import os os.environ["HF_ENABLE_PARALLEL_LOADING"] = "yes" os.environ["DIFFUSERS_ENABLE_HUB_KERNELS"] = "yes" import argparse from concurrent.futures import ThreadPoolExecutor from pathlib import Path import torch import torch.distributed as dist from accelerate import Accelerator from helios.utils.utils_base import encode_prompt from torch.utils.data import DataLoader, Dataset from tqdm import tqdm from transformers import AutoTokenizer, UMT5EncoderModel def setup_distributed_env(): dist.init_process_group(backend="nccl") torch.cuda.set_device(int(os.environ["LOCAL_RANK"])) def check_file_exists(args): basename, idx, line, output_folder = args uttid = f"{basename}_{idx:05d}" output_path = os.path.join(output_folder, f"{uttid}.pt") if os.path.exists(output_path): return None, None return line.strip(), uttid def prepare_dataset_on_rank0(txt_file, output_folder, rank): while True: try: if rank == 0: basename = Path(txt_file).stem output_dir = Path(output_folder) existing_files = set() if output_dir.exists(): existing_files = {f.name for f in output_dir.iterdir() if f.is_file()} prompts = [] uttids = [] with open(txt_file, "r") as f: for idx, line in enumerate(f): if not line.strip(): continue uttid = f"{basename}_{idx:05d}" filename = f"{uttid}.pt" if filename not in existing_files: prompts.append(line.strip()) uttids.append(uttid) data_to_broadcast = [prompts, uttids] else: data_to_broadcast = [None, None] dist.broadcast_object_list(data_to_broadcast, src=0) break except Exception: continue return data_to_broadcast[0], data_to_broadcast[1] class PromptDataset(Dataset): def __init__(self, prompts, uttids): self.prompts = prompts self.uttids = uttids def __len__(self): return len(self.prompts) def __getitem__(self, idx): return {"prompt": self.prompts[idx], "uttid": self.uttids[idx]} def save_single_file(uttid, output_path, prompt_raw, prompt_embed): temp_to_save = { "prompt_raw": prompt_raw, "prompt_embed": prompt_embed, } try: torch.save(temp_to_save, output_path, pickle_protocol=4) return f"✓ Saved: {output_path}" except Exception as e: return f"✗ Failed to save {uttid}: {str(e)}" def main(): save_executor = ThreadPoolExecutor(max_workers=8) save_futures = [] args = parse_args() # =============== Environment =============== batch_size = 16 dataloader_num_workers = 8 feature_folders = [ "example/vidprom_first_1k.txt", ] output_folders = [ "example/toy_data/text-embedding/vidprom_filtered_extended", ] if args.weight_dtype == "fp32": args.weight_dtype = torch.float32 elif args.weight_dtype == "fp16": args.weight_dtype = torch.float16 else: args.weight_dtype = torch.bfloat16 setup_distributed_env() rank = int(os.environ["LOCAL_RANK"]) device = torch.cuda.current_device() accelerator = Accelerator() # =============== Prepare Model =============== weight_dtype = torch.bfloat16 tokenizer = AutoTokenizer.from_pretrained( args.base_model_path, subfolder="tokenizer", ) text_encoder = UMT5EncoderModel.from_pretrained( args.base_model_path, subfolder="text_encoder", dtype=weight_dtype, ) text_encoder.eval() text_encoder.requires_grad_(False) text_encoder = text_encoder.to(device) for feature_folder, output_folder in zip(feature_folders, output_folders): print(f"Process {feature_folder} !") os.makedirs(output_folder, exist_ok=True) prompts, uttids = prepare_dataset_on_rank0(feature_folder, output_folder, rank) dataset = PromptDataset(prompts, uttids) dataloader = DataLoader( dataset, batch_size=batch_size, shuffle=False, num_workers=dataloader_num_workers, prefetch_factor=2 if dataloader_num_workers > 0 else None, pin_memory=True, drop_last=False, ) dataloader = accelerator.prepare(dataloader) print(f"Dataset size: {len(dataset)}, Dataloader batches: {len(dataloader)}") print(f"Process index: {accelerator.process_index}, World size: {accelerator.num_processes}") if len(dataloader) == 0: continue # =============== Main Loop =============== if rank == 0: pbar = tqdm(total=len(dataloader), desc="Processing") for i, batch in enumerate(dataloader): batch_size = len(batch["uttid"]) uttids = batch["uttid"] prompts_raw = batch["prompt"] files_to_process = [] indices_to_process = [] for idx, uttid in enumerate(uttids): output_path = os.path.join(output_folder, f"{uttid}.pt") if os.path.exists(output_path): if rank == 0: print(f"Skipping existing file: {output_path}") else: files_to_process.append((uttid, output_path)) indices_to_process.append(idx) if len(files_to_process) == 0: if rank == 0: pbar.update(1) continue prompts_to_encode = [prompts_raw[idx] for idx in indices_to_process] with torch.no_grad(): prompt_embeds, _ = encode_prompt( tokenizer=tokenizer, text_encoder=text_encoder, prompt=prompts_to_encode, device=device, ) for idx, (uttid, output_path) in enumerate(files_to_process): prompt_embed_cpu = prompt_embeds[idx].cpu().clone() future = save_executor.submit( save_single_file, uttid, output_path, prompts_to_encode[idx], prompt_embed_cpu ) save_futures.append(future) if len(save_futures) > 100: completed_futures = [f for f in save_futures if f.done()] if rank == 0: for future in completed_futures: try: result = future.result() print(result) except Exception as e: print(f"Save task error: {e}") save_futures = [f for f in save_futures if not f.done()] if rank == 0: pbar.update(1) if rank == 0: pbar.close() def parse_args(): parser = argparse.ArgumentParser(description="Generate video with model") # === Model paths === parser.add_argument("--base_model_path", type=str, default="BestWishYsh/Helios-Base") # === Generation parameters === parser.add_argument( "--weight_dtype", type=str, default="bf16", choices=["bf16", "fp16", "fp32"], help="Data type for model weights.", ) parser.add_argument("--seed", type=int, default=42, help="Seed for random number generator.") # === Prompts === parser.add_argument( "--negative_prompt", type=str, default="Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards", ) return parser.parse_args() if __name__ == "__main__": main()