enginex-mlu370-text2video/main.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import argparse
import json
import os
import re
import time
from datetime import datetime
from pathlib import Path
import patch

from modelscope.pipelines import pipeline
from modelscope.outputs import OutputKeys
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import export_to_video


def safe_stem(text: str, maxlen: int = 60) -> str:
    """将提示词转为安全的文件名片段。"""
    text = re.sub(r"\s+", "_", text.strip())
    text = re.sub(r"[^A-Za-z0-9_\-]+", "", text)
    return (text[:maxlen] or "image").strip("_")


def load_prompts(json_path: Path):
    """
    支持 JSON 结构：
    1) ["prompt 1", "prompt 2", ...]
    """
    with open(json_path, "r", encoding="utf-8") as f:
        data = json.load(f)

    prompts = []
    if isinstance(data, list):
        if all(isinstance(x, str) for x in data):
            for s in data:
                prompts.append({"prompt": s})
        elif all(isinstance(x, dict) for x in data):
            for obj in data:
                if "prompt" not in obj:
                    raise ValueError("每个对象都需要包含 'prompt' 字段")
                prompts.append(obj)
        else:
            raise ValueError("JSON 列表元素需全为字符串或全为对象。")
    else:
        raise ValueError("JSON 顶层必须是列表。")
    return prompts


def build_pipeline(model_path: str, device: str = "cuda", dtype=torch.float16, model_type: str = "text-to-video-synthesis"):
    if model_type == "text-to-video-synthesis":
        pipe = pipeline('text-to-video-synthesis', model_path, device=device)
    elif model_type == "text-to-video-ms":
        pipe = DiffusionPipeline.from_pretrained(model_path, torch_dtype=dtype)
        pipe.enable_model_cpu_offload()       # 省显存
        pipe.enable_vae_slicing()
    else:
        raise ValueError(f"不支持的模型类型: {model_type}")
    return pipe


def generate_one(pipe, cfg: dict, out_dir: Path, index: int, model_type: str = "text-to-video-synthesis"):
    """
    依据 cfg 生成一张图并返回 (保存路径, 耗时秒, 详细参数)
    支持字段：
      - prompt (必需)
    """
    prompt = cfg["prompt"]
    stamp = datetime.now().strftime("%Y%m%d-%H%M%S")
    stem = safe_stem(prompt)
    filename = f"{index:03d}_{stem}_{stamp}.mp4"
    out_path = out_dir / filename

    start = time.time()
    if model_type == "text-to-video-synthesis":
        output_video_path = pipe({"text": prompt}, output_video=str(out_path))[OutputKeys.OUTPUT_VIDEO]
    elif model_type == "text-to-video-ms":
        frames = pipe(prompt, num_frames=16).frames[0]
        export_to_video(frames, str(out_path))
    else:
        raise ValueError(f"不支持的模型类型: {model_type}")
    elapsed = time.time() - start

    detail = {
        "index": index,
        "filename": filename,
        "elapsed_seconds": round(elapsed, 6),
        "prompt": prompt
    }
    return out_path, elapsed, detail


def main():
    parser = argparse.ArgumentParser(
        description="Stable Diffusion 基准与批量生成脚本（JSON 结果）"
    )
    parser.add_argument("--model", required=True, help="模型路径或模型名（本地目录或 HF 仓库名）")
    parser.add_argument("--json", required=True, help="测试文本 JSON 文件路径")
    parser.add_argument("--results", required=True, help="结果 JSON 文件输出路径（*.json）")
    parser.add_argument("--outdir", required=True, help="图片输出目录")
    parser.add_argument("--device", default="cuda", help="推理设备")
    parser.add_argument("--dtype", default="fp16", choices=["fp16", "fp32"], help="推理精度")
    parser.add_argument("--model_type", default="text-to-video-synthesis", choices=["text-to-video-synthesis", "text-to-video-ms"], help="模型类型")
    args, _ = parser.parse_known_args()

    model_path = args.model
    json_path = Path(args.json)
    results_path = Path(args.results)
    out_dir = Path(args.outdir)

    out_dir.mkdir(parents=True, exist_ok=True)
    results_path.parent.mkdir(parents=True, exist_ok=True)

    dtype = torch.float16 if args.dtype == "fp16" else torch.float32

    prompts = load_prompts(json_path)
    if not prompts:
        raise ValueError("测试列表为空。")

    pipe = build_pipeline(model_path=model_path, device=args.device, dtype=dtype, model_type=args.model_type)

    records = []
    total_start = time.time()
    for i, cfg in enumerate(prompts, 1):
        out_path, elapsed, detail = generate_one(pipe, cfg, out_dir, i, model_type=args.model_type)
        print(f"[{i}/{len(prompts)}] saved: {out_path.name}  elapsed: {elapsed:.3f}s")
        records.append(detail)
    total_elapsed = round(time.time() - total_start, 6)
    avg_latency = total_elapsed / len(records) if records else 0

    # 结果 JSON 结构
    result_obj = {
        "timestamp": datetime.now().isoformat(timespec="seconds"),
        "model": model_path,
        "device": str(getattr(pipe, "device", "unknown")),
        "dtype": "fp16" if dtype == torch.float16 else "fp32",
        "count": len(records),
        "total_elapsed_seconds": total_elapsed,
        "avg_latency": avg_latency,
        "cases": records
    }

    with open(results_path, "w", encoding="utf-8") as f:
        json.dump(result_obj, f, ensure_ascii=False, indent=2)

    print(f"\nAll done. vidoes: {len(records)}, total_elapsed: {total_elapsed:.3f}s, avg_latency: {avg_latency:.3f}")
    print(f"Results JSON: {results_path}")
    print(f"Output dir : {out_dir.resolve()}")


if __name__ == "__main__":
    main()