update

Dockerfile

@@ -0,0 +1,12 @@
+FROM git.modelhub.org.cn:9443/enginex-kunlunxin/xmlir/r200-8f_xmlir-ubuntu_2004_x86_64:v0.27
+
+WORKDIR /root
+
+SHELL ["/bin/bash", "-c"]
+
+ADD xpytorch-cp38-torch201-ubuntu2004-x64-socket.run /root/
+RUN source /root/miniconda/etc/profile.d/conda.sh && conda activate python38_torch201_cuda && \
+    ./xpytorch-cp38-torch201-ubuntu2004-x64-socket.run
+
+WORKDIR /workspace
+COPY run_diffuser_test_r200_8f.py dataset.json /workspace/

README.md

@@ -1,3 +1,30 @@
 # enginex-r_series-diffusers
 
-运行于【昆仑芯 R200】系列算力卡的【文本生成图片】引擎，基于 diffusion 架构，支持 Stable Diffusion 等最新流行模型
+运行于【昆仑芯 R200】系列算力卡的【文本生成图片】引擎，基于 diffusion 架构，支持 Stable Diffusion 等最新流行模型
+
+## Quickstart
+
+### 构建镜像
+需要xpytorch-cp38-torch201-ubuntu2004-x64-socket.run文件请前往[此处](https://git.modelhub.org.cn:9443/EngineX-Kunlunxin/r200_8f_xpytorch)下载
+
+```bash
+docker build -t r200_8f-diffuser:v0.21.4 .
+```
+
+### 模型下载
+模型地址：https://modelscope.cn/models/AI-ModelScope/stable-diffusion-v1-5
+并放到目录：`/mnt/data/models/stable-diffusion-v1-5`（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`目录下生成图片文件。
+```bash
+./run_in_docker.sh
+```
+
+## 测试结果
+### stable-diffusion-v1-5模型
+
+|      | A100 平均生成时间（秒） | R200_8F 平均生成时间（秒） |
+|------|-------------------------|----------------------------|
+| 时间 | 3.0721                  | 	4.6615                    |

dataset.json

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+[
+    "An image of a squirrel in Picasso style",
+    "A cozy cabin in the woods, watercolor painting"
+]

run_diffuser_test_r200_8f.py

@@ -0,0 +1,196 @@
+#!/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 diffusers import DiffusionPipeline
+
+
+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):
+    """
+    支持两种 JSON 结构：
+    1) ["prompt 1", "prompt 2", ...]
+    2) [{"prompt": "...", "negative_prompt": "...", "num_inference_steps": 30,
+         "guidance_scale": 7.5, "seed": 42, "width": 512, "height": 512}, ...]
+    """
+    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 = DiffusionPipeline.from_pretrained(
+        model_path,
+        torch_dtype=dtype,
+        use_safetensors=True,
+    )
+    # 设备放置
+    if device == "cuda" and torch.cuda.is_available():
+        pipe.to("cuda")
+        try:
+            pipe.enable_attention_slicing()
+        except Exception:
+            pass
+        # 对大模型友好；若已放到 CUDA，会按需处理
+        try:
+            pipe.enable_model_cpu_offload()
+        except Exception:
+            pass
+    else:
+        pipe.to("cpu")
+
+    pipe.set_progress_bar_config(disable=True)
+    return pipe
+
+
+def generate_one(pipe: DiffusionPipeline, 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)
+      - negative_prompt (可选)
+      - num_inference_steps (默认 20)
+      - guidance_scale (默认 7.5)
+      - seed (可选)
+      - width, height (可选)
+    """
+    prompt = cfg["prompt"]
+    negative_prompt = cfg.get("negative_prompt", None)
+    steps = int(cfg.get("num_inference_steps", 20))
+    guidance = float(cfg.get("guidance_scale", 7.5))
+    seed = cfg.get("seed", None)
+    width = cfg.get("width", None)
+    height = cfg.get("height", None)
+
+    # 随机数生成器（与管线设备一致）
+    gen = None
+    try:
+        device_str = str(getattr(pipe, "device", "cuda" if torch.cuda.is_available() else "cpu"))
+    except Exception:
+        device_str = "cuda" if torch.cuda.is_available() else "cpu"
+    if seed is not None:
+        gen = torch.Generator(device=device_str).manual_seed(int(seed))
+
+    call_kwargs = dict(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=steps,
+        guidance_scale=guidance,
+        generator=gen,
+    )
+    if width is not None and height is not None:
+        call_kwargs.update({"width": int(width), "height": int(height)})
+
+    start = time.time()
+    images = pipe(**call_kwargs).images
+    elapsed = time.time() - start
+
+    stamp = datetime.now().strftime("%Y%m%d-%H%M%S")
+    stem = safe_stem(prompt)
+    filename = f"{index:03d}_{stem}_{stamp}.png"
+    out_path = out_dir / filename
+    images[0].save(out_path)
+
+    detail = {
+        "index": index,
+        "filename": filename,
+        "elapsed_seconds": round(elapsed, 6),
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "num_inference_steps": steps,
+        "guidance_scale": guidance,
+        "seed": seed,
+        "width": width,
+        "height": height,
+    }
+    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_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. images: {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__":
+    main()

run_in_docker.sh

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+#! /usr/bin/env bash
+image=r200_8f-diffuser:v0.21.4
+docker run -e TZ=Asia/Shanghai -v /mnt/data/models/stable-diffusion-v1-5:/workspace/stable-diffusion-v1-5 \
+    --security-opt=seccomp=unconfined --cap-add=SYS_PTRACE --cap-add=SYS_ADMIN --device /dev/fuse --shm-size=32g --ulimit=memlock=-1 --ulimit=nofile=120000 --ulimit=stack=67108864 --device=/dev/xpu1:/dev/xpu1 --device=/dev/xpuctrl:/dev/xpuctrl \
+    $image \
+    /bin/bash -c 'source /root/miniconda/etc/profile.d/conda.sh && conda activate python38_torch201_cuda && python3 run_diffuser_test_r200_8f.py --model /workspace/stable-diffusion-v1-5 --json dataset.json --results result.json --outdir output --device cuda --dtype fp16'