feat(接口2): 支持动态切换Flux模型+分辨率 + 模型对比测试脚本
代码改动: - comfyui.py: run() 新增 unet_name 参数，提交前自动改写模型节点 (.gguf→UnetLoaderGGUF, .safetensors→UNETLoader)，并按模型自动同步 文本编码器(4b→qwen_3_4b, 9b→qwen_3_8b)，避免切换时维度不匹配 - redraw.py: run_redraw() 透传 unet_name - service.py: generate_grow_results_swap/generate_grow_results 支持 redraw_max_side(分辨率参数化) 和 unet_name 透传 - app.py: 接口2 新增 flux_model/redraw_max_side 两个 Form 参数(男女路径都加) - test_interface2.html: 新增 Flux模型/压图长边 下拉选择器 - add_hair.json/0716add-hair-api.json: 工作流默认模型改为 9b 测试脚本: - benchmark_matrix.py: 4模型×3分辨率×3图×3次 矩阵测试 - benchmark_hairstyle.py: 3图×5发型×10组合 发型对比测试 - benchmark_report.py/benchmark_hairstyle_report.py: HTML报告生成 清理: - .gitignore: 排除 benchmark_out/、报告HTML、gateway.log、*.bak.* - 移除 gateway.log 的 git 跟踪
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""发型对比矩阵测试：3图×5发型=15行，每行10张图(4b@896×1 + 9b三模型×三分辨率×9)。
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按模型分组跑（减少模型切换次数、降低OOM风险），结果重组为15行存JSON+生成报告。
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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 time
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from collections import defaultdict
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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/hairstyle")
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OUT.mkdir(parents=True, exist_ok=True)
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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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HAIRSTYLES = [
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(1, "ellipse", "椭圆"), (2, "flower", "花瓣"), (3, "heart", "心形"),
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(4, "straight", "直线"), (5, "wave", "波浪"),
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]
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# 按模型分组：每个模型对应其要跑的(分辨率,列标题)
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MODEL_GROUPS = [
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("flux-2-klein-4b-fp8.safetensors", [("896", "4B@896")]),
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("flux2.0/flux-2-klein-9b-fp8.safetensors",
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[("0", "9B-fp8@原图"), ("896", "9B-fp8@896"), ("640", "9B-fp8@640")]),
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("flux-2-klein-9b-Q5_K_M.gguf",
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[("0", "9B-Q5@原图"), ("896", "9B-Q5@896"), ("640", "9B-Q5@640")]),
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("flux-2-klein-9b-Q4_K_M.gguf",
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[("0", "9B-Q4@原图"), ("896", "9B-Q4@896"), ("640", "9B-Q4@640")]),
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]
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# 列顺序（4b在前，然后9b三模型）
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COLUMN_TITLES = ["4B@896", "9B-fp8@原图", "9B-fp8@896", "9B-fp8@640",
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"9B-Q5@原图", "9B-Q5@896", "9B-Q5@640",
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"9B-Q4@原图", "9B-Q4@896", "9B-Q4@640"]
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def gpu_used():
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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"], timeout=10)
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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, hair_num, model_file, res_val):
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fd = {"gender": "female", "hair_style": str(hair_num), "use_mask": "true",
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"prompt": "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"}
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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_b64 = None
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try:
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with open(img_path, "rb") as f:
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r = requests.post(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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elapsed = time.perf_counter() - t0
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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', '')[:60]}"
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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_b64 = res[0]["grown_image_base64"]
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elif res:
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err = "grown_image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)[:150]
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return {"elapsed": elapsed, "gpu_peak": peak, "grown_b64": grown_b64, "error": err}
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def main():
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# 结果字典: results[(img, hair_num, column_title)] = {grown_path, elapsed, gpu_peak, error}
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results = {}
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total = len(IMGS) * len(HAIRSTYLES) * len(COLUMN_TITLES)
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idx = 0
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for mfile, res_list in MODEL_GROUPS:
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mname = os.path.basename(mfile)
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print(f"\n===== 切换到模型: {mname} =====", flush=True)
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# 等模型切换稳定
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time.sleep(2)
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for ilabel, ipath in IMGS:
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for hnum, hkey, hname in HAIRSTYLES:
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for rval, ctitle in res_list:
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idx += 1
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print(f"[{idx}/{total}] {ilabel}|{hname}|{ctitle}", flush=True)
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r = call(ipath, hnum, mfile, rval)
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status = f"{r['elapsed']:.1f}s" if not r["error"] else r["error"][:40]
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print(f" -> {status} peak={r['gpu_peak']}M", flush=True)
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if r["grown_b64"]:
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fname = f"{ilabel}_{hkey}_{ctitle.replace('@','_').replace('-','')}.jpg"
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with open(OUT / fname, "wb") as gf:
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gf.write(base64.b64decode(r["grown_b64"]))
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r["grown_path"] = str(OUT / fname)
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results[(ilabel, hnum, ctitle)] = r
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# 重组为15行
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rows = []
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for ilabel, ipath in IMGS:
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for hnum, hkey, hname in HAIRSTYLES:
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cells = []
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for ct in COLUMN_TITLES:
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r = results.get((ilabel, hnum, ct), {"error": "未跑"})
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cells.append({"title": ct, **{k: v for k, v in r.items() if k != "grown_b64"}})
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rows.append({"img": ilabel, "img_path": ipath,
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"hair_num": hnum, "hair_key": hkey, "hair_name": hname,
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"cells": cells})
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with open(OUT / "results.json", "w", encoding="utf-8") as f:
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json.dump({"columns": COLUMN_TITLES, "rows": rows}, f, ensure_ascii=False, indent=2)
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ok = sum(1 for row in rows for c in row["cells"] if not c.get("error"))
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print(f"\n✓ 完成: {ok}/{total} 成功 -> {OUT/'results.json'}", flush=True)
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if __name__ == "__main__":
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main()
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