init commit

Dockerfile

@@ -0,0 +1,9 @@
+FROM git.modelhub.org.cn:9443/enginex-metax/diffusers.training:maca.ai3.0.0.5-torch2.4-py310-ubuntu22.04-amd64
+
+RUN /opt/conda/bin/pip install pytorch_lightning opencv-python-headless==4.10.0.84 imageio[ffmpeg] einops datasets==3.2.0 simplejson open_clip_torch==2.24.0 sortedcontainers modelscope av==11.0.0 addict
+
+WORKDIR /opt/app
+
+COPY ./main.py ./dataset.json ./
+
+ENTRYPOINT ["/opt/conda/bin/python"]

README.md

@@ -0,0 +1,24 @@
+## Quickstart
+
+### 构建镜像
+```bash
+docker build -t text2video:v0.1 .
+```
+
+### 模型下载
+模型地址：https://modelscope.cn/models/iic/text-to-video-synthesis
+并放到目录：`/mnt/contest_ceph/zhanghao/models/iic/text-to-video-synthesis`（如更改目录，请修改后面的执行脚本中的模型路径）
+
+### 测试程序
+1. 准备输入数据集，可以参考示例`dataset.json`
+2. 在docker镜像里运行测试程序，会根据`dataset.json`内容，在`output`目录下生成视频文件。
+```bash
+python3 main.py --model "/mnt/contest_ceph/zhanghao/models/iic/text-to-video-synthesis" --json  "dataset.json" --results "results.json" --outdir "output" --device cuda --dtype fp16
+```
+
+## 测试结果
+|      | A100 平均生成时间（秒） | MetaX C500 平均生成时间（秒） |
+|------|-------------------------|----------------------------|
+| 时间 | 12                  | 16                     |
+
+

dataset.json

@@ -0,0 +1,4 @@
+[
+    "An Running Dog",
+    "Trump talking in front of white house"
+]

main.py

@@ -0,0 +1,161 @@
+#!/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 torch
+from functools import wraps
+
+_orig_load = torch.load
+
+@wraps(_orig_load)
+def _load_patch(*args, **kwargs):
+    kwargs.setdefault("weights_only", False)
+    return _orig_load(*args, **kwargs)
+
+torch.load = _load_patch
+
+from modelscope.pipelines import pipeline
+from modelscope.outputs import OutputKeys
+
+
+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):
+    pipe = pipeline('text-to-video-synthesis', model_path)
+    # pipe.to("cuda")
+    return pipe
+
+
+def generate_one(pipe, cfg: dict, out_dir: Path, index: int):
+    """
+    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()
+    output_video_path = pipe({"text": prompt}, output_video=str(out_path))[OutputKeys.OUTPUT_VIDEO]
+    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", choices=["cuda", "cpu"], help="推理设备")
+    parser.add_argument("--dtype", default="fp16", choices=["fp16", "fp32"], help="推理精度")
+    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)
+
+    records = []
+    total_start = time.time()
+    for i, cfg in enumerate(prompts, 1):
+        out_path, elapsed, detail = generate_one(pipe, cfg, out_dir, i)
+        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"Images dir : {out_dir.resolve()}")
+
+
+if __name__ == "__main__":
+
+# Check what version of PyTorch is installed
+    print(torch.__version__)
+
+# Check the current CUDA version being used
+    print("CUDA Version: ", torch.version.cuda)
+
+# Check if CUDA is available and if so, print the device name
+    print("Device name:", torch.cuda.get_device_properties("cuda").name)
+
+# Check if FlashAttention is available
+    print("FlashAttention available:", torch.backends.cuda.flash_sdp_enabled())
+    main()