xsl
5bcaeb2594
测试结论: 纯生发提示词(不做皮肤处理)效果最佳。 - 替换所有位置的提示词默认值(原"填充遮罩区域的头发,皮肤加一点磨皮,再加一点美颜"等) - 涉及25个文件: app.py/hairline/service.py/hairline/redraw.py/工作流json/测试页/benchmark脚本/local_test - _REDRAW_PROMPT / _DEFAULT_PROMPT / 各接口Form默认值 / 测试页输入框默认值 全部统一
130 lines
4.5 KiB
Python
130 lines
4.5 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""接口2女性 花瓣形 单发型 4模型×3分辨率×3图×3次 矩阵测试。
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调用本机 hair-worker (:8187) 的 /api/v1/hair/grow，gender=female, hair_style=2(花瓣形)。
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每次记录：生发图、耗时、显存峰值。结果图存到 benchmark_out/matrix/，最后生成 HTML 报告。
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"""
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import base64
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import json
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import os
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import subprocess
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import sys
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import time
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from pathlib import Path
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import requests
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API = "http://127.0.0.1:8187/api/v1/hair/grow"
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TOKEN = "dev-shared-secret-2026"
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OUT = Path("/home/ubuntu/hair/benchmark_out/matrix")
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OUT.mkdir(parents=True, exist_ok=True)
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# 4 模型 × 3 分辨率 × 3 图 × 3 次
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MODELS = [
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("4b-fp8", "flux-2-klein-4b-fp8.safetensors"),
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("9b-fp8", "flux2.0/flux-2-klein-9b-fp8.safetensors"),
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("9b-Q5", "flux-2-klein-9b-Q5_K_M.gguf"),
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("9b-Q4", "flux-2-klein-9b-Q4_K_M.gguf"),
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]
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RES = [("orig", "0"), ("640", "640"), ("896", "896")]
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IMGS = [
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("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
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("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
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("girl5", "/home/ubuntu/hair/image/girl_img/girl5.jpg"),
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]
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REPEAT = 3
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def gpu_used():
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"""返回当前显存已用 MiB。"""
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try:
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out = subprocess.check_output(
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["nvidia-smi", "--query-gpu=memory.used", "--format=csv,noheader,nounits"],
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timeout=10,
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)
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return int(out.decode().strip())
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except Exception:
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return 0
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def call(img_path, model_file, res_val):
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"""调一次接口2。返回 dict: ok/elapsed/grown_path/gpu_peak/error。"""
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fd = {
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"gender": "female",
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"hair_style": "2", # 花瓣形
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"use_mask": "true",
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"prompt": "填充遮罩区域的头发",
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}
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if model_file:
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fd["flux_model"] = model_file
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if res_val != "":
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fd["redraw_max_side"] = res_val
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t0 = time.perf_counter()
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peak = gpu_used()
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err = None
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grown_path = None
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try:
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with open(img_path, "rb") as f:
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r = requests.post(
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API, headers={"X-Internal-Token": TOKEN},
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files={"image_file": (os.path.basename(img_path), f, "image/jpeg")},
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data=fd, timeout=300,
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)
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elapsed = time.perf_counter() - t0
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# 采样峰值（推理刚结束）
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peak = max(peak, gpu_used())
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j = r.json()
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if j.get("code") != 0:
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err = f"code={j.get('code')} {j.get('message','')}"
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else:
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res = j.get("data", {}).get("results", [])
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if res and res[0].get("grown_image_base64"):
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grown_path = OUT / f"tmp_grown.jpg"
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with open(grown_path, "wb") as gf:
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gf.write(base64.b64decode(res[0]["grown_image_base64"]))
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elif res:
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err = "grown_image_base64 为空"
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else:
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err = "无 results"
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except Exception as e:
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elapsed = time.perf_counter() - t0
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err = str(e)[:200]
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return {"elapsed": elapsed, "gpu_peak": peak, "grown_path": str(grown_path) if grown_path else None, "error": err}
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def main():
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results = [] # 每元素一个组合
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total = len(MODELS) * len(RES) * len(IMGS) * REPEAT
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idx = 0
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for mlabel, mfile in MODELS:
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for rlabel, rval in RES:
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for ilabel, ipath in IMGS:
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# 一个组合：3 次
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runs = []
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for rep in range(REPEAT):
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idx += 1
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print(f"[{idx}/{total}] {mlabel} | res={rlabel} | {ilabel} | rep{rep+1}", flush=True)
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r = call(ipath, mfile, rval)
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print(f" -> {r['elapsed']:.1f}s peak={r['gpu_peak']}MiB err={r['error']}", flush=True)
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# 存每次的生发图
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if r["grown_path"]:
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save_to = OUT / f"{mlabel}_{rlabel}_{ilabel}_r{rep+1}.jpg"
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os.replace(r["grown_path"], save_to)
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r["grown_path"] = str(save_to)
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runs.append(r)
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results.append({
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"model": mlabel, "model_file": mfile,
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"res": rlabel, "res_val": rval,
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"img": ilabel, "img_path": ipath,
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"runs": runs,
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})
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# 存原始数据
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with open(OUT / "results.json", "w", encoding="utf-8") as f:
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json.dump(results, f, ensure_ascii=False, indent=2)
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print(f"\n✓ 全部完成，原始数据 -> {OUT/'results.json'}", flush=True)
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if __name__ == "__main__":
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main()
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