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import os
from tqdm import tqdm
from diffusers import AutoencoderKLHunyuanVideo
from transformers import (
CLIPTextModel,
CLIPTokenizer,
LlamaModel,
LlamaTokenizerFast,
SiglipImageProcessor,
SiglipVisionModel,
)
from diffusers.video_processor import VideoProcessor
from diffusers.utils import export_to_video, load_image
from dataset_tool import CollectionDataset, collate_fn_map
from omegaconf import OmegaConf
from torch.utils.data import DataLoader
import torch
import torch.distributed as dist
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel as DDP
import torchvision.transforms as transforms
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from IPython.display import HTML, display
from IPython.display import clear_output # 用于清理历史输出
from accelerate import Accelerator, DistributedType
from accelerate.logging import get_logger
from accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed
from utils_framepack import encode_image, encode_prompt
def main(rank, world_size):
weight_dtype = torch.bfloat16
batch_size = 2
dataloader_num_workers = 0
output_latent_folder = "/mnt/bn/yufan-dev-my/ysh/Datasets/fp_offload_latents"
pretrained_model_name_or_path = "/mnt/bn/yufan-dev-my/ysh/Ckpts/hunyuanvideo-community/HunyuanVideo"
siglip_model_name_or_path = "/mnt/bn/yufan-dev-my/ysh/Ckpts/lllyasviel/flux_redux_bfl"
os.makedirs(output_latent_folder, exist_ok=True)
device = "cuda"
# Load the tokenizers
tokenizer_one = LlamaTokenizerFast.from_pretrained(
pretrained_model_name_or_path,
subfolder="tokenizer",
)
tokenizer_two = CLIPTokenizer.from_pretrained(
pretrained_model_name_or_path,
subfolder="tokenizer_2",
)
feature_extractor = SiglipImageProcessor.from_pretrained(
siglip_model_name_or_path,
subfolder="feature_extractor",
)
vae = AutoencoderKLHunyuanVideo.from_pretrained(
pretrained_model_name_or_path,
subfolder="vae",
torch_dtype=torch.float32,
)
vae_scale_factor_spatial = vae.spatial_compression_ratio
video_processor = VideoProcessor(vae_scale_factor=vae_scale_factor_spatial)
text_encoder_one = LlamaModel.from_pretrained(
pretrained_model_name_or_path,
subfolder="text_encoder",
torch_dtype=weight_dtype,
)
text_encoder_two = CLIPTextModel.from_pretrained(
pretrained_model_name_or_path,
subfolder="text_encoder_2",
torch_dtype=weight_dtype,
)
image_encoder = SiglipVisionModel.from_pretrained(
siglip_model_name_or_path,
subfolder="image_encoder",
torch_dtype=weight_dtype,
)
vae.requires_grad_(False)
text_encoder_one.requires_grad_(False)
text_encoder_two.requires_grad_(False)
image_encoder.requires_grad_(False)
vae.eval()
text_encoder_one.eval()
text_encoder_two.eval()
image_encoder.eval()
vae = vae.to(device)
text_encoder_one = text_encoder_one.to(device)
text_encoder_two = text_encoder_two.to(device)
image_encoder = image_encoder.to(device)
configs = OmegaConf.load("512_collection_config_vae1011_aligned_full_dump.yaml")
dataset = CollectionDataset.create_dataset_function(configs['train_data'],
configs['train_data_weights'],
**configs['data']['params'])
dataloader = DataLoader(
dataset,
shuffle=False,
batch_size=batch_size,
num_workers=dataloader_num_workers,
collate_fn=collate_fn_map,
pin_memory=True,
prefetch_factor=2 if dataloader_num_workers != 0 else None,
persistent_workers=True if dataloader_num_workers != 0 else False,
)
for idx, batch in tqdm(enumerate(dataloader), total=len(dataloader), desc="Processing batches"):
exis_flag = True
num_frames = batch["video_metadata"]["num_frames"]
for uttid, num_frame in batch["uttid"], num_frames:
output_path = os.path.join(output_latent_folder, f"{uttid}_{num_frame}.pt")
if not os.path.exists(output_path):
exis_flag = False
break
if exis_flag:
print("skipping!")
continue
with torch.no_grad():
# Get Vae feature
pixel_values = batch["videos"].permute(0, 2, 1, 3, 4).to(dtype=vae.dtype, device=device)
vae_latents = vae.encode(pixel_values).latent_dist.sample()
vae_latents = vae_latents * vae.config.scaling_factor
# Encode prompts
prompts = batch["prompts"]
prompt_embeds, pooled_prompt_embeds, prompt_attention_mask = encode_prompt(
tokenizer=tokenizer_one,
text_encoder=text_encoder_one,
tokenizer_2=tokenizer_two,
text_encoder_2=text_encoder_two,
prompt=prompts,
device=device,
)
# Prepare images
image_tensor = batch["first_frames_images"]
images = [transforms.ToPILImage()(x.to(torch.uint8)) for x in image_tensor]
image = video_processor.preprocess(image=images, height=batch["videos"].shape[-2], width=batch["videos"].shape[-1])
image_embeds = encode_image(
feature_extractor,
image_encoder,
image,
device=device,
dtype=weight_dtype,
)
for uttid, cur_vae_latent, cur_prompt_embed, cur_pooled_prompt_embed, cur_prompt_attention_mask, cur_image_embed in zip(batch["uttid"], vae_latents, prompt_embeds, pooled_prompt_embeds, prompt_attention_mask, image_embeds):
output_path = os.path.join(output_latent_folder, f"{uttid}_{pixel_values.shape[2]}.pt")
torch.save(
{
"vae_latent": cur_vae_latent.cpu().detach(),
"prompt_embed": cur_prompt_embed.cpu().detach(),
"pooled_prompt_embeds": cur_pooled_prompt_embed.cpu().detach(),
"prompt_attention_mask": cur_prompt_attention_mask.cpu().detach(),
"image_embeds": cur_image_embed.cpu().detach(),
},
output_path
)
print(f"save to: {output_path}")
def setup_distributed_env():
dist.init_process_group(backend="nccl")
torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
def cleanup_distributed_env():
dist.destroy_process_group()
if __name__ == "__main__":
setup_distributed_env()
global_rank = dist.get_rank()
local_rank = int(os.environ["LOCAL_RANK"])
device = torch.cuda.current_device()
world_size = dist.get_world_size()
main(world_size=world_size, rank = device) |