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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()
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