68 lines
2.6 KiB
Python
68 lines
2.6 KiB
Python
import argparse
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import os
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import torch
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import torch.distributed as dist
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from accelerate.logging import get_logger
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from fastvideo.utils.load import load_text_encoder
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logger = get_logger(__name__)
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def main(args):
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local_rank = int(os.getenv("RANK", 0))
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world_size = int(os.getenv("WORLD_SIZE", 1))
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print("world_size", world_size, "local rank", local_rank)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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torch.cuda.set_device(local_rank)
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if not dist.is_initialized():
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dist.init_process_group(backend="nccl", init_method="env://", world_size=world_size, rank=local_rank)
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text_encoder = load_text_encoder(args.model_type, args.model_path, device=device)
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autocast_type = torch.float16 if args.model_type == "hunyuan" else torch.bfloat16
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# output_dir/validation/prompt_attention_mask
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# output_dir/validation/prompt_embed
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os.makedirs(os.path.join(args.output_dir, "validation"), exist_ok=True)
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os.makedirs(
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os.path.join(args.output_dir, "validation", "prompt_attention_mask"),
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exist_ok=True,
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)
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os.makedirs(os.path.join(args.output_dir, "validation", "prompt_embed"), exist_ok=True)
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with open(args.validation_prompt_txt, "r", encoding="utf-8") as file:
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lines = file.readlines()
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prompts = [line.strip() for line in lines]
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for prompt in prompts:
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with torch.inference_mode():
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with torch.autocast("cuda", dtype=autocast_type):
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prompt_embeds, prompt_attention_mask = text_encoder.encode_prompt(prompt)
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file_name = prompt.split(".")[0]
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prompt_embed_path = os.path.join(args.output_dir, "validation", "prompt_embed", f"{file_name}.pt")
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prompt_attention_mask_path = os.path.join(
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args.output_dir,
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"validation",
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"prompt_attention_mask",
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f"{file_name}.pt",
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)
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torch.save(prompt_embeds[0], prompt_embed_path)
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torch.save(prompt_attention_mask[0], prompt_attention_mask_path)
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print(f"sample {file_name} saved")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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# dataset & dataloader
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parser.add_argument("--model_path", type=str, default="data/mochi")
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parser.add_argument("--model_type", type=str, default="mochi")
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parser.add_argument("--validation_prompt_txt", type=str)
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parser.add_argument(
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"--output_dir",
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type=str,
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default=None,
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help="The output directory where the model predictions and checkpoints will be written.",
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)
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args = parser.parse_args()
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main(args)
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