214 lines
7.2 KiB
Python
214 lines
7.2 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import argparse
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import json
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import os
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import re
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import time
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from datetime import datetime
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from pathlib import Path
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import torch
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from diffusers import DiffusionPipeline
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def safe_stem(text: str, maxlen: int = 60) -> str:
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"""将提示词转为安全的文件名片段。"""
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text = re.sub(r"\s+", "_", text.strip())
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text = re.sub(r"[^A-Za-z0-9_\-]+", "", text)
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return (text[:maxlen] or "image").strip("_")
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def load_prompts(json_path: Path):
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"""
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支持两种 JSON 结构：
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1) ["prompt 1", "prompt 2", ...]
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2) [{"prompt": "...", "negative_prompt": "...", "num_inference_steps": 30,
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"guidance_scale": 7.5, "seed": 42, "width": 512, "height": 512}, ...]
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"""
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with open(json_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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prompts = []
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if isinstance(data, list):
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if all(isinstance(x, str) for x in data):
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for s in data:
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prompts.append({"prompt": s})
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elif all(isinstance(x, dict) for x in data):
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for obj in data:
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if "prompt" not in obj:
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raise ValueError("每个对象都需要包含 'prompt' 字段")
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prompts.append(obj)
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else:
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raise ValueError("JSON 列表元素需全为字符串或全为对象。")
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else:
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raise ValueError("JSON 顶层必须是列表。")
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return prompts
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def build_pipeline(model_path: str, device: str = "cuda", dtype=torch.float16):
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pipe = DiffusionPipeline.from_pretrained(
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model_path,
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torch_dtype=dtype,
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use_safetensors=True,
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)
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# 设备放置
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if device == "cuda":
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if torch.cuda.is_available():
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pipe.to("cuda")
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elif torch.npu.is_available():
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pipe.to("npu")
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try:
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pipe.enable_attention_slicing()
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except Exception:
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pass
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# 对大模型友好；若已放到 CUDA，会按需处理
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try:
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pipe.enable_model_cpu_offload()
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except Exception:
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pass
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else:
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pipe.to("cpu")
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pipe.set_progress_bar_config(disable=True)
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return pipe
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def generate_one(pipe: DiffusionPipeline, cfg: dict, out_dir: Path, index: int):
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"""
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依据 cfg 生成一张图并返回 (保存路径, 耗时秒, 详细参数)
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支持字段：
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- prompt (必需)
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- negative_prompt (可选)
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- num_inference_steps (默认 20)
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- guidance_scale (默认 7.5)
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- seed (可选)
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- width, height (可选)
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"""
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prompt = cfg["prompt"]
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negative_prompt = cfg.get("negative_prompt", None)
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steps = int(cfg.get("num_inference_steps", 0))
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guidance = float(cfg.get("guidance_scale", 0))
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seed = cfg.get("seed", None)
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width = cfg.get("width", None)
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height = cfg.get("height", None)
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# 随机数生成器（与管线设备一致）
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# gen = None
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# try:
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# device_str = str(getattr(pipe, "device", "cuda" if torch.cuda.is_available() else "npu" if torch.npu.is_available() else "cpu"))
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# except Exception:
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# device_str = "cuda" if torch.cuda.is_available() else "npu" if torch.npu.is_available() else "cpu"
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# if seed is not None:
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# gen = torch.Generator(device=device_str).manual_seed(int(seed))
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call_kwargs = dict(
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prompt=prompt,
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# generator=gen,
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)
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if width is not None and height is not None:
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call_kwargs.update({"width": int(width), "height": int(height)})
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if negative_prompt is not None:
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call_kwargs.update({"negative_prompt": negative_prompt})
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if guidance > 0:
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call_kwargs.update({"guidance_scale": guidance})
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if steps > 0:
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call_kwargs.update({"num_inference_steps": steps})
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start = time.time()
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images = pipe(**call_kwargs).images
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elapsed = time.time() - start
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stamp = datetime.now().strftime("%Y%m%d-%H%M%S")
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stem = safe_stem(prompt)
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filename = f"{index:03d}_{stem}_{stamp}.png"
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out_path = out_dir / filename
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images[0].save(out_path)
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detail = {
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"index": index,
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"filename": filename,
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"elapsed_seconds": round(elapsed, 6),
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"prompt": prompt,
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"negative_prompt": negative_prompt,
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"num_inference_steps": steps,
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"guidance_scale": guidance,
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"seed": seed,
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"width": width,
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"height": height,
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}
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return out_path, elapsed, detail
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def main():
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parser = argparse.ArgumentParser(
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description="Stable Diffusion 基准与批量生成脚本（JSON 结果）"
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)
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parser.add_argument("--model", required=True, help="模型路径或模型名（本地目录或 HF 仓库名）")
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parser.add_argument("--json", required=True, help="测试文本 JSON 文件路径")
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parser.add_argument("--results", required=True, help="结果 JSON 文件输出路径（*.json）")
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parser.add_argument("--outdir", required=True, help="图片输出目录")
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parser.add_argument("--device", default="cuda", choices=["cuda", "cpu"], help="推理设备")
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parser.add_argument("--dtype", default="fp16", choices=["fp16", "fp32"], help="推理精度")
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parser.add_argument("--negative_prompt", default=None, help="negative_prompt")
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parser.add_argument("--num_inference_steps", default=0, help="num_inference_steps")
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parser.add_argument("--guidance_scale", default=0, help="guidance_scale")
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args = parser.parse_args()
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model_path = args.model
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json_path = Path(args.json)
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results_path = Path(args.results)
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out_dir = Path(args.outdir)
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out_dir.mkdir(parents=True, exist_ok=True)
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results_path.parent.mkdir(parents=True, exist_ok=True)
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dtype = torch.float16 if args.dtype == "fp16" else torch.float32
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prompts = load_prompts(json_path)
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if not prompts:
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raise ValueError("测试列表为空。")
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pipe = build_pipeline(model_path=model_path, device=args.device, dtype=dtype)
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records = []
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total_start = time.time()
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for i, cfg in enumerate(prompts, 1):
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if args.negative_prompt:
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cfg["negative_prompt"] = args.negative_prompt
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if args.num_inference_steps:
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cfg["num_inference_steps"] = args.num_inference_steps
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if args.guidance_scale:
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cfg["guidance_scale"] = args.guidance_scale
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out_path, elapsed, detail = generate_one(pipe, cfg, out_dir, i)
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print(f"[{i}/{len(prompts)}] saved: {out_path.name} elapsed: {elapsed:.3f}s")
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records.append(detail)
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total_elapsed = round(time.time() - total_start, 6)
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avg_latency = total_elapsed / len(records) if records else 0
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# 结果 JSON 结构
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result_obj = {
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"timestamp": datetime.now().isoformat(timespec="seconds"),
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"model": model_path,
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"device": str(getattr(pipe, "device", "unknown")),
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"dtype": "fp16" if dtype == torch.float16 else "fp32",
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"count": len(records),
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"total_elapsed_seconds": total_elapsed,
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"avg_latency": avg_latency,
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"cases": records
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}
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with open(results_path, "w", encoding="utf-8") as f:
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json.dump(result_obj, f, ensure_ascii=False, indent=2)
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print(f"\nAll done. images: {len(records)}, total_elapsed: {total_elapsed:.3f}s, avg_latency: {avg_latency:.3f}")
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print(f"Results JSON: {results_path}")
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print(f"Images dir : {out_dir.resolve()}")
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if __name__ == "__main__":
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main()
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