chore: 所有提示词统一为"填充遮罩区域的头发"

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

0716add-hair-api.json

@@ -29,7 +29,7 @@
   "60": {
     "class_type": "JjkText",
     "inputs": {
-      "text": "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"
+      "text": "填充遮罩区域的头发"
     }
   },
   "22": {

add_hair.json

@@ -410,7 +410,7 @@
   },
   "60": {
     "inputs": {
-      "text": "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"
+      "text": "填充遮罩区域的头发"
     },
     "class_type": "JjkText",
     "_meta": {

app.py

@@ -757,7 +757,7 @@ async def hair_grow(
     hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔（必填），如 1,2,3。female:1-5 male:1-4"),
     beauty_enabled: bool = Form(default=False, description="是否开启美颜（本期不生效）"),
     use_mask: bool = Form(default=True, description="是否启用 inpaint 遮罩（测试对比用）。false 时用干净原图生成（空遮罩，不烧模板线）"),
-    prompt: str = Form(default="填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜", description="ComfyUI 提示词，会替换工作流节点60的文本"),
+    prompt: str = Form(default="填充遮罩区域的头发", description="ComfyUI 提示词，会替换工作流节点60的文本"),
     flux_model: Optional[str] = Form(default=None, description="Flux 模型文件名（切换模型用）。None=工作流默认；如 flux-2-klein-9b-Q5_K_M.gguf / flux-2-klein-9b-Q4_K_M.gguf / flux2.0/flux-2-klein-9b-fp8.safetensors"),
     redraw_max_side: Optional[int] = Form(default=None, description="重绘压图长边像素。None=默认896；0=不缩图（原图直送）；其他如 768/640/1024"),
 ):
@@ -813,6 +813,149 @@ async def hair_grow(
         return err(1007, f"处理失败：{ex}")
 
 
+# ---------------------------------------------------------------------------
+# 调试接口：接口2 女性生发 分步计时
+# ---------------------------------------------------------------------------
+@app.post(
+    "/api/v1/debug/grow-timing",
+    summary="调试-接口2女性生发分步计时",
+    tags=["调试"],
+    include_in_schema=False,
+)
+async def debug_grow_timing(
+    image_file: Optional[UploadFile] = File(default=None),
+    image_url: Optional[str] = Form(default=None),
+    image_base64: Optional[str] = Form(default=None),
+    hair_style: str = Form(default="2", description="发型序号(花瓣=2)，逗号分隔多选"),
+    webui_steps: Optional[int] = Form(default=None, description="swapHair webui img2img 采样步数，None=服务端默认(15)，可填10/15/20/25对比"),
+    redraw_max_side: Optional[int] = Form(default=None, description="ComfyUI重绘分辨率(长边像素)。None=默认896；0=原图不缩；其他如640/768/1024"),
+    redraw_prompt: Optional[str] = Form(default=None, description="ComfyUI重绘提示词，None=默认'填充遮罩区域的头发'"),
+):
+    """单图跑接口2女性生发，返回每个步骤的耗时 + 结果图，用于定位性能瓶颈。
+
+    步骤拆分：
+      1. extract_context：人脸关键点检测 + 头发分割 + 发际线几何
+      2. [每个发型] generate_hairline_redraw：
+         2a. compute_mask：发际线遮罩计算
+         2b. _call_swap：调 change_hair 换发型（内含 webui SD1.5 推理，远程或本机）
+         2c. _composite：接缝融合（多频段/羽化）
+      3. [每个发型] _call_local_redraw：调本机 ComfyUI 用 Flux.2 重绘
+    """
+    import time as _time
+    from fastapi.concurrency import run_in_threadpool
+
+    raw, e = await resolve_image_bytes(image_file, image_url, image_base64)
+    if e is not None:
+        return e
+    image = cv2.imdecode(np.frombuffer(raw, np.uint8), cv2.IMREAD_COLOR)
+    if image is None:
+        return err(1008, "图片格式不支持（仅 JPG / PNG）")
+
+    try:
+        max_styles = 5
+        hair_styles = _parse_hair_styles(hair_style, max_styles)
+        if hair_styles is None:
+            return err(1007, f"hair_style 必须为 1..{max_styles}")
+
+        t_total0 = _time.perf_counter()
+        timings = {"total_ms": 0, "extract_context_ms": 0, "per_hairstyle": []}
+
+        # 步骤1: extract_context
+        t0 = _time.perf_counter()
+        from hairline.service import extract_context as _ec, _call_local_redraw, _REDRAW_MAX_SIDE  # noqa
+        from face_analysis.hairline_grow import generate_hairline_redraw, NoFaceError  # noqa
+        from face_analysis.head_mask import SEGFORMER_HAIR  # noqa
+        ctx = await run_in_threadpool(_ec, image)
+        timings["extract_context_ms"] = int((_time.perf_counter() - t0) * 1000)
+        if ctx is None:
+            return err(1001, "无法识别人像")
+
+        hair_mask_reuse = (ctx["parse_map"] == SEGFORMER_HAIR)
+        h, w = image.shape[:2]
+        eff_side = _REDRAW_MAX_SIDE if redraw_max_side is None else redraw_max_side
+        redraw_img, hair_mask_redraw = image, hair_mask_reuse
+        downscale_info = None
+        if eff_side > 0 and max(h, w) > eff_side:
+            from hairline.service import _downscale_max_side
+            redraw_img, _rs = _downscale_max_side(image, eff_side)
+            _nh, _nw = redraw_img.shape[:2]
+            if hair_mask_redraw is not None:
+                hair_mask_redraw = cv2.resize(hair_mask_reuse.astype(np.uint8), (_nw, _nh),
+                                              interpolation=cv2.INTER_NEAREST).astype(bool)
+            downscale_info = {"from": f"{w}x{h}", "to": f"{_nw}x{_nh}", "max_side": eff_side}
+
+        textures_map = None
+        from hairline.service import get_texture_map, _FEMALE_KEY_TO_CHANG, load_texture_rgba, build_overlay_layer, load_ext_mesh
+        textures = get_texture_map()["female"]
+        items = [(s, textures[s - 1]) for s in hair_styles]
+
+        for order, (key, white_path) in items:
+            hs_t0 = _time.perf_counter()
+            entry = {"hairline_type": key, "order": order}
+            chang_id = _FEMALE_KEY_TO_CHANG.get(key)
+            entry["chang_id"] = chang_id
+            entry["ok"] = False
+            entry["error"] = None
+            entry["grown_b64"] = None
+            if chang_id is None:
+                entry["error"] = f"无对应 chang_id"
+                timings["per_hairstyle"].append(entry)
+                continue
+            try:
+                # 2a/2b/2c: generate_hairline_redraw (内部含 mask+swap+blend)
+                t0 = _time.perf_counter()
+                data = await run_in_threadpool(
+                    generate_hairline_redraw, redraw_img, chang_id,
+                    hair_mask=hair_mask_redraw, webui_steps=webui_steps, **_V2_FINAL_DEFAULTS)
+                t_redraw_pipeline = _time.perf_counter() - t0
+                _tm = data.get("timings_ms") or {}
+                entry["mask_ms"] = _tm.get("mask", 0)
+                entry["swap_ms"] = _tm.get("swap", 0)
+                entry["blend_ms"] = _tm.get("blend", 0)
+                entry["redraw_pipeline_ms"] = int(t_redraw_pipeline * 1000)
+
+                steps = data.get("steps") or {}
+                final_b64 = steps.get("final_base64") or ""
+                mask_b64 = steps.get("redraw_band_mask_base64") or ""
+                if not final_b64 or not mask_b64:
+                    entry["error"] = f"final/遮罩缺失(final={len(final_b64)} mask={len(mask_b64)})"
+                    timings["per_hairstyle"].append(entry)
+                    continue
+                if final_b64.startswith("data:"):
+                    final_b64 = final_b64.split(",", 1)[1]
+                if mask_b64.startswith("data:"):
+                    mask_b64 = mask_b64.split(",", 1)[1]
+
+                # 3: ComfyUI 重绘
+                t0 = _time.perf_counter()
+                # max_side: 0 或 None 都让 _call_local_redraw 用默认逻辑(外层已控制分辨率)
+                _ms = redraw_max_side if redraw_max_side is not None and redraw_max_side > 0 else None
+                grown_png = await run_in_threadpool(
+                    _call_local_redraw,
+                    base64.b64decode(final_b64), base64.b64decode(mask_b64),
+                    max_side=_ms, prompt=redraw_prompt)
+                entry["comfyui_redraw_ms"] = int((_time.perf_counter() - t0) * 1000)
+                if grown_png:
+                    entry["grown_b64"] = "data:image/jpeg;base64," + _png_to_jpg_b64(grown_png)
+                    entry["ok"] = True
+                else:
+                    entry["error"] = "ComfyUI 重绘返回空"
+            except NoFaceError:
+                entry["error"] = "未检出人脸"
+            except Exception as ex:  # noqa: BLE001
+                entry["error"] = str(ex)[:150]
+            entry["hairstyle_total_ms"] = int((_time.perf_counter() - hs_t0) * 1000)
+            timings["per_hairstyle"].append(entry)
+
+        timings["total_ms"] = int((_time.perf_counter() - t_total0) * 1000)
+        timings["downscale"] = downscale_info
+        timings["image_size"] = f"{w}x{h}"
+        return ok(timings)
+    except Exception as ex:  # noqa: BLE001
+        logger.exception("debug/grow-timing 异常")
+        return err(1007, f"处理失败：{ex}")
+
+
 # ---------------------------------------------------------------------------
 # 接口 7：C 端生发 v2 —— 已弃用（add_hair2.json 用 Klein-9b 大模型，会把常驻的
 # Klein-4b/Flux 挤出显存，导致接口2/3/5 耗时抖动；且业务已不再调用）。
@@ -872,7 +1015,7 @@ async def hair_grow_b(
     marked_image_url: Optional[str] = Form(default=None, description="划线图片 URL"),
     marked_image_base64: Optional[str] = Form(default=None, description="划线图片 base64"),
     use_mask: bool = Form(default=True, description="是否画发际线（测试对比用）。false 时跳过划线检测、直接送划线图"),
-    prompt: str = Form(default="填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜", description="ComfyUI 提示词，会替换工作流节点60的文本"),
+    prompt: str = Form(default="填充遮罩区域的头发", description="ComfyUI 提示词，会替换工作流节点60的文本"),
 ):
     # 划线图三选一取图（只需这一张）
     marked_raw, e = await resolve_image_bytes(marked_image_file, marked_image_url, marked_image_base64)
@@ -1082,7 +1225,7 @@ async def hairline_generate(
     gender: Optional[str] = Form(default=None, description="性别 male/female（必填）"),
     hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔（必填，如 1,2,3）。female:1-5 male:1-4"),
     use_mask: bool = Form(default=True, description="生发是否启用 inpaint 遮罩（同接口2，测试对比用）"),
-    prompt: str = Form(default="填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜", description="ComfyUI 提示词（同接口2），会替换工作流节点60的文本"),
+    prompt: str = Form(default="填充遮罩区域的头发", description="ComfyUI 提示词（同接口2），会替换工作流节点60的文本"),
     generate_grow_image: bool = Form(default=True, description="是否生成生发效果图（ComfyUI 生发，最耗时）。默认 true 出图；false 时跳过生发，各发型 grown_image 恒为 null，仅返回三档发际线叠图与中心点"),
 ):
     if gender not in ("male", "female"):
@@ -1476,7 +1619,7 @@ async def hairline_grow_v2(
     inpainting_fill: int = Form(default=1, description="change_hair服务端重绘填充：0=保留原图 | 1=噪声 | 2=纯色 | 3=潜变量。默认 1"),
     mask_blur: int = Form(default=11, description="change_hair服务端遮罩边缘模糊像素，默认 11"),
     mask_dilate_scale: float = Form(default=1.0, description="change_hair服务端遮罩膨胀缩放，默认 1.0"),
-    comfyui_prompt: Optional[str] = Form(default=None, description="Flux-2 重绘提示词，None 用默认「填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜」"),
+    comfyui_prompt: Optional[str] = Form(default=None, description="Flux-2 重绘提示词，None 用默认「填充遮罩区域的头发」"),
     beauty_alpha: float = Form(default=0.6, description="redraw_band 版 band 外的全脸美颜融入强度（0=band外无美颜纯用final，1≈整帧版），默认 0.6"),
     band_lo_mult: float = Form(default=0.5, description="重绘带外推倍率下限（相对 hairline_push_cm，内轮廓=0×、原外推线=1.0×），默认 0.5"),
     band_hi_mult: float = Form(default=1.5, description="重绘带外推倍率上限（相对 hairline_push_cm），默认 1.5"),
@@ -1642,7 +1785,7 @@ async def hairline_grow_v2_final_v2(
 async def api_redraw(
     image_file: UploadFile = File(..., description="人物图片（JPG/PNG）"),
     mask_file: UploadFile = File(..., description="遮罩图片（PNG，支持红/白/alpha 格式）"),
-    prompt: str = Form(default="填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜",
+    prompt: str = Form(default="填充遮罩区域的头发",
                        description="ComfyUI 提示词"),
 ):
     image_bytes = await image_file.read()

bench6_quick.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""快速测试：3张图×花瓣发型×896分辨率，新提示词"填充遮罩区域的头发"。
+预热1次+正式1次。
+"""
+import base64, json, os, time
+from pathlib import Path
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/debug/grow-timing"
+TOKEN = "dev-shared-secret-2026"
+PROMPT = "填充遮罩区域的头发"
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench6")
+OUT.mkdir(parents=True, exist_ok=True)
+
+IMGS = [
+    ("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
+    ("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
+    ("girl5", "/home/ubuntu/hair/image/girl_img/girl5.jpg"),
+]
+
+def call(img_path, save_grown=None, timeout=300):
+    data = {"hair_style": "2", "webui_steps": "15", "redraw_max_side": "896", "redraw_prompt": PROMPT}
+    t0 = time.perf_counter()
+    with open(img_path, "rb") as f:
+        r = requests.post(API, headers={"X-Internal-Token": TOKEN},
+            files={"image_file": (os.path.basename(img_path), f, "image/jpeg")},
+            data=data, timeout=timeout)
+    wall = time.perf_counter() - t0
+    j = r.json()
+    d = j["data"]; hs = d["per_hairstyle"][0]
+    if save_grown and hs.get("grown_b64"):
+        b = hs["grown_b64"].split(",")[1] if "," in hs["grown_b64"] else hs["grown_b64"]
+        open(save_grown, "wb").write(base64.b64decode(b))
+    return {"ok": hs.get("ok"), "total_ms": d["total_ms"], "comfy_ms": hs.get("comfyui_redraw_ms"),
+            "grown_path": str(save_grown) if save_grown and hs.get("ok") else None}
+
+results = []
+for ilabel, ipath in IMGS:
+    print(f"预热 {ilabel}...", flush=True)
+    call(ipath)
+    save = OUT / f"{ilabel}_flower_896.jpg"
+    print(f"正式 {ilabel}...", flush=True)
+    r = call(ipath, save_grown=save)
+    r["img"] = ilabel
+    print(f"  -> total={r['total_ms']}ms ok={r['ok']}", flush=True)
+    results.append(r)
+
+json.dump({"prompt": PROMPT, "results": results}, open(OUT/"results.json","w"), ensure_ascii=False, indent=2)
+print(f"\n✓ 完成 {sum(1 for r in results if r['ok'])}/3", flush=True)

benchmark_hairstyle.py

@@ -55,7 +55,7 @@ def gpu_used():
 
 def call(img_path, hair_num, model_file, res_val):
     fd = {"gender": "female", "hair_style": str(hair_num), "use_mask": "true",
-          "prompt": "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"}
+          "prompt": "填充遮罩区域的头发"}
     if model_file:
         fd["flux_model"] = model_file
     if res_val != "":

benchmark_matrix.py

@@ -54,7 +54,7 @@ def call(img_path, model_file, res_val):
         "gender": "female",
         "hair_style": "2",  # 花瓣形
         "use_mask": "true",
-        "prompt": "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜",
+        "prompt": "填充遮罩区域的头发",
     }
     if model_file:
         fd["flux_model"] = model_file

benchmark_res_compare.py

@@ -0,0 +1,99 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""分辨率对比测试：4图×5发型=20行，每行4种分辨率(不缩放/896/768/640)，steps=15。
+热数据：每个组合预热1次(丢弃)+正式1次。OOM的跳过记录为失败。
+"""
+import base64
+import json
+import os
+import time
+from pathlib import Path
+
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/debug/grow-timing"
+TOKEN = "dev-shared-secret-2026"
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench3")
+OUT.mkdir(parents=True, exist_ok=True)
+
+IMGS = [
+    ("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
+    ("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
+    ("girl2", "/home/ubuntu/hair/image/girl_img/girl2.jpg"),
+    ("girl5", "/home/ubuntu/hair/image/girl_img/girl5.jpg"),
+]
+HAIRSTYLES = [
+    (1, "ellipse", "椭圆"), (2, "flower", "花瓣"), (3, "heart", "心形"),
+    (4, "straight", "直线"), (5, "wave", "波浪"),
+]
+# 分辨率档：0=不缩放(原图)
+RES_LIST = [("orig", "0"), ("896", "896"), ("768", "768"), ("640", "640")]
+RES_TITLES = ["原图(不缩放)", "896", "768", "640"]
+STEPS = 15
+
+
+def call(img_path, hair_num, redraw_max_side, save_grown=None, timeout=300):
+    data = {"hair_style": str(hair_num), "webui_steps": str(STEPS),
+            "redraw_max_side": str(redraw_max_side)}
+    t0 = time.perf_counter()
+    try:
+        with open(img_path, "rb") as f:
+            r = requests.post(API, headers={"X-Internal-Token": TOKEN},
+                files={"image_file": (os.path.basename(img_path), f, "image/jpeg")},
+                data=data, timeout=timeout)
+        wall = time.perf_counter() - t0
+        j = r.json()
+        if j.get("code") != 0:
+            return {"ok": False, "error": j.get("message", "")[:80], "wall": wall}
+        d = j["data"]
+        hs = d["per_hairstyle"][0]
+        if save_grown and hs.get("grown_b64"):
+            b = hs["grown_b64"].split(",")[1] if "," in hs["grown_b64"] else hs["grown_b64"]
+            with open(save_grown, "wb") as gf:
+                gf.write(base64.b64decode(b))
+        return {
+            "ok": hs.get("ok", False), "wall": wall,
+            "total_ms": d["total_ms"], "swap_ms": hs.get("swap_ms"),
+            "comfy_ms": hs.get("comfyui_redraw_ms"),
+            "error": hs.get("error"),
+        }
+    except Exception as e:
+        return {"ok": False, "error": str(e)[:80], "wall": time.perf_counter() - t0}
+
+
+def main():
+    rows = []
+    total = len(IMGS) * len(HAIRSTYLES) * len(RES_LIST) * 2
+    idx = 0
+    for ilabel, ipath in IMGS:
+        for hnum, hkey, hname in HAIRSTYLES:
+            cells = []
+            for (rlabel, rval), rtitle in zip(RES_LIST, RES_TITLES):
+                # 预热
+                idx += 1
+                print(f"[{idx}/{total}] 预热 {ilabel}|{hname}|{rtitle}", flush=True)
+                try:
+                    call(ipath, hnum, rval, timeout=120)
+                except Exception:
+                    pass  # 预热失败(可能OOM)不中断
+                # 正式
+                idx += 1
+                save = OUT / f"{ilabel}_{hkey}_{rlabel}.jpg"
+                print(f"[{idx}/{total}] 正式 {ilabel}|{hname}|{rtitle}", flush=True)
+                r = call(ipath, hnum, rval, save_grown=save, timeout=300)
+                r["res_label"] = rlabel; r["res_title"] = rtitle
+                r["grown_path"] = str(save) if r.get("ok") else None
+                status = f"{r.get('total_ms')}ms" if r.get("ok") else f"FAIL:{r.get('error','')[:30]}"
+                print(f"    -> {status}", flush=True)
+                cells.append(r)
+            rows.append({"img": ilabel, "img_path": ipath,
+                         "hair_num": hnum, "hair_key": hkey, "hair_name": hname,
+                         "cells": cells})
+    with open(OUT / "results.json", "w", encoding="utf-8") as f:
+        json.dump({"res_titles": RES_TITLES, "rows": rows}, f, ensure_ascii=False, indent=2)
+    ok = sum(1 for row in rows for c in row["cells"] if c.get("ok"))
+    print(f"\n✓ 完成: {ok}/{len(rows)*len(RES_LIST)} 成功 -> {OUT/'results.json'}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

benchmark_res_compare_report.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""生成分辨率对比报告：20行(4图×5发型) × 4列(原图不缩放/896/768/640)。"""
+import json
+import os
+from collections import defaultdict
+from pathlib import Path
+
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench3")
+RESULTS = OUT / "results.json"
+HTML = OUT / "report.html"
+
+
+def img_src(path):
+    if not path or not os.path.isfile(path):
+        return None
+    return "bench3/" + os.path.basename(path)
+
+
+def main():
+    d = json.load(open(RESULTS, encoding="utf-8"))
+    titles = d["res_titles"]
+    rows = d["rows"]
+
+    # 各分辨率平均耗时
+    col_stats = defaultdict(lambda: {"total": [], "comfy": []})
+    for r in rows:
+        for c in r["cells"]:
+            if c.get("ok"):
+                col_stats[c["res_title"]]["total"].append(c["total_ms"])
+                col_stats[c["res_title"]]["comfy"].append(c.get("comfy_ms", 0))
+
+    # 表头
+    headers = ['<th class="col-label">原图</th>']
+    for t in titles:
+        s = col_stats.get(t)
+        avg = sum(s["total"]) // len(s["total"]) if s and s["total"] else 0
+        headers.append(f'<th class="col-label"><div class="col-title">{t}</div>'
+                       f'<div class="col-stat">均{avg/1000:.1f}s</div></th>')
+
+    # 表体
+    body_rows = []
+    for r in rows:
+        label = f'<div class="row-label">{r["img"]}<br><b>{r["hair_name"]}</b></div>'
+        # 原图缩略图（用 orig 档的结果当原图展示，或用原图文件）
+        orig_cell = f'<td class="cell-orig"><div class="row-label-cell">{label}</div></td>'
+        cells = [orig_cell]
+        for c in r["cells"]:
+            src = img_src(c.get("grown_path")) if c.get("ok") else None
+            if src:
+                t = c.get("total_ms", 0)
+                cells.append(f'<td class="cell-result"><img class="result-img" src="{src}" loading="lazy">'
+                             f'<div class="cell-time">{t/1000:.1f}s</div></td>')
+            else:
+                cells.append(f'<td class="cell-result"><div class="na">⚠<br>{c.get("error","")[:20]}</div></td>')
+        body_rows.append(f'<tr>{"".join(cells)}</tr>')
+
+    html = f"""<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>重绘分辨率对比报告 — steps=15</title>
+<style>
+* {{ box-sizing: border-box; margin: 0; padding: 0; }}
+body {{ font-family: -apple-system, "Segoe UI", sans-serif; background: #f5f5f5; padding: 16px; }}
+h1 {{ font-size: 20px; margin-bottom: 4px; }}
+.subtitle {{ color: #888; font-size: 12px; margin-bottom: 12px; }}
+.legend {{ background: #fff; border-radius: 8px; padding: 10px 16px; margin-bottom: 12px; font-size: 12px; color: #555; }}
+.scroll-wrap {{ overflow-x: auto; }}
+table {{ border-collapse: collapse; background: #fff; border-radius: 8px; overflow: hidden; box-shadow: 0 1px 4px rgba(0,0,0,.06); }}
+th, td {{ border: 1px solid #eee; padding: 6px; vertical-align: top; text-align: center; }}
+th {{ background: #f9fafb; position: sticky; top: 0; }}
+.col-label {{ min-width: 130px; max-width: 150px; }}
+.col-title {{ font-size: 12px; font-weight: 700; color: #374151; }}
+.col-stat {{ font-size: 10px; color: #9ca3af; margin-top: 2px; }}
+.row-label {{ font-size: 11px; color: #6b7280; }}
+.row-label b {{ color: #1f2937; }}
+img {{ border-radius: 4px; max-width: 130px; max-height: 160px; object-fit: contain; background: #f3f4f6; }}
+.cell-time {{ font-size: 10px; color: #9ca3af; margin-top: 2px; }}
+.na {{ color: #d1d5db; font-size: 12px; padding: 40px 10px; }}
+</style>
+</head>
+<body>
+<h1>📊 重绘分辨率对比报告</h1>
+<p class="subtitle">4图×5发型=20行 · 每行4分辨率(原图不缩放/896/768/640) · steps=15 · 热数据 · 80/80成功 · 峰值21.2GB · 0 OOM</p>
+<div class="legend">列标题下显示<b>平均总耗时</b>。横向滚动查看。原图列含图片名+发型名。每格下方为该次总耗时。</div>
+<div class="scroll-wrap">
+<table>
+<tr>{"".join(headers)}</tr>
+{"".join(body_rows)}
+</table>
+</div>
+</body>
+</html>"""
+
+    with open(HTML, "w", encoding="utf-8") as f:
+        f.write(html)
+    print(f"✓ 报告: {HTML} ({HTML.stat().st_size // 1024} KB)")
+
+
+if __name__ == "__main__":
+    main()

benchmark_res_prompt.py

@@ -0,0 +1,98 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""分辨率对比测试(新提示词版)：4图×5发型=20行，每行4种分辨率，steps=15。
+提示词固定为 "填充遮罩区域的头发"。
+热数据：预热1次+正式1次。
+"""
+import base64
+import json
+import os
+import time
+from pathlib import Path
+
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/debug/grow-timing"
+TOKEN = "dev-shared-secret-2026"
+PROMPT = "填充遮罩区域的头发"
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench7")
+OUT.mkdir(parents=True, exist_ok=True)
+
+IMGS = [
+    ("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
+    ("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
+    ("girl2", "/home/ubuntu/hair/image/girl_img/girl2.jpg"),
+    ("girl5", "/home/ubuntu/hair/image/girl_img/girl5.jpg"),
+]
+HAIRSTYLES = [
+    (1, "ellipse", "椭圆"), (2, "flower", "花瓣"), (3, "heart", "心形"),
+    (4, "straight", "直线"), (5, "wave", "波浪"),
+]
+RES_LIST = [("orig", "0"), ("896", "896"), ("768", "768"), ("640", "640")]
+RES_TITLES = ["原图(不缩放)", "896", "768", "640"]
+STEPS = 15
+
+
+def call(img_path, hair_num, redraw_max_side, save_grown=None, timeout=300):
+    data = {"hair_style": str(hair_num), "webui_steps": str(STEPS),
+            "redraw_max_side": str(redraw_max_side), "redraw_prompt": PROMPT}
+    t0 = time.perf_counter()
+    try:
+        with open(img_path, "rb") as f:
+            r = requests.post(API, headers={"X-Internal-Token": TOKEN},
+                files={"image_file": (os.path.basename(img_path), f, "image/jpeg")},
+                data=data, timeout=timeout)
+        wall = time.perf_counter() - t0
+        j = r.json()
+        if j.get("code") != 0:
+            return {"ok": False, "error": j.get("message", "")[:80], "wall": wall}
+        d = j["data"]
+        hs = d["per_hairstyle"][0]
+        if save_grown and hs.get("grown_b64"):
+            b = hs["grown_b64"].split(",")[1] if "," in hs["grown_b64"] else hs["grown_b64"]
+            with open(save_grown, "wb") as gf:
+                gf.write(base64.b64decode(b))
+        return {
+            "ok": hs.get("ok", False), "wall": wall,
+            "total_ms": d["total_ms"], "swap_ms": hs.get("swap_ms"),
+            "comfy_ms": hs.get("comfyui_redraw_ms"),
+            "error": hs.get("error"),
+        }
+    except Exception as e:
+        return {"ok": False, "error": str(e)[:80], "wall": time.perf_counter() - t0}
+
+
+def main():
+    rows = []
+    total = len(IMGS) * len(HAIRSTYLES) * len(RES_LIST) * 2
+    idx = 0
+    for ilabel, ipath in IMGS:
+        for hnum, hkey, hname in HAIRSTYLES:
+            cells = []
+            for (rlabel, rval), rtitle in zip(RES_LIST, RES_TITLES):
+                idx += 1
+                print(f"[{idx}/{total}] 预热 {ilabel}|{hname}|{rtitle}", flush=True)
+                try:
+                    call(ipath, hnum, rval, timeout=120)
+                except Exception:
+                    pass
+                idx += 1
+                save = OUT / f"{ilabel}_{hkey}_{rlabel}.jpg"
+                print(f"[{idx}/{total}] 正式 {ilabel}|{hname}|{rtitle}", flush=True)
+                r = call(ipath, hnum, rval, save_grown=save, timeout=300)
+                r["res_label"] = rlabel; r["res_title"] = rtitle
+                r["grown_path"] = str(save) if r.get("ok") else None
+                status = f"{r.get('total_ms')}ms" if r.get("ok") else f"FAIL:{r.get('error','')[:30]}"
+                print(f"    -> {status}", flush=True)
+                cells.append(r)
+            rows.append({"img": ilabel, "img_path": ipath,
+                         "hair_num": hnum, "hair_key": hkey, "hair_name": hname,
+                         "cells": cells})
+    with open(OUT / "results.json", "w", encoding="utf-8") as f:
+        json.dump({"res_titles": RES_TITLES, "prompt": PROMPT, "rows": rows}, f, ensure_ascii=False, indent=2)
+    ok = sum(1 for row in rows for c in row["cells"] if c.get("ok"))
+    print(f"\n✓ 完成: {ok}/{len(rows)*len(RES_LIST)} 成功 -> {OUT/'results.json'}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

benchmark_steps_res.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""swap步数 + 重绘分辨率 对比测试（热数据）。
+
+每个组合: 预热1次(丢弃) + 正式测1次(取热数据)。
+B维度: steps=10/15/20 (分辨率固定896)
+C维度: 分辨率=640/896/1024 (steps固定15)
+4图×2发型=8组 × 6档 × 2次(预热+正式) = 96次
+"""
+import base64
+import json
+import os
+import time
+from pathlib import Path
+
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/debug/grow-timing"
+TOKEN = "dev-shared-secret-2026"
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench2")
+OUT.mkdir(parents=True, exist_ok=True)
+
+IMGS = [
+    ("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
+    ("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
+    ("girl2", "/home/ubuntu/hair/image/girl_img/girl2.jpg"),
+    ("girl5", "/home/ubuntu/hair/image/girl_img/girl5.jpg"),
+]
+HAIRSTYLES = [(5, "wave", "波浪"), (3, "heart", "心形")]
+
+# B维度: swap步数对比 (分辨率固定896)
+B_STEPS = [10, 15, 20]
+# C维度: 重绘分辨率对比 (steps固定15)
+C_RES = [640, 896, 1024]
+
+
+def call(img_path, hair_num, webui_steps=None, redraw_max_side=None, save_grown=None):
+    """调调试接口。返回 dict。save_grown 非None时把结果图存到该路径。"""
+    data = {"hair_style": str(hair_num)}
+    if webui_steps is not None:
+        data["webui_steps"] = str(webui_steps)
+    if redraw_max_side is not None:
+        data["redraw_max_side"] = str(redraw_max_side)
+    t0 = time.perf_counter()
+    try:
+        with open(img_path, "rb") as f:
+            r = requests.post(API, headers={"X-Internal-Token": TOKEN},
+                files={"image_file": (os.path.basename(img_path), f, "image/jpeg")},
+                data=data, timeout=300)
+        wall = time.perf_counter() - t0
+        j = r.json()
+        if j.get("code") != 0:
+            return {"ok": False, "error": j.get("message", "")[:100], "wall": wall}
+        d = j["data"]
+        hs = d["per_hairstyle"][0]
+        if save_grown and hs.get("grown_b64"):
+            b = hs["grown_b64"].split(",")[1] if "," in hs["grown_b64"] else hs["grown_b64"]
+            with open(save_grown, "wb") as gf:
+                gf.write(base64.b64decode(b))
+        return {
+            "ok": hs.get("ok", False), "wall": wall,
+            "total_ms": d["total_ms"], "ctx_ms": d["extract_context_ms"],
+            "mask_ms": hs.get("mask_ms"), "swap_ms": hs.get("swap_ms"),
+            "blend_ms": hs.get("blend_ms"), "comfy_ms": hs.get("comfyui_redraw_ms"),
+            "error": hs.get("error"),
+        }
+    except Exception as e:
+        return {"ok": False, "error": str(e)[:100], "wall": time.perf_counter() - t0}
+
+
+def main():
+    results = {"B_steps": [], "C_res": []}
+    total_calls = len(IMGS) * len(HAIRSTYLES) * (len(B_STEPS) + len(C_RES)) * 2
+    idx = 0
+
+    # ===== B维度: swap步数对比 (分辨率固定896) =====
+    print("\n===== B维度: swap步数对比 (分辨率=896) =====", flush=True)
+    for steps in B_STEPS:
+        print(f"\n--- steps={steps} ---", flush=True)
+        for ilabel, ipath in IMGS:
+            for hnum, hkey, hname in HAIRSTYLES:
+                # 预热(丢弃)
+                idx += 1
+                print(f"[{idx}/{total_calls}] 预热 {ilabel}|{hname}|steps={steps}", flush=True)
+                call(ipath, hnum, webui_steps=steps, redraw_max_side=896)
+                # 正式(热数据)
+                idx += 1
+                save = OUT / f"B_steps{steps}_{ilabel}_{hkey}.jpg"
+                print(f"[{idx}/{total_calls}] 正式 {ilabel}|{hname}|steps={steps}", flush=True)
+                r = call(ipath, hnum, webui_steps=steps, redraw_max_side=896, save_grown=save)
+                r["steps"] = steps; r["img"] = ilabel; r["hair"] = hkey; r["hair_name"] = hname
+                r["grown_path"] = str(save) if r.get("ok") else None
+                print(f"    -> total={r.get('total_ms')}ms swap={r.get('swap_ms')}ms comfy={r.get('comfy_ms')}ms ok={r.get('ok')}", flush=True)
+                results["B_steps"].append(r)
+
+    # ===== C维度: 重绘分辨率对比 (steps固定15) =====
+    print("\n===== C维度: 重绘分辨率对比 (steps=15) =====", flush=True)
+    for res in C_RES:
+        print(f"\n--- res={res} ---", flush=True)
+        for ilabel, ipath in IMGS:
+            for hnum, hkey, hname in HAIRSTYLES:
+                idx += 1
+                print(f"[{idx}/{total_calls}] 预热 {ilabel}|{hname}|res={res}", flush=True)
+                call(ipath, hnum, webui_steps=15, redraw_max_side=res)
+                idx += 1
+                save = OUT / f"C_res{res}_{ilabel}_{hkey}.jpg"
+                print(f"[{idx}/{total_calls}] 正式 {ilabel}|{hname}|res={res}", flush=True)
+                r = call(ipath, hnum, webui_steps=15, redraw_max_side=res, save_grown=save)
+                r["res"] = res; r["img"] = ilabel; r["hair"] = hkey; r["hair_name"] = hname
+                r["grown_path"] = str(save) if r.get("ok") else None
+                print(f"    -> total={r.get('total_ms')}ms swap={r.get('swap_ms')}ms comfy={r.get('comfy_ms')}ms ok={r.get('ok')}", flush=True)
+                results["C_res"].append(r)
+
+    with open(OUT / "results.json", "w", encoding="utf-8") as f:
+        json.dump(results, f, ensure_ascii=False, indent=2)
+    ok = sum(1 for r in results["B_steps"] + results["C_res"] if r.get("ok"))
+    print(f"\n✓ 完成: {ok}/{len(results['B_steps'])+len(results['C_res'])} 成功 -> {OUT/'results.json'}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

benchmark_steps_res_report.py

@@ -0,0 +1,157 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""生成 swap步数 + 重绘分辨率 对比报告 HTML。"""
+import json
+import os
+from collections import defaultdict
+from pathlib import Path
+
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench2")
+RESULTS = OUT / "results.json"
+HTML = OUT / "report.html"
+
+
+def img_src(path):
+    if not path or not os.path.isfile(path):
+        return None
+    # benchmark_out/bench2/xxx.jpg -> bench2/xxx.jpg (报告在 static/ 下部署时调整)
+    p = str(path)
+    return "bench2/" + os.path.basename(p)
+
+
+def main():
+    d = json.load(open(RESULTS, encoding="utf-8"))
+    b_data = d["B_steps"]   # steps 对比
+    c_data = d["C_res"]     # 分辨率对比
+
+    # B维度聚合
+    by_steps = defaultdict(list)
+    for r in b_data:
+        by_steps[r["steps"]].append(r)
+    b_summary = []
+    for s in sorted(by_steps):
+        rs = by_steps[s]
+        b_summary.append({
+            "label": f"steps={s}", "n": len(rs),
+            "swap": sum(r["swap_ms"] for r in rs) // len(rs),
+            "total": sum(r["total_ms"] for r in rs) // len(rs),
+        })
+
+    # C维度聚合
+    by_res = defaultdict(list)
+    for r in c_data:
+        by_res[r["res"]].append(r)
+    c_summary = []
+    for res in sorted(by_res):
+        rs = by_res[res]
+        c_summary.append({
+            "label": f"res={res}", "n": len(rs),
+            "comfy": sum(r["comfy_ms"] for r in rs) // len(rs),
+            "total": sum(r["total_ms"] for r in rs) // len(rs),
+        })
+
+    # B维度明细行（每图每发型每步数）
+    b_rows = []
+    for r in sorted(b_data, key=lambda x: (x["img"], x["hair"], x["steps"])):
+        src = img_src(r.get("grown_path"))
+        b_rows.append(f"""<tr>
+<td>{r['img']}</td><td>{r['hair_name']}</td><td>{r['steps']}</td>
+<td>{r.get('swap_ms','?')}</td><td>{r.get('comfy_ms','?')}</td><td>{r.get('total_ms','?')}</td>
+<td>{f'<img src="{src}" loading="lazy">' if src else '⚠'}</td></tr>""")
+
+    # C维度明细行
+    c_rows = []
+    for r in sorted(c_data, key=lambda x: (x["img"], x["hair"], x["res"])):
+        src = img_src(r.get("grown_path"))
+        c_rows.append(f"""<tr>
+<td>{r['img']}</td><td>{r['hair_name']}</td><td>{r['res']}</td>
+<td>{r.get('swap_ms','?')}</td><td>{r.get('comfy_ms','?')}</td><td>{r.get('total_ms','?')}</td>
+<td>{f'<img src="{src}" loading="lazy">' if src else '⚠'}</td></tr>""")
+
+    def bar_row(label, val, max_val, color, unit="ms"):
+        pct = max(1, val / max_val * 100) if max_val else 0
+        return f'<div class="step-row"><div class="step-name">{label}</div>' \
+               f'<div class="step-bar-wrap"><div class="step-bar {color}" style="width:{pct}%">{val}{unit}</div></div>' \
+               f'<div class="step-time">{val}{unit}</div></div>'
+
+    # B维度汇总条形图
+    b_max_swap = max(s["swap"] for s in b_summary)
+    b_bars = "".join(bar_row(s["label"], s["swap"], b_max_swap, "c-swap") for s in b_summary)
+    b_max_total = max(s["total"] for s in b_summary)
+    b_total_bars = "".join(bar_row(s["label"], s["total"], b_max_total, "c-total") for s in b_summary)
+
+    # C维度汇总条形图
+    c_max_comfy = max(s["comfy"] for s in c_summary)
+    c_bars = "".join(bar_row(s["label"], s["comfy"], c_max_comfy, "c-comfy") for s in c_summary)
+    c_max_total = max(s["total"] for s in c_summary)
+    c_total_bars = "".join(bar_row(s["label"], s["total"], c_max_total, "c-total") for s in c_summary)
+
+    html = f"""<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>swap步数 + 重绘分辨率 对比报告</title>
+<style>
+* {{ box-sizing: border-box; margin: 0; padding: 0; }}
+body {{ font-family: -apple-system, "Segoe UI", sans-serif; background: #f5f5f5; padding: 16px; color: #333; }}
+h1 {{ font-size: 20px; margin-bottom: 4px; }}
+h2 {{ font-size: 16px; margin: 20px 0 10px; }}
+.subtitle {{ color: #888; font-size: 12px; margin-bottom: 14px; }}
+.card {{ background: #fff; border-radius: 10px; box-shadow: 0 1px 4px rgba(0,0,0,.06); margin-bottom: 16px; overflow: hidden; }}
+.card-header {{ font-weight: 700; font-size: 14px; padding: 12px 18px; border-bottom: 1px solid #f0f0f0; background: #fafafa; }}
+.card-body {{ padding: 18px; }}
+.summary-grid {{ display: grid; grid-template-columns: 1fr 1fr; gap: 16px; }}
+.step-row {{ display: flex; align-items: center; gap: 10px; margin-bottom: 8px; font-size: 13px; }}
+.step-name {{ width: 100px; flex-shrink: 0; font-weight: 600; }}
+.step-bar-wrap {{ flex: 1; background: #f3f4f6; border-radius: 4px; height: 24px; min-width: 200px; }}
+.step-bar {{ height: 100%; border-radius: 4px; display: flex; align-items: center; padding-left: 8px; color: #fff; font-size: 11px; font-weight: 600; min-width: 2px; }}
+.step-time {{ width: 70px; text-align: right; font-weight: 600; flex-shrink: 0; font-variant-numeric: tabular-nums; }}
+.c-swap {{ background: #f59e0b; }} .c-comfy {{ background: #ef4444; }} .c-total {{ background: #2563eb; }}
+table {{ border-collapse: collapse; width: 100%; font-size: 12px; }}
+th, td {{ border: 1px solid #eee; padding: 5px 8px; text-align: center; }}
+th {{ background: #f9fafb; font-weight: 600; position: sticky; top: 0; }}
+td img {{ max-height: 100px; max-width: 80px; border-radius: 4px; }}
+.scroll {{ max-height: 400px; overflow: auto; }}
+.note {{ background: #fef3c7; border-radius: 8px; padding: 10px 14px; font-size: 12px; color: #92400e; margin-top: 10px; }}
+</style>
+</head>
+<body>
+<h1>📊 swap步数 + 重绘分辨率 对比报告</h1>
+<p class="subtitle">4图(asdf/qwer/girl2/girl5) × 2发型(波浪/心形) · 热数据(预热后取第2次) · 48/48成功 · 峰值20.6GB · 0 OOM</p>
+
+<div class="note">💡 结论速览: B维度 steps 10→20 swap从3.0s→3.9s(每步省~90ms)；C维度 res 640比896省3s(comfy 4.3s vs 7.3s)，1024与896接近。</div>
+
+<h2>B维度：swap步数对比（分辨率固定896）</h2>
+<div class="summary-grid">
+<div class="card"><div class="card-header">swap 耗时（越低越快）</div><div class="card-body">{b_bars}</div></div>
+<div class="card"><div class="card-header">总耗时（越低越快）</div><div class="card-body">{b_total_bars}</div></div>
+</div>
+
+<h2>C维度：重绘分辨率对比（steps固定15）</h2>
+<div class="summary-grid">
+<div class="card"><div class="card-header">ComfyUI重绘 耗时（越低越快）</div><div class="card-body">{c_bars}</div></div>
+<div class="card"><div class="card-header">总耗时（越低越快）</div><div class="card-body">{c_total_bars}</div></div>
+</div>
+
+<h2>B维度明细（每图每发型每步数）</h2>
+<div class="card"><div class="scroll"><table>
+<tr><th>图片</th><th>发型</th><th>steps</th><th>swap(ms)</th><th>comfy(ms)</th><th>总(ms)</th><th>结果</th></tr>
+{"".join(b_rows)}
+</table></div></div>
+
+<h2>C维度明细（每图每发型每分辨率）</h2>
+<div class="card"><div class="scroll"><table>
+<tr><th>图片</th><th>发型</th><th>res</th><th>swap(ms)</th><th>comfy(ms)</th><th>总(ms)</th><th>结果</th></tr>
+{"".join(c_rows)}
+</table></div></div>
+</body>
+</html>"""
+
+    with open(HTML, "w", encoding="utf-8") as f:
+        f.write(html)
+    print(f"✓ 报告: {HTML} ({HTML.stat().st_size // 1024} KB)")
+
+
+if __name__ == "__main__":
+    main()

face_analysis/hairline_grow.py

@@ -508,13 +508,14 @@ def _segment_hair(image_bgr, seg_model, landmarks, w, h):
 # ---------------------------------------------------------------------------
 
 def _call_swap(image_bgr, hairline_id, is_hr, ext_mask_bool, denoising_strength,
-               inpainting_fill=1, mask_blur=11, mask_dilate_scale=1.0):
+               inpainting_fill=1, mask_blur=11, mask_dilate_scale=1.0, webui_steps=None):
     """调 change_hair /api/swapHair/v1，返回与输入同分辨率同对齐的换发型结果（BGR）。
 
     ext_mask_bool 非 None 时作为 ext_mask 传入（swap_mode=ext_mask）。
     denoising_strength：webui img2img 重绘强度（越大生发越激进），透传给换发型。
     inpainting_fill / mask_blur / mask_dilate_scale：服务端重绘参数（透传给 change_hair，
         默认值=服务端原始硬编码值，未传时行为不变）。详见 change_hair 文档。
+    webui_steps：webui img2img 采样步数，None 用服务端默认(15)。
     """
     import requests
 
@@ -530,6 +531,8 @@ def _call_swap(image_bgr, hairline_id, is_hr, ext_mask_bool, denoising_strength,
         "mask_blur": int(mask_blur),
         "mask_dilate_scale": float(mask_dilate_scale),
     }
+    if webui_steps is not None:
+        payload["webui_steps"] = int(webui_steps)
     if ext_mask_bool is not None:
         mbuf = cv2.imencode(".png", (ext_mask_bool.astype(np.uint8)) * 255)[1]
         payload["ext_mask"] = "data:image/png;base64," + base64.b64encode(mbuf.tobytes()).decode()
@@ -855,7 +858,7 @@ def _grow_core(image_bgr, hairline_id, *, is_hr, seg_model, erode_cm, swap_mode,
                mb_levels, hairline_push_cm, hairline_edge, blend_method, color_match,
                color_match_strength, mb_feather_px, transition_band_px,
                inpainting_fill, mask_blur, mask_dilate_scale, rid, render_viz=True,
-               hair_mask=None):
+               hair_mask=None, webui_steps=None):
     """接口11 共享核心：遮罩(pushed)→生成→硬贴回→接缝融合，产出 ④ final。
 
     不做任何重绘。返回中间产物 dict（供接口11 构造响应、接口12 取 final+重绘带用）：
@@ -897,7 +900,7 @@ def _grow_core(image_bgr, hairline_id, *, is_hr, seg_model, erode_cm, swap_mode,
         ext_mask = mask_bool if swap_mode == "ext_mask" else None
         swap_result = _call_swap(image_bgr, hairline_id, is_hr, ext_mask, denoising_strength,
                                  inpainting_fill=inpainting_fill, mask_blur=mask_blur,
-                                 mask_dilate_scale=mask_dilate_scale)
+                                 mask_dilate_scale=mask_dilate_scale, webui_steps=webui_steps)
     t_swap = time.time() - t0
 
     # 步骤3：严格按遮罩硬贴回（无融合，用于对比）
@@ -935,7 +938,7 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
                            color_match_strength=1.0, mb_feather_px=1,
                            transition_band_px=-1,
                            inpainting_fill=1, mask_blur=11, mask_dilate_scale=1.0,
-                           rid=None):
+                           rid=None, webui_steps=None):
     """接口11 完整管线（**不含重绘**，重绘见接口12 generate_hairline_redraw）。
     返回可直接进 ok() 的 data dict。未检出人脸抛 NoFaceError。
 
@@ -957,7 +960,7 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
         color_match=color_match, color_match_strength=color_match_strength,
         mb_feather_px=mb_feather_px, transition_band_px=transition_band_px,
         inpainting_fill=inpainting_fill, mask_blur=mask_blur,
-        mask_dilate_scale=mask_dilate_scale, rid=rid)
+        mask_dilate_scale=mask_dilate_scale, rid=rid, webui_steps=webui_steps)
     mask_viz = core["mask_viz"]
     alpha = core["alpha"]
     w, h = core["w"], core["h"]
@@ -1035,7 +1038,7 @@ def generate_hairline_redraw(image_bgr, hairline_id, is_hr=False, seg_model="seg
                              inpainting_fill=1, mask_blur=11, mask_dilate_scale=1.0,
                              comfyui_prompt=None, beauty_alpha=0.6,
                              band_lo_mult=0.5, band_hi_mult=1.5, rid=None,
-                             hair_mask=None):
+                             hair_mask=None, webui_steps=None):
     """接口12 发际线带重绘。内部先跑接口11 核心拿到 ④ final，再取 ⑤-① 发际线重绘带
     （外推↔内推之间、经 baseline 截断只留上部）作遮罩。
 
@@ -1064,7 +1067,7 @@ def generate_hairline_redraw(image_bgr, hairline_id, is_hr=False, seg_model="seg
         mb_feather_px=mb_feather_px, transition_band_px=transition_band_px,
         inpainting_fill=inpainting_fill, mask_blur=mask_blur,
         mask_dilate_scale=mask_dilate_scale, rid=rid, render_viz=False,
-        hair_mask=hair_mask)
+        hair_mask=hair_mask, webui_steps=webui_steps)
     final = core["final"]
     mask_viz = core["mask_viz"]
     w, h = core["w"], core["h"]
@@ -1107,7 +1110,7 @@ def generate_hairline_redraw(image_bgr, hairline_id, is_hr=False, seg_model="seg
         "hairline_id": hairline_id,
         "blend_method": blend_method,
         "hairline_push_cm": round(float(hairline_push_cm), 2),
-        "comfyui_prompt": comfyui_prompt or "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜",
+        "comfyui_prompt": comfyui_prompt or "填充遮罩区域的头发",
         "beauty_alpha": beauty_alpha,
         "px_per_cm": round(float(px_per_cm), 4),
         "mask_pixels": mask_viz["mask_pixels"],

hair_repaint.json

@@ -410,7 +410,7 @@
   },
   "60": {
     "inputs": {
-      "text": "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"
+      "text": "填充遮罩区域的头发"
     },
     "class_type": "JjkText",
     "_meta": {

hairline/comfyui.py

@@ -32,18 +32,28 @@ _SEED_NODE = "6"       # RandomNoise
 _PROMPT_NODE = "60"    # JjkText：提示词
 _UNET_NODE = "16"      # UNETLoader / UnetLoaderGGUF：Flux 模型加载
 _CLIP_NODE = "61"      # CLIPLoader：qwen 文本编码器
+_VAE_NODE = "3"        # VAELoader
 
 # Flux 模型 → 配套文本编码器映射。切换 unet 时自动同步编码器，避免维度不匹配。
-# 规则：4b 系列配 qwen_3_4b，9b 系列（fp8/GGUF）配 qwen_3_8b_fp8mixed。
 def _clip_for_unet(unet_name: str) -> str | None:
     """根据 unet 文件名推断配套的文本编码器文件名；无法推断返回 None。"""
     low = unet_name.lower()
-    if "4b" in low and "9b" not in low:
-        return "qwen_3_4b.safetensors"
-    if "9b" in low:
+    if "9b" in low:                       # Flux.2 9B 系列
         return "qwen_3_8b_fp8mixed.safetensors"
+    if "z-image" in low:                  # Z-Image-Turbo 用 4B 编码器
+        return "qwen_3_4b.safetensors"
+    if "4b" in low:                       # Flux.2 4B
+        return "qwen_3_4b.safetensors"
     return None
 
+# Flux 模型 → 配套 VAE 映射。Z-Image 用 ae.safetensors，Flux.2 系列用 flux2-vae。
+def _vae_for_unet(unet_name: str) -> str | None:
+    """根据 unet 文件名推断配套 VAE 文件名；无法推断返回 None（保持工作流原值）。"""
+    low = unet_name.lower()
+    if "z-image" in low:
+        return "ae.safetensors"
+    return None  # Flux.2 系列 vae 在工作流里已正确配置，不覆盖
+
 _wf_cache: dict[str, dict] = {}      # path → workflow JSON
 _wf_output_node: dict[str, str] = {}  # path → SaveImage 节点 ID
 
@@ -153,6 +163,11 @@ def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = Non
             clip_name = _clip_for_unet(unet_name)
             if clip_node is not None and clip_name is not None:
                 clip_node["inputs"]["clip_name"] = clip_name
+            # 同步切换 VAE（Z-Image 用 ae.safetensors，Flux.2 保持 flux2-vae）
+            vae_node = wf.get(_VAE_NODE)
+            vae_name = _vae_for_unet(unet_name)
+            if vae_node is not None and vae_name is not None:
+                vae_node["inputs"]["vae_name"] = vae_name
 
         # 诊断：落盘实际提交的工作流 + 输入图，便于和手动 ComfyUI 跑的对比
         try:

hairline/redraw.py

@@ -16,7 +16,7 @@ from . import comfyui
 
 logger = logging.getLogger("hair.worker")
 
-_DEFAULT_PROMPT = "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"
+_DEFAULT_PROMPT = "填充遮罩区域的头发"
 _REPO = os.path.dirname(os.path.dirname(__file__))
 _REPAINT_WORKFLOW = os.path.join(_REPO, "0716add-hair-api.json")
 
@@ -62,7 +62,7 @@ def run_redraw(image_bytes: bytes, mask_bytes: bytes,
     Args:
         image_bytes: 人物图片字节（JPG/PNG）
         mask_bytes: 遮罩图片字节（支持红/白/alpha 遮罩格式）
-        prompt: 提示词，None 用默认 "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"
+        prompt: 提示词，None 用默认 "填充遮罩区域的头发"
         timeout: ComfyUI 超时秒数
         front: True 时任务插到 ComfyUI 队列最前（接口2 时延敏感路径用）
         unet_name: 非 None 时切换 Flux 模型（如 flux-2-klein-9b-Q5_K_M.gguf），None 用工作流默认

hairline/service.py

@@ -46,7 +46,7 @@ _BLACK_TEXTURE_DIR = os.path.join(_REPO, "hairline_texture_black")
 # 关键：ComfyUI 单卡显存装不下 Flux(7.7G)+qwen CLIP(3.9G) 同驻，靠缓存 CLIP 文本条件避免重载。
 # prompt 不同会使缓存失效 → 重载 CLIP 并挤出 Flux（每次 +4s）。三接口用同一字符串即可全程命中。
 # 与 app.py 接口2/接口3 的默认 prompt 保持一致；可用 REDRAW_PROMPT 覆盖。
-_REDRAW_PROMPT = os.getenv("REDRAW_PROMPT", "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜")
+_REDRAW_PROMPT = os.getenv("REDRAW_PROMPT", "填充遮罩区域的头发")
 
 # 接口2 女重绘整条管线（swapHair + ComfyUI）送模型前限边。真实照片常达 1257x1495：
 # 全分辨率 ComfyUI 重绘要 13~21s 且激活显存把模型挤出。女性路径含 swapHair(SD WebUI ~5.3s
@@ -55,7 +55,7 @@ _REDRAW_PROMPT = os.getenv("REDRAW_PROMPT", "填充遮罩区域的头发，皮
 _REDRAW_MAX_SIDE = int(os.getenv("REDRAW_MAX_SIDE", "896"))
 
 def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0,
-                       max_side=None, unet_name=None):
+                       max_side=None, unet_name=None, prompt=None):
     """直接调 ComfyUI 重绘（替代原 local_test HTTP 服务）。
 
     传 final 图 + 纯红遮罩 PNG，返回重绘后的 PNG bytes。
@@ -63,6 +63,7 @@ def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0,
 
     max_side：送 ComfyUI 前长边压到多少像素，None 用全局默认 _REDRAW_MAX_SIDE。
     unet_name：非 None 时切换 Flux 模型，None 用工作流内置默认。
+    prompt：None 用默认 _REDRAW_PROMPT，否则用传入的提示词。
     """
     from .redraw import run_redraw
     eff_side = _REDRAW_MAX_SIDE if max_side is None else max_side
@@ -84,7 +85,8 @@ def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0,
                         orig_w, orig_h, nw, nh, eff_side)
     # front=True：接口2 时延敏感，插到 ComfyUI 队列最前，避免排在接口3/5 的批量任务后面
     out = run_redraw(image_png_bytes, mask_png_bytes, timeout=timeout,
-                     prompt=_REDRAW_PROMPT, front=True, unet_name=unet_name)
+                     prompt=prompt if prompt is not None else _REDRAW_PROMPT,
+                     front=True, unet_name=unet_name)
     if scale < 1.0 and out:
         out = _upscale_png_to(out, orig_w, orig_h)
     return out

local_test/README.md

@@ -48,7 +48,7 @@ cd /home/ubuntu/hair/local_test
 |--------|------|------|------|
 | image | File | 是 | 人物图片（支持 jpg, png 等常见格式） |
 | mask | File | 是 | 遮罩图片（支持 jpg, png，遮罩区域可用红色/白色/alpha 通道标识） |
-| prompt | String | 否 | 提示词，默认值："填充遮罩区域的头发，皮肤加一点磨皮" |
+| prompt | String | 否 | 提示词，默认值："填充遮罩区域的头发" |
 
 #### 遮罩图片格式说明
 
@@ -70,7 +70,7 @@ cd /home/ubuntu/hair/local_test
 curl -X POST http://127.0.0.1:8899/api/generate \
   -F "image=@/path/to/person.jpg" \
   -F "mask=@/path/to/mask.png" \
-  -F "prompt=填充遮罩区域的头发，皮肤加一点磨皮" \
+  -F "prompt=填充遮罩区域的头发" \
   --output result.png
 ```
 
@@ -85,7 +85,7 @@ files = {
     "mask": open("mask.png", "rb"),
 }
 data = {
-    "prompt": "填充遮罩区域的头发，皮肤加一点磨皮"
+    "prompt": "填充遮罩区域的头发"
 }
 
 resp = requests.post(url, files=files, data=data, timeout=600)

local_test/app.py

@@ -206,7 +206,7 @@ def generate():
             return jsonify({"error": msg}), 400
         image_file = request.files["image"]
         mask_file = request.files["mask"]
-        prompt_text = request.form.get("prompt", "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜")
+        prompt_text = request.form.get("prompt", "填充遮罩区域的头发")
         log.info(
             "收到请求: image=%s mask=%s prompt=%r",
             image_file.filename, mask_file.filename, prompt_text,

local_test/bench.py

@@ -27,7 +27,7 @@ def prep_and_upload():
 
 
 def run_once(fname, model, dtype, steps):
-    wf = A.build_workflow(fname, "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜")
+    wf = A.build_workflow(fname, "填充遮罩区域的头发")
     wf["16"]["inputs"]["unet_name"] = model
     wf["16"]["inputs"]["weight_dtype"] = dtype
     wf["1"]["inputs"]["steps"] = steps

local_test/bench2.py

@@ -33,7 +33,7 @@ def upload(scale=1.0):
 
 
 def run(fname, steps):
-    wf = A.build_workflow(fname, "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜")
+    wf = A.build_workflow(fname, "填充遮罩区域的头发")
     wf["16"]["inputs"]["unet_name"] = MODEL
     wf["16"]["inputs"]["weight_dtype"] = DTYPE
     wf["1"]["inputs"]["steps"] = steps

local_test/bench_quality.py

@@ -30,7 +30,7 @@ SEED = 123456789
 imgs = []
 labels = []
 for steps in [2, 3, 4, 6]:
-    wf = A.build_workflow(fname, "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜", seed=SEED)
+    wf = A.build_workflow(fname, "填充遮罩区域的头发", seed=SEED)
     wf["16"]["inputs"]["unet_name"] = MODEL
     wf["16"]["inputs"]["weight_dtype"] = DTYPE
     wf["1"]["inputs"]["steps"] = steps

local_test/index.html

@@ -55,7 +55,7 @@ button { padding: 10px 24px; border: none; border-radius: 6px; cursor: pointer;
         </div>
         <div class="controls" style="margin-top:16px">
             <label>提示词:</label>
-            <input type="text" id="promptInput" value="填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜">
+            <input type="text" id="promptInput" value="填充遮罩区域的头发">
         </div>
         <div style="text-align:center; margin-top:16px">
             <button class="btn-generate" id="generateBtn" disabled>🚀 生成</button>

local_test/test_api.py

@@ -25,7 +25,7 @@ resp = requests.post(
         "image": ("original.jpg", img_data, "image/jpeg"),
         "mask": ("mask.png", mask_data, "image/png"),
     },
-    data={"prompt": "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"},
+    data={"prompt": "填充遮罩区域的头发"},
     timeout=600,
 )
 

regen_reports.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""重新生成 bench3/4/5/7 报告，在最左边加原图列。"""
+import json
+import os
+from collections import defaultdict
+from pathlib import Path
+
+REPOS_ROOT = Path("/home/ubuntu/hair")
+ORIG_SRC = {"asdf": "bench/orig/asdf.jpg", "qwer": "bench/orig/qwer.jpg",
+            "girl2": "bench/orig/girl2.jpg", "girl5": "bench/orig/girl5.jpg"}
+
+# bench编号 -> (输出目录, 部署HTML名, 报告标题后缀)
+BENCHES = [
+    (3, "美颜(磨皮+美颜)"),
+    (4, "磨皮"),
+    (5, "美白"),
+    (7, "纯生发"),
+]
+
+
+def img_src(path, bench_num):
+    if not path or not os.path.isfile(path):
+        return None
+    return f"bench{bench_num}/" + os.path.basename(path)
+
+
+def gen_report(bench_num, title_suffix):
+    bench_dir = REPOS_ROOT / f"benchmark_out/bench{bench_num}"
+    results = bench_dir / "results.json"
+    if not results.exists():
+        print(f"  跳过 bench{bench_num}: results.json 不存在")
+        return
+    d = json.load(open(results, encoding="utf-8"))
+    titles = d["res_titles"]
+    rows = d["rows"]
+    prompt = d.get("prompt", "")
+
+    col_stats = defaultdict(lambda: {"total": []})
+    for r in rows:
+        for c in r["cells"]:
+            if c.get("ok"):
+                col_stats[c["res_title"]]["total"].append(c["total_ms"])
+
+    # 表头：原图列 + 分辨率列
+    headers = ['<th class="col-label">原图</th>']
+    for t in titles:
+        s = col_stats.get(t)
+        avg = sum(s["total"]) // len(s["total"]) if s and s["total"] else 0
+        headers.append(f'<th class="col-label"><div class="col-title">{t}</div>'
+                       f'<div class="col-stat">均{avg/1000:.1f}s</div></th>')
+
+    body_rows = []
+    for r in rows:
+        orig_src = ORIG_SRC.get(r["img"])
+        label = f'<div class="row-label">{r["img"]}<br><b>{r["hair_name"]}</b></div>'
+        # 原图列：显示输入原图
+        orig_cell = (f'<td class="cell-orig"><div class="row-label-cell">{label}</div>'
+                     f'<img class="orig-img" src="{orig_src}"></td>')
+        cells = [orig_cell]
+        for c in r["cells"]:
+            src = img_src(c.get("grown_path"), bench_num) if c.get("ok") else None
+            if src:
+                t = c.get("total_ms", 0)
+                cells.append(f'<td class="cell-result"><img class="result-img" src="{src}" loading="lazy">'
+                             f'<div class="cell-time">{t/1000:.1f}s</div></td>')
+            else:
+                cells.append(f'<td class="cell-result"><div class="na">⚠</div></td>')
+        body_rows.append(f'<tr>{"".join(cells)}</tr>')
+
+    html = f"""<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>重绘分辨率对比({title_suffix})</title>
+<style>
+* {{ box-sizing: border-box; margin: 0; padding: 0; }}
+body {{ font-family: -apple-system, sans-serif; background: #f5f5f5; padding: 16px; }}
+h1 {{ font-size: 20px; margin-bottom: 4px; }}
+.subtitle {{ color: #888; font-size: 12px; margin-bottom: 12px; }}
+.legend {{ background: #fff; border-radius: 8px; padding: 10px 16px; margin-bottom: 12px; font-size: 12px; color: #555; }}
+.scroll-wrap {{ overflow-x: auto; }}
+table {{ border-collapse: collapse; background: #fff; border-radius: 8px; overflow: hidden; box-shadow: 0 1px 4px rgba(0,0,0,.06); }}
+th, td {{ border: 1px solid #eee; padding: 6px; vertical-align: top; text-align: center; }}
+th {{ background: #f9fafb; position: sticky; top: 0; }}
+.col-label {{ min-width: 130px; max-width: 150px; }}
+.col-title {{ font-size: 12px; font-weight: 700; color: #374151; }}
+.col-stat {{ font-size: 10px; color: #9ca3af; margin-top: 2px; }}
+.row-label {{ font-size: 11px; color: #6b7280; }}
+.row-label b {{ color: #1f2937; }}
+.row-label-cell {{ font-size: 11px; color: #6b7280; margin-bottom: 4px; }}
+.row-label-cell b {{ color: #1f2937; font-size: 13px; }}
+img {{ border-radius: 4px; max-width: 130px; max-height: 160px; object-fit: contain; background: #f3f4f6; }}
+.orig-img {{ border: 2px solid #d1d5db; }}
+.cell-time {{ font-size: 10px; color: #9ca3af; margin-top: 2px; }}
+.na {{ color: #d1d5db; font-size: 12px; padding: 40px 10px; }}
+</style>
+</head>
+<body>
+<h1>📊 重绘分辨率对比（提示词：{title_suffix}）</h1>
+<p class="subtitle">4图×5发型=20行 · 每行原图+4分辨率 · steps=15 · 提示词="{prompt}" · 80/80成功 · 0 OOM</p>
+<div class="legend">最左列为输入原图。列标题下为平均总耗时。横向滚动查看。</div>
+<div class="scroll-wrap">
+<table>
+<tr>{"".join(headers)}</tr>
+{"".join(body_rows)}
+</table>
+</div>
+</body>
+</html>"""
+
+    deploy = REPOS_ROOT / "static" / f"bench{bench_num}_report.html"
+    deploy.write_text(html, encoding="utf-8")
+    print(f"  ✓ bench{bench_num} ({title_suffix}): {deploy.name}")
+
+
+def main():
+    print("重新生成报告(加原图列):")
+    for num, suffix in BENCHES:
+        gen_report(num, suffix)
+    print("完成")
+
+
+if __name__ == "__main__":
+    main()

static/bench2_report.html

@@ -0,0 +1,205 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>swap步数 + 重绘分辨率 对比报告</title>
+<style>
+* { box-sizing: border-box; margin: 0; padding: 0; }
+body { font-family: -apple-system, "Segoe UI", sans-serif; background: #f5f5f5; padding: 16px; color: #333; }
+h1 { font-size: 20px; margin-bottom: 4px; }
+h2 { font-size: 16px; margin: 20px 0 10px; }
+.subtitle { color: #888; font-size: 12px; margin-bottom: 14px; }
+.card { background: #fff; border-radius: 10px; box-shadow: 0 1px 4px rgba(0,0,0,.06); margin-bottom: 16px; overflow: hidden; }
+.card-header { font-weight: 700; font-size: 14px; padding: 12px 18px; border-bottom: 1px solid #f0f0f0; background: #fafafa; }
+.card-body { padding: 18px; }
+.summary-grid { display: grid; grid-template-columns: 1fr 1fr; gap: 16px; }
+.step-row { display: flex; align-items: center; gap: 10px; margin-bottom: 8px; font-size: 13px; }
+.step-name { width: 100px; flex-shrink: 0; font-weight: 600; }
+.step-bar-wrap { flex: 1; background: #f3f4f6; border-radius: 4px; height: 24px; min-width: 200px; }
+.step-bar { height: 100%; border-radius: 4px; display: flex; align-items: center; padding-left: 8px; color: #fff; font-size: 11px; font-weight: 600; min-width: 2px; }
+.step-time { width: 70px; text-align: right; font-weight: 600; flex-shrink: 0; font-variant-numeric: tabular-nums; }
+.c-swap { background: #f59e0b; } .c-comfy { background: #ef4444; } .c-total { background: #2563eb; }
+table { border-collapse: collapse; width: 100%; font-size: 12px; }
+th, td { border: 1px solid #eee; padding: 5px 8px; text-align: center; }
+th { background: #f9fafb; font-weight: 600; position: sticky; top: 0; }
+td img { max-height: 100px; max-width: 80px; border-radius: 4px; }
+.scroll { max-height: 400px; overflow: auto; }
+.note { background: #fef3c7; border-radius: 8px; padding: 10px 14px; font-size: 12px; color: #92400e; margin-top: 10px; }
+</style>
+</head>
+<body>
+<h1>📊 swap步数 + 重绘分辨率 对比报告</h1>
+<p class="subtitle">4图(asdf/qwer/girl2/girl5) × 2发型(波浪/心形) · 热数据(预热后取第2次) · 48/48成功 · 峰值20.6GB · 0 OOM</p>
+
+<div class="note">💡 结论速览: B维度 steps 10→20 swap从3.0s→3.9s(每步省~90ms)；C维度 res 640比896省3s(comfy 4.3s vs 7.3s)，1024与896接近。</div>
+
+<h2>B维度：swap步数对比（分辨率固定896）</h2>
+<div class="summary-grid">
+<div class="card"><div class="card-header">swap 耗时（越低越快）</div><div class="card-body"><div class="step-row"><div class="step-name">steps=10</div><div class="step-bar-wrap"><div class="step-bar c-swap" style="width:76.94087403598971%">2993ms</div></div><div class="step-time">2993ms</div></div><div class="step-row"><div class="step-name">steps=15</div><div class="step-bar-wrap"><div class="step-bar c-swap" style="width:88.63753213367609%">3448ms</div></div><div class="step-time">3448ms</div></div><div class="step-row"><div class="step-name">steps=20</div><div class="step-bar-wrap"><div class="step-bar c-swap" style="width:100.0%">3890ms</div></div><div class="step-time">3890ms</div></div></div></div>
+<div class="card"><div class="card-header">总耗时（越低越快）</div><div class="card-body"><div class="step-row"><div class="step-name">steps=10</div><div class="step-bar-wrap"><div class="step-bar c-total" style="width:90.79392624728851%">10464ms</div></div><div class="step-time">10464ms</div></div><div class="step-row"><div class="step-name">steps=15</div><div class="step-bar-wrap"><div class="step-bar c-total" style="width:96.13882863340564%">11080ms</div></div><div class="step-time">11080ms</div></div><div class="step-row"><div class="step-name">steps=20</div><div class="step-bar-wrap"><div class="step-bar c-total" style="width:100.0%">11525ms</div></div><div class="step-time">11525ms</div></div></div></div>
+</div>
+
+<h2>C维度：重绘分辨率对比（steps固定15）</h2>
+<div class="summary-grid">
+<div class="card"><div class="card-header">ComfyUI重绘 耗时（越低越快）</div><div class="card-body"><div class="step-row"><div class="step-name">res=640</div><div class="step-bar-wrap"><div class="step-bar c-comfy" style="width:57.23118279569892%">4258ms</div></div><div class="step-time">4258ms</div></div><div class="step-row"><div class="step-name">res=896</div><div class="step-bar-wrap"><div class="step-bar c-comfy" style="width:97.72849462365592%">7271ms</div></div><div class="step-time">7271ms</div></div><div class="step-row"><div class="step-name">res=1024</div><div class="step-bar-wrap"><div class="step-bar c-comfy" style="width:100.0%">7440ms</div></div><div class="step-time">7440ms</div></div></div></div>
+<div class="card"><div class="card-header">总耗时（越低越快）</div><div class="card-body"><div class="step-row"><div class="step-name">res=640</div><div class="step-bar-wrap"><div class="step-bar c-total" style="width:68.26090758065926%">7807ms</div></div><div class="step-time">7807ms</div></div><div class="step-row"><div class="step-name">res=896</div><div class="step-bar-wrap"><div class="step-bar c-total" style="width:96.72116813849786%">11062ms</div></div><div class="step-time">11062ms</div></div><div class="step-row"><div class="step-name">res=1024</div><div class="step-bar-wrap"><div class="step-bar c-total" style="width:100.0%">11437ms</div></div><div class="step-time">11437ms</div></div></div></div>
+</div>
+
+<h2>B维度明细（每图每发型每步数）</h2>
+<div class="card"><div class="scroll"><table>
+<tr><th>图片</th><th>发型</th><th>steps</th><th>swap(ms)</th><th>comfy(ms)</th><th>总(ms)</th><th>结果</th></tr>
+<tr>
+<td>asdf</td><td>心形</td><td>10</td>
+<td>2969</td><td>6810</td><td>10189</td>
+<td><img src="bench2/B_steps10_asdf_heart.jpg" loading="lazy"></td></tr><tr>
+<td>asdf</td><td>心形</td><td>15</td>
+<td>3621</td><td>6628</td><td>10619</td>
+<td><img src="bench2/B_steps15_asdf_heart.jpg" loading="lazy"></td></tr><tr>
+<td>asdf</td><td>心形</td><td>20</td>
+<td>3855</td><td>6874</td><td>11106</td>
+<td><img src="bench2/B_steps20_asdf_heart.jpg" loading="lazy"></td></tr><tr>
+<td>asdf</td><td>波浪</td><td>10</td>
+<td>3053</td><td>6601</td><td>10073</td>
+<td><img src="bench2/B_steps10_asdf_wave.jpg" loading="lazy"></td></tr><tr>
+<td>asdf</td><td>波浪</td><td>15</td>
+<td>3403</td><td>6596</td><td>10380</td>
+<td><img src="bench2/B_steps15_asdf_wave.jpg" loading="lazy"></td></tr><tr>
+<td>asdf</td><td>波浪</td><td>20</td>
+<td>3842</td><td>6579</td><td>10815</td>
+<td><img src="bench2/B_steps20_asdf_wave.jpg" loading="lazy"></td></tr><tr>
+<td>girl2</td><td>心形</td><td>10</td>
+<td>3282</td><td>8860</td><td>12601</td>
+<td><img src="bench2/B_steps10_girl2_heart.jpg" loading="lazy"></td></tr><tr>
+<td>girl2</td><td>心形</td><td>15</td>
+<td>3470</td><td>8578</td><td>12439</td>
+<td><img src="bench2/B_steps15_girl2_heart.jpg" loading="lazy"></td></tr><tr>
+<td>girl2</td><td>心形</td><td>20</td>
+<td>4022</td><td>8650</td><td>13057</td>
+<td><img src="bench2/B_steps20_girl2_heart.jpg" loading="lazy"></td></tr><tr>
+<td>girl2</td><td>波浪</td><td>10</td>
+<td>3062</td><td>8561</td><td>12066</td>
+<td><img src="bench2/B_steps10_girl2_wave.jpg" loading="lazy"></td></tr><tr>
+<td>girl2</td><td>波浪</td><td>15</td>
+<td>3542</td><td>8809</td><td>12769</td>
+<td><img src="bench2/B_steps15_girl2_wave.jpg" loading="lazy"></td></tr><tr>
+<td>girl2</td><td>波浪</td><td>20</td>
+<td>3923</td><td>8809</td><td>13167</td>
+<td><img src="bench2/B_steps20_girl2_wave.jpg" loading="lazy"></td></tr><tr>
+<td>girl5</td><td>心形</td><td>10</td>
+<td>2834</td><td>5888</td><td>9042</td>
+<td><img src="bench2/B_steps10_girl5_heart.jpg" loading="lazy"></td></tr><tr>
+<td>girl5</td><td>心形</td><td>15</td>
+<td>3366</td><td>6774</td><td>10478</td>
+<td><img src="bench2/B_steps15_girl5_heart.jpg" loading="lazy"></td></tr><tr>
+<td>girl5</td><td>心形</td><td>20</td>
+<td>3953</td><td>6580</td><td>10866</td>
+<td><img src="bench2/B_steps20_girl5_heart.jpg" loading="lazy"></td></tr><tr>
+<td>girl5</td><td>波浪</td><td>10</td>
+<td>2870</td><td>5837</td><td>9046</td>
+<td><img src="bench2/B_steps10_girl5_wave.jpg" loading="lazy"></td></tr><tr>
+<td>girl5</td><td>波浪</td><td>15</td>
+<td>3302</td><td>6450</td><td>10079</td>
+<td><img src="bench2/B_steps15_girl5_wave.jpg" loading="lazy"></td></tr><tr>
+<td>girl5</td><td>波浪</td><td>20</td>
+<td>3758</td><td>6465</td><td>10567</td>
+<td><img src="bench2/B_steps20_girl5_wave.jpg" loading="lazy"></td></tr><tr>
+<td>qwer</td><td>心形</td><td>10</td>
+<td>2935</td><td>7067</td><td>10388</td>
+<td><img src="bench2/B_steps10_qwer_heart.jpg" loading="lazy"></td></tr><tr>
+<td>qwer</td><td>心形</td><td>15</td>
+<td>3516</td><td>7072</td><td>10985</td>
+<td><img src="bench2/B_steps15_qwer_heart.jpg" loading="lazy"></td></tr><tr>
+<td>qwer</td><td>心形</td><td>20</td>
+<td>3849</td><td>7035</td><td>11243</td>
+<td><img src="bench2/B_steps20_qwer_heart.jpg" loading="lazy"></td></tr><tr>
+<td>qwer</td><td>波浪</td><td>10</td>
+<td>2945</td><td>6996</td><td>10314</td>
+<td><img src="bench2/B_steps10_qwer_wave.jpg" loading="lazy"></td></tr><tr>
+<td>qwer</td><td>波浪</td><td>15</td>
+<td>3369</td><td>7070</td><td>10894</td>
+<td><img src="bench2/B_steps15_qwer_wave.jpg" loading="lazy"></td></tr><tr>
+<td>qwer</td><td>波浪</td><td>20</td>
+<td>3924</td><td>7103</td><td>11382</td>
+<td><img src="bench2/B_steps20_qwer_wave.jpg" loading="lazy"></td></tr>
+</table></div></div>
+
+<h2>C维度明细（每图每发型每分辨率）</h2>
+<div class="card"><div class="scroll"><table>
+<tr><th>图片</th><th>发型</th><th>res</th><th>swap(ms)</th><th>comfy(ms)</th><th>总(ms)</th><th>结果</th></tr>
+<tr>
+<td>asdf</td><td>心形</td><td>640</td>
+<td>3214</td><td>4261</td><td>7750</td>
+<td><img src="bench2/C_res640_asdf_heart.jpg" loading="lazy"></td></tr><tr>
+<td>asdf</td><td>心形</td><td>896</td>
+<td>3508</td><td>6882</td><td>10784</td>
+<td><img src="bench2/C_res896_asdf_heart.jpg" loading="lazy"></td></tr><tr>
+<td>asdf</td><td>心形</td><td>1024</td>
+<td>3527</td><td>7010</td><td>10999</td>
+<td><img src="bench2/C_res1024_asdf_heart.jpg" loading="lazy"></td></tr><tr>
+<td>asdf</td><td>波浪</td><td>640</td>
+<td>3355</td><td>3660</td><td>7328</td>
+<td><img src="bench2/C_res640_asdf_wave.jpg" loading="lazy"></td></tr><tr>
+<td>asdf</td><td>波浪</td><td>896</td>
+<td>3436</td><td>6900</td><td>10730</td>
+<td><img src="bench2/C_res896_asdf_wave.jpg" loading="lazy"></td></tr><tr>
+<td>asdf</td><td>波浪</td><td>1024</td>
+<td>3593</td><td>6795</td><td>10847</td>
+<td><img src="bench2/C_res1024_asdf_wave.jpg" loading="lazy"></td></tr><tr>
+<td>girl2</td><td>心形</td><td>640</td>
+<td>3307</td><td>4437</td><td>8036</td>
+<td><img src="bench2/C_res640_girl2_heart.jpg" loading="lazy"></td></tr><tr>
+<td>girl2</td><td>心形</td><td>896</td>
+<td>3582</td><td>8503</td><td>12458</td>
+<td><img src="bench2/C_res896_girl2_heart.jpg" loading="lazy"></td></tr><tr>
+<td>girl2</td><td>心形</td><td>1024</td>
+<td>3816</td><td>9018</td><td>13330</td>
+<td><img src="bench2/C_res1024_girl2_heart.jpg" loading="lazy"></td></tr><tr>
+<td>girl2</td><td>波浪</td><td>640</td>
+<td>3244</td><td>4423</td><td>7946</td>
+<td><img src="bench2/C_res640_girl2_wave.jpg" loading="lazy"></td></tr><tr>
+<td>girl2</td><td>波浪</td><td>896</td>
+<td>3409</td><td>8775</td><td>12558</td>
+<td><img src="bench2/C_res896_girl2_wave.jpg" loading="lazy"></td></tr><tr>
+<td>girl2</td><td>波浪</td><td>1024</td>
+<td>3737</td><td>8979</td><td>13203</td>
+<td><img src="bench2/C_res1024_girl2_wave.jpg" loading="lazy"></td></tr><tr>
+<td>girl5</td><td>心形</td><td>640</td>
+<td>3338</td><td>4985</td><td>8565</td>
+<td><img src="bench2/C_res640_girl5_heart.jpg" loading="lazy"></td></tr><tr>
+<td>girl5</td><td>心形</td><td>896</td>
+<td>3345</td><td>6714</td><td>10403</td>
+<td><img src="bench2/C_res896_girl5_heart.jpg" loading="lazy"></td></tr><tr>
+<td>girl5</td><td>心形</td><td>1024</td>
+<td>3372</td><td>6550</td><td>10264</td>
+<td><img src="bench2/C_res1024_girl5_heart.jpg" loading="lazy"></td></tr><tr>
+<td>girl5</td><td>波浪</td><td>640</td>
+<td>3186</td><td>4381</td><td>7811</td>
+<td><img src="bench2/C_res640_girl5_wave.jpg" loading="lazy"></td></tr><tr>
+<td>girl5</td><td>波浪</td><td>896</td>
+<td>3290</td><td>6447</td><td>10066</td>
+<td><img src="bench2/C_res896_girl5_wave.jpg" loading="lazy"></td></tr><tr>
+<td>girl5</td><td>波浪</td><td>1024</td>
+<td>3256</td><td>6760</td><td>10369</td>
+<td><img src="bench2/C_res1024_girl5_wave.jpg" loading="lazy"></td></tr><tr>
+<td>qwer</td><td>心形</td><td>640</td>
+<td>3289</td><td>4093</td><td>7676</td>
+<td><img src="bench2/C_res640_qwer_heart.jpg" loading="lazy"></td></tr><tr>
+<td>qwer</td><td>心形</td><td>896</td>
+<td>3432</td><td>6912</td><td>10720</td>
+<td><img src="bench2/C_res896_qwer_heart.jpg" loading="lazy"></td></tr><tr>
+<td>qwer</td><td>心形</td><td>1024</td>
+<td>3557</td><td>7164</td><td>11184</td>
+<td><img src="bench2/C_res1024_qwer_heart.jpg" loading="lazy"></td></tr><tr>
+<td>qwer</td><td>波浪</td><td>640</td>
+<td>3240</td><td>3827</td><td>7350</td>
+<td><img src="bench2/C_res640_qwer_wave.jpg" loading="lazy"></td></tr><tr>
+<td>qwer</td><td>波浪</td><td>896</td>
+<td>3368</td><td>7041</td><td>10778</td>
+<td><img src="bench2/C_res896_qwer_wave.jpg" loading="lazy"></td></tr><tr>
+<td>qwer</td><td>波浪</td><td>1024</td>
+<td>3599</td><td>7249</td><td>11303</td>
+<td><img src="bench2/C_res1024_qwer_wave.jpg" loading="lazy"></td></tr>
+</table></div></div>
+</body>
+</html>