .utils.fm_solvers import (
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FlowDPMSolverMultistepScheduler,
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get_sampling_sigmas,
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retrieve_timesteps,
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)
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from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
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class WanAnimate:
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def __init__(
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self,
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config,
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checkpoint_dir,
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device_id=0,
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rank=0,
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t5_fsdp=False,
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dit_fsdp=False,
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use_sp=False,
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t5_cpu=False,
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init_on_cpu=True,
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convert_model_dtype=False,
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use_relighting_lora=False
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):
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r"""
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Initializes the generation model components.
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Args:
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config (EasyDict):
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Object containing model parameters initialized from config.py
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checkpoint_dir (`str`):
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Path to directory containing model checkpoints
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device_id (`int`, *optional*, defaults to 0):
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Id of target GPU device
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rank (`int`, *optional*, defaults to 0):
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Process rank for distributed training
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t5_fsdp (`bool`, *optional*, defaults to False):
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Enable FSDP sharding for T5 model
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dit_fsdp (`bool`, *optional*, defaults to False):
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Enable FSDP sharding for DiT model
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use_sp (`bool`, *optional*, defaults to False):
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Enable distribution strategy of sequence parallel.
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t5_cpu (`bool`, *optional*, defaults to False):
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Whether to place T5 model on CPU. Only works without t5_fsdp.
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init_on_cpu (`bool`, *optional*, defaults to True):
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Enable initializing Transformer Model on CPU. Only works without FSDP or USP.
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convert_model_dtype (`bool`, *optional*, defaults to False):
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Convert DiT model parameters dtype to 'config.param_dtype'.
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Only works without FSDP.
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use_relighting_lora (`bool`, *optional*, defaults to False):
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Whether to use relighting lora for character replacement.
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"""
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self.device = torch.device(f"cuda:{device_id}")
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self.config = config
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self.rank = rank
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self.t5_cpu = t5_cpu
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self.init_on_cpu = init_on_cpu
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self.num_train_timesteps = config.num_train_timesteps
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self.param_dtype = config.param_dtype
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if t5_fsdp or dit_fsdp or use_sp:
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self.init_on_cpu = False
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shard_fn = partial(shard_model, device_id=device_id)
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self.text_encoder = T5EncoderModel(
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text_len=config.text_len,
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dtype=config.t5_dtype,
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device=torch.device('cpu'),
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checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint),
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tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer),
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shard_fn=shard_fn if t5_fsdp else None,
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)
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self.clip = CLIPModel(
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dtype=torch.float16,
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device=self.device,
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checkpoint_path=os.path.join(checkpoint_dir,
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config.clip_checkpoint),
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tokenizer_path=os.path.join(checkpoint_dir, config.clip_tokenizer))
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self.vae = Wan2_1_VAE(
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vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint),
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device=self.device)
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logging.info(f"Creating WanAnimate from {checkpoint_dir}")
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if not dit_fsdp:
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self.noise_model = WanAnimateModel.from_pretrained(
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checkpoint_dir,
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torch_dtype=self.param_dtype,
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device_map=self.device)
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else:
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self.noise_model = WanAnimateModel.from_pretrained(
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checkpoint_dir, torch_dtype=self.param_dtype)
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self.noise_model = self._configure_model(
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model=self.noise_model,
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use_sp=use_sp,
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dit_fsdp=dit_fsdp,
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shard_fn=shard_fn,
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convert_model_dtype=convert_model_dtype,
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use_lora=use_relighting_lora,
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checkpoint_dir=checkpoint_dir,
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config=config
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)
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if use_sp:
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self.sp_size = get_world_size()
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else:
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self.sp_size = 1
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self.sample_neg_prompt = config.sample_neg_prompt
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self.sample_prompt = config.prompt
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def _configure_model(self, model, use_sp, dit_fsdp, shard_fn,
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convert_model_dtype, use_lora, checkpoint_dir, config):
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"""
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Configures a model object. This includes setting evaluation modes,
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applying distributed parallel strategy, and handling device placement.
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Args:
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model (torch.nn.Module):
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The model instance to configure.
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use_sp (`bool`):
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Enable distribution strategy of sequence parallel.
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dit_fsdp (`bool`):
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Enable FSDP sharding for DiT model.
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shard_fn (callable):
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The function to apply FSDP sharding.
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convert_model_dtype (`bool`):
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Convert DiT model parameters dtype to 'config.param_dtype'.
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Only works without FSDP.
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Returns:
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torch.nn.Module:
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The configured model.
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"""
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model.eval().requires_grad_(False)
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if use_sp:
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for block in model.blocks:
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block.self_attn.forward = types.MethodType(
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sp_attn_forward, block.self_attn)
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model.use_context_parallel = True
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if dist.is_initialized():
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dist.barrier()
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if use_lora:
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logging.info("Loading Relighting Lora. ")
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lora_config = get_loraconfig(
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transformer=model,
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rank=128,
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alpha=128
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)
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model.add_adapter(lora_config)
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lora_path = os.path.join(checkpoint_dir, config.lora_checkpoint)
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peft_state_dict = torch.load(lora_path)["state_dict"]
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set_peft_model_state_dict(model, peft_state_dict)
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if dit_fsdp:
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model = shard_fn(model, use_lora=use_lora)
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else:
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if convert_model_dtype:
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model.to(self.param_dtype)
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if not self.init_on_cpu:
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model.to(self.device)
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return model
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def inputs_padding(self, array, target_len):
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idx = 0
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flip = False
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target_array = []
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while len(target_array) < target_len:
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target_array.append(deepcopy(array[idx]))
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if flip:
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idx -= 1
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else:
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idx += 1
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if idx == 0 or idx == len(array) - 1:
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flip = not flip
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return target_array[:target_len]
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def get_valid_len(self, real_len, clip_len=81, overlap=1):
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real_clip_len = clip_len - overlap
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last_clip_num = (real_len - overlap) % real_clip_len
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if last_clip_num == 0:
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extra = 0
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else:
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extra = real_clip_len - last_clip_num
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target_len = real_len + extra
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return target_len
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def get_i2v_mask(self, lat_t, lat_h, lat_w, mask_len=1, mask_pixel_values=None, device="cuda"):
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if mask_pixel_values is None:
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msk = torch.zeros(1, (lat_t-1) * 4 + 1, lat_h, lat_w, device=device)
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else:
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msk = mask_pixel_values.clone()
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msk[:, :mask_len] = 1
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msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
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msk = msk.view(1, msk.shape[1] // 4, 4, lat_h, lat_w)
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msk = msk.transpose(1, 2)[0]
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return msk
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def padding_resize(self, img_ori, height=512, width=512, padding_color=(0, 0, 0), interpolation=cv2.INTER_LINEAR):
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ori_height = img_ori.shape[0]
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ori_width = img_ori.shape[1]
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channel = img_ori.shape[2]
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img_pad = np.zeros((height, width, channel))
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if channel == 1:
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img_pad[:, :, 0] = padding_color[0]
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else:
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img_pad[:, :, 0] = padding_color[0]
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img_pad[:, :, 1] = padding_color[1]
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img_pad[:, :, 2] = padding_color[2]
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if (ori_height / ori_width) > (height / width):
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new_width = int(height / ori_height * ori_width)
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img = cv2.resize(img_ori, (new_width, height), interpolation=interpolation)
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padding = int((width - new_width) / 2)
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if len(img.shape) == 2:
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img = img[:, :, np.newaxis]
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img_pad[:, padding: padding + new_width, :] = img
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else:
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new_height = int(width / ori_width * ori_height)
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img = cv2.resize(img_ori, (width, new_height), interpolation=interpolation)
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padding = int((height - new_height) / 2)
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if len(img.shape) == 2:
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img = img[:, :, np.newaxis]
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img_pad[padding: padding + new_height, :, :] = img
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img_pad = np.uint8(img_pad)
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return img_pad
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def prepare_source(self, src_pose_path, src_face_path, src_ref_path):
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pose_video_reader = VideoReader(src_pose_path)
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pose_len = len(pose_video_reader)
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pose_idxs = list(range(pose_len))
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cond_images = pose_video_reader.get_batch(pose_idxs).asnumpy()
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face_video_reader = VideoReader(src_face_path)
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face_len = len(face_video_reader)
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face_idxs = list(range(face_len))
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face_images = face_video_reader.get_batch(face_idxs).asnumpy()
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height, width = cond_images[0].shape[:2]
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refer_images = cv2.imread(src_ref_path)[..., ::-1]
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refer_images = self.padding_resize(refer_images, height=height, width=width)
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return cond_images, face_images, refer_images
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def prepare_source_for_replace(self, src_bg_path, src_mask_path):
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bg_video_reader = VideoReader(src_bg_path)
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bg_len = len(bg_video_reader)
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bg_idxs = list(range(bg_len))
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bg_images = bg_video_reader.get_batch(bg_idxs).asnumpy()
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mask_video_reader = VideoReader(src_mask_path)
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mask_len = len(mask_video_reader)
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mask_idxs = list(range(mask_len))
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mask_images = mask_video_reader.get_batch(mask_idxs).asnumpy()
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mask_images = mask_images[:, :, :, 0] / 255
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return bg_images, mask_images
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def generate(
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self,
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src_root_path,
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replace_flag=False,
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clip_len=77,
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refert_num=1,
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shift=5.0,
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sample_solver='dpm++',
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sampling_steps=20,
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guide_scale=1,
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input_prompt="",
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n_prompt="",
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seed=-1,
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offload_model=True,
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):
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r"""
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Generates video frames from input image using diffusion process.
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Args:
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src_root_path ('str'):
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Process output path
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replace_flag (`bool`, *optional*, defaults to False):
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Whether to use character replace.
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clip_len (`int`, *optional*, defaults to 77):
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How many frames to generate per clips. The number should be 4n+1
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refert_num (`int`, *optional*, defaults to 1):
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How many frames used for temporal guidance. Recommended to be 1 or 5.
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shift (`float`, *optional*, defaults to 5.0):
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Noise schedule shift parameter.
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sample_solver (`str`, *optional*, defaults to 'dpm++'):
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Solver used to sample the video.
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sampling_steps (`int`, *optional*, defaults to 20):
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Number of diffusion sampling steps. Higher values improve quality but slow generation
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guide_scale (`float` or tuple[`float`], *optional*, defaults 1.0):
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Classifier-free guidance scale. We only use it for expression control.
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In most cases, it's not necessary and faster generation can be achieved without it.
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When expression adjustments are needed, you may consider using this feature.
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input_prompt (`str`):
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Text prompt for content generation. We don't recommend custom prompts (although they work)
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n_prompt (`str`, *optional*, defaults to ""):
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Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt`
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seed (`int`, *optional*, defaults to -1):
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Random seed for noise generation. If -1, use random seed
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offload_model (`bool`, *optional*, defaults to True):
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If True, offloads models to CPU during generation to save VRAM
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Returns:
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torch.Tensor:
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Generated video frames tensor. Dimensions: (C, N, H, W) where:
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- C: Color channels (3 for RGB)
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- N: Number of frames
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- H: Frame height
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- W: Frame width
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"""
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assert refert_num == 1 or refert_num == 5, "refert_num should be 1 or 5."
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seed_g = torch.Generator(device=self.device)
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seed_g.manual_seed(seed)
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if n_prompt == "":
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n_prompt = self.sample_neg_prompt
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if input_prompt == "":
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input_prompt = self.sample_prompt
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src_pose_path = os.path.join(src_root_path, "src_pose.mp4")
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src_face_path = os.path.join(src_root_path, "src_face.mp4")
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src_ref_path = os.path.join(src_root_path, "src_ref.png")
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cond_images, face_images, refer_images = self.prepare_source(src_pose_path=src_pose_path, src_face_path=src_face_path, src_ref_path=src_ref_path)
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if not self.t5_cpu:
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self.text_encoder.model.to(self.device)
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context = self.text_encoder([input_prompt], self.device)
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context_null = self.text_encoder([n_prompt], self.device)
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if offload_model:
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self.text_encoder.model.cpu()
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else:
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context = self.text_encoder([input_prompt], torch.device('cpu'))
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context_null = self.text_encoder([n_prompt], torch.device('cpu'))
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context = [t.to(self.device) for t in context]
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context_null = [t.to(self.device) for t in context_null]
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real_frame_len = len(cond_images)
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target_len = self.get_valid_len(real_frame_len, clip_len, overlap=refert_num)
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logging.info('real frames: {} target frames: {}'.format(real_frame_len, target_len))
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cond_images = self.inputs_padding(cond_images, target_len)
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face_images = self.inputs_padding(face_images, target_len)
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if replace_flag:
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src_bg_path = os.path.join(src_root_path, "src_bg.mp4")
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src_mask_path = os.path.join(src_root_path, "src_mask.mp4")
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bg_images, mask_images = self.prepare_source_for_replace(src_bg_path, src_mask_path)
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bg_images = self.inputs_padding(bg_images, target_len)
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mask_images = self.inputs_padding(mask_images, target_len)
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height, width = refer_images.shape[:2]
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start = 0
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end = clip_len
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all_out_frames = []
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while True:
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if start + refert_num >= len(cond_images):
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break
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if start == 0:
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mask_reft_len = 0
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else:
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mask_reft_len = refert_num
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batch = {
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"conditioning_pixel_values": torch.zeros(1, 3, clip_len, height, width),
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"bg_pixel_values": torch.zeros(1, 3, clip_len, height, width),
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"mask_pixel_values": torch.zeros(1, 1, clip_len, height, width),
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"face_pixel_values": torch.zeros(1, 3, clip_len, 512, 512),
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"refer_pixel_values": torch.zeros(1, 3, height, width),
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"refer_t_pixel_values": torch.zeros(refert_num, 3, height, width)
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}
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batch["conditioning_pixel_values"] = rearrange(
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torch.tensor(np.stack(cond_images[start:end]) / 127.5 - 1),
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"t h w c -> 1 c t h w",
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)
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batch["face_pixel_values"] = rearrange(
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torch.tensor(np.stack(face_images[start:end]) / 127.5 - 1),
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"t h w c -> 1 c t h w",
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)
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batch["refer_pixel_values"] = rearrange(
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torch.tensor(refer_images / 127.5 - 1), "h w c -> 1 c h w"
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)
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if start > 0:
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batch["refer_t_pixel_values"] = rearrange(
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out_frames[0, :, -refert_num:].clone().detach(),
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"c t h w -> t c h w",
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)
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batch["refer_t_pixel_values"] = rearrange(batch["refer_t_pixel_values"],
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"t c h w -> 1 c t h w",
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)
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if replace_flag:
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batch["bg_pixel_values"] = rearrange(
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torch.tensor(np.stack(bg_images[start:end]) / 127.5 - 1),
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"t h w c -> 1 c t h w",
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)
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batch["mask_pixel_values"] = rearrange(
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torch.tensor(np.stack(mask_images[start:end])[:, :, :, None]),
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"t h w c -> 1 t c h w",
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)
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for key, value in batch.items():
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if isinstance(value, torch.Tensor):
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batch[key] = value.to(device=self.device, dtype=torch.bfloat16)
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ref_pixel_values = batch["refer_pixel_values"]
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refer_t_pixel_values = batch["refer_t_pixel_values"]
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conditioning_pixel_values = batch["conditioning_pixel_values"]
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face_pixel_values = batch["face_pixel_values"]
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B, _, H, W = ref_pixel_values.shape
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T = clip_len
|
|
lat_h = H // 8
|
|
lat_w = W // 8
|
|
lat_t = T // 4 + 1
|
|
target_shape = [lat_t + 1, lat_h, lat_w]
|
|
noise = [
|
|
torch.randn(
|
|
16,
|
|
target_shape[0],
|
|
target_shape[1],
|
|
target_shape[2],
|
|
dtype=torch.float32,
|
|
device=self.device,
|
|
generator=seed_g,
|
|
)
|
|
]
|
|
|
|
max_seq_len = int(math.ceil(np.prod(target_shape) // 4 / self.sp_size)) * self.sp_size
|
|
if max_seq_len % self.sp_size != 0:
|
|
raise ValueError(f"max_seq_len {max_seq_len} is not divisible by sp_size {self.sp_size}")
|
|
|
|
with (
|
|
torch.autocast(device_type=str(self.device), dtype=torch.bfloat16, enabled=True),
|
|
torch.no_grad()
|
|
):
|
|
if sample_solver == 'unipc':
|
|
sample_scheduler = FlowUniPCMultistepScheduler(
|
|
num_train_timesteps=self.num_train_timesteps,
|
|
shift=1,
|
|
use_dynamic_shifting=False)
|
|
sample_scheduler.set_timesteps(
|
|
sampling_steps, device=self.device, shift=shift)
|
|
timesteps = sample_scheduler.timesteps
|
|
elif sample_solver == 'dpm++':
|
|
sample_scheduler = FlowDPMSolverMultistepScheduler(
|
|
num_train_timesteps=self.num_train_timesteps,
|
|
shift=1,
|
|
use_dynamic_shifting=False)
|
|
sampling_sigmas = get_sampling_sigmas(sampling_steps, shift)
|
|
timesteps, _ = retrieve_timesteps(
|
|
sample_scheduler,
|
|
device=self.device,
|
|
sigmas=sampling_sigmas)
|
|
else:
|
|
raise NotImplementedError("Unsupported solver.")
|
|
|
|
latents = noise
|
|
|
|
pose_latents_no_ref = self.vae.encode(conditioning_pixel_values.to(torch.bfloat16))
|
|
pose_latents_no_ref = torch.stack(pose_latents_no_ref)
|
|
pose_latents = torch.cat([pose_latents_no_ref], dim=2)
|
|
|
|
ref_pixel_values = rearrange(ref_pixel_values, "t c h w -> 1 c t h w")
|
|
ref_latents = self.vae.encode(ref_pixel_values.to(torch.bfloat16))
|
|
ref_latents = torch.stack(ref_latents)
|
|
|
|
mask_ref = self.get_i2v_mask(1, lat_h, lat_w, 1, device=self.device)
|
|
y_ref = torch.concat([mask_ref, ref_latents[0]]).to(dtype=torch.bfloat16, device=self.device)
|
|
|
|
img = ref_pixel_values[0, :, 0]
|
|
clip_context = self.clip.visual([img[:, None, :, :]]).to(dtype=torch.bfloat16, device=self.device)
|
|
|
|
if mask_reft_len > 0:
|
|
if replace_flag:
|
|
bg_pixel_values = batch["bg_pixel_values"]
|
|
y_reft = self.vae.encode(
|
|
[
|
|
torch.concat([refer_t_pixel_values[0, :, :mask_reft_len], bg_pixel_values[0, :, mask_reft_len:]], dim=1).to(self.device)
|
|
]
|
|
)[0]
|
|
mask_pixel_values = 1 - batch["mask_pixel_values"]
|
|
mask_pixel_values = rearrange(mask_pixel_values, "b t c h w -> (b t) c h w")
|
|
mask_pixel_values = F.interpolate(mask_pixel_values, size=(H//8, W//8), mode='nearest')
|
|
mask_pixel_values = rearrange(mask_pixel_values, "(b t) c h w -> b t c h w", b=1)[:,:,0]
|
|
msk_reft = self.get_i2v_mask(lat_t, lat_h, lat_w, mask_reft_len, mask_pixel_values=mask_pixel_values, device=self.device)
|
|
else:
|
|
y_reft = self.vae.encode(
|
|
[
|
|
torch.concat(
|
|
[
|
|
torch.nn.functional.interpolate(refer_t_pixel_values[0, :, :mask_reft_len].cpu(),
|
|
size=(H, W), mode="bicubic"),
|
|
torch.zeros(3, T - mask_reft_len, H, W),
|
|
],
|
|
dim=1,
|
|
).to(self.device)
|
|
]
|
|
)[0]
|
|
msk_reft = self.get_i2v_mask(lat_t, lat_h, lat_w, mask_reft_len, device=self.device)
|
|
else:
|
|
if replace_flag:
|
|
bg_pixel_values = batch["bg_pixel_values"]
|
|
mask_pixel_values = 1 - batch["mask_pixel_values"]
|
|
mask_pixel_values = rearrange(mask_pixel_values, "b t c h w -> (b t) c h w")
|
|
mask_pixel_values = F.interpolate(mask_pixel_values, size=(H//8, W//8), mode='nearest')
|
|
mask_pixel_values = rearrange(mask_pixel_values, "(b t) c h w -> b t c h w", b=1)[:,:,0]
|
|
y_reft = self.vae.encode(
|
|
[
|
|
torch.concat(
|
|
[
|
|
bg_pixel_values[0],
|
|
],
|
|
dim=1,
|
|
).to(self.device)
|
|
]
|
|
)[0]
|
|
msk_reft = self.get_i2v_mask(lat_t, lat_h, lat_w, mask_reft_len, mask_pixel_values=mask_pixel_values, device=self.device)
|
|
else:
|
|
y_reft = self.vae.encode(
|
|
[
|
|
torch.concat(
|
|
[
|
|
torch.zeros(3, T - mask_reft_len, H, W),
|
|
],
|
|
dim=1,
|
|
).to(self.device)
|
|
]
|
|
)[0]
|
|
msk_reft = self.get_i2v_mask(lat_t, lat_h, lat_w, mask_reft_len, device=self.device)
|
|
|
|
y_reft = torch.concat([msk_reft, y_reft]).to(dtype=torch.bfloat16, device=self.device)
|
|
y = torch.concat([y_ref, y_reft], dim=1)
|
|
|
|
arg_c = {
|
|
"context": context,
|
|
"seq_len": max_seq_len,
|
|
"clip_fea": clip_context.to(dtype=torch.bfloat16, device=self.device),
|
|
"y": [y],
|
|
"pose_latents": pose_latents,
|
|
"face_pixel_values": face_pixel_values,
|
|
}
|
|
|
|
if guide_scale > 1:
|
|
face_pixel_values_uncond = face_pixel_values * 0 - 1
|
|
arg_null = {
|
|
"context": context_null,
|
|
"seq_len": max_seq_len,
|
|
"clip_fea": clip_context.to(dtype=torch.bfloat16, device=self.device),
|
|
"y": [y],
|
|
"pose_latents": pose_latents,
|
|
"face_pixel_values": face_pixel_values_uncond,
|
|
}
|
|
|
|
for i, t in enumerate(tqdm(timesteps)):
|
|
latent_model_input = latents
|
|
timestep = [t]
|
|
|
|
timestep = torch.stack(timestep)
|
|
|
|
noise_pred_cond = TensorList(
|
|
self.noise_model(TensorList(latent_model_input), t=timestep, **arg_c)
|
|
)
|
|
|
|
if guide_scale > 1:
|
|
noise_pred_uncond = TensorList(
|
|
self.noise_model(
|
|
TensorList(latent_model_input), t=timestep, **arg_null
|
|
)
|
|
)
|
|
noise_pred = noise_pred_uncond + guide_scale * (
|
|
noise_pred_cond - noise_pred_uncond
|
|
)
|
|
else:
|
|
noise_pred = noise_pred_cond
|
|
|
|
temp_x0 = sample_scheduler.step(
|
|
noise_pred[0].unsqueeze(0),
|
|
t,
|
|
latents[0].unsqueeze(0),
|
|
return_dict=False,
|
|
generator=seed_g,
|
|
)[0]
|
|
latents[0] = temp_x0.squeeze(0)
|
|
|
|
x0 = latents
|
|
|
|
x0 = [x.to(dtype=torch.float32) for x in x0]
|
|
out_frames = torch.stack(self.vae.decode([x0[0][:, 1:]]))
|
|
|
|
if start != 0:
|
|
out_frames = out_frames[:, :, refert_num:]
|
|
|
|
all_out_frames.append(out_frames.cpu())
|
|
|
|
start += clip_len - refert_num
|
|
end += clip_len - refert_num
|
|
|
|
videos = torch.cat(all_out_frames, dim=2)[:, :, :real_frame_len]
|
|
return videos[0] if self.rank == 0 else None
|