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

.gitignore

@@ -65,16 +65,3 @@ gateway.log
 
 # 工作流备份文件（不入 git）
 *.json.bak.*
-
-# 分辨率对比测试产物（out/ 下结果图+原图副本+日志，体积大，不入 git）
-# 仅忽略 out/ 与运行期文件；测试脚本与 HTML 报告仍入库
-image/compare_test/out/
-image/res_test/out/
-image/wave_test/out/
-image/compare_test/progress.json
-image/res_test/progress.json
-image/wave_test/progress.json
-image/compare_test/batch_test.log
-image/res_test/batch_test.log
-image/wave_test/batch_test.log
-image/compare_test/http.log

app.py

@@ -401,36 +401,21 @@ def _run_face_measure_data(image, variant="v1"):
         logger.warning("头发/耳朵分割失败，回退方案A：%s", seg_e)
 
     result = measure_face(landmarks, hair_mask, w, h, head_pose=head_pose)
-    discarded = result.hairline_discarded
     data = result.to_response()
-    vd = result.vertical
     if variant == "v6":
-        if discarded:
-            # 发际线弃用：接口6 的上庭也依赖发际线，一并置 null；只保留中/下庭。
-            base_px = vd["middle_court_px"] + vd["lower_court_px"]
-            data["four_courts"]["upper_court_cm"] = None
-            data["four_courts"]["ratios"] = {
-                "upper_court": None,
-                "middle_court": round(vd["middle_court_px"] / base_px, 3),
-                "lower_court": round(vd["lower_court_px"] / base_px, 3),
-            }
-            data["four_courts"].pop("top_court_cm", None)
-            data["face_total_height_cm"] = round(
-                result.middle_cm + result.lower_cm, 2)
-            data["landmarks"]["hairline"] = None
-        else:
-            base_px = vd["upper_court_px"] + vd["middle_court_px"] + vd["lower_court_px"]
-            # 接口6 是三庭：去掉顶庭相关字段（top_court_cm / ratios.top_court / landmarks.hair_top）
-            data["four_courts"]["ratios"] = {
-                "upper_court": round(vd["upper_court_px"] / base_px, 3),
-                "middle_court": round(vd["middle_court_px"] / base_px, 3),
-                "lower_court": round(vd["lower_court_px"] / base_px, 3),
-            }
-            data["four_courts"].pop("top_court_cm", None)
-            data["face_total_height_cm"] = round(
-                result.upper_cm + result.middle_cm + result.lower_cm, 2)
-            # 注：landmarks.hair_top 保留返回（供前端/下游定位头顶），但顶庭数值、
-            # 占比、标注图仍按三庭处理，显示效果不变。
+        vd = result.vertical
+        base_px = vd["upper_court_px"] + vd["middle_court_px"] + vd["lower_court_px"]
+        # 接口6 是三庭：去掉顶庭相关字段（top_court_cm / ratios.top_court / landmarks.hair_top）
+        data["four_courts"]["ratios"] = {
+            "upper_court": round(vd["upper_court_px"] / base_px, 3),
+            "middle_court": round(vd["middle_court_px"] / base_px, 3),
+            "lower_court": round(vd["lower_court_px"] / base_px, 3),
+        }
+        data["four_courts"].pop("top_court_cm", None)
+        data["face_total_height_cm"] = round(
+            result.upper_cm + result.middle_cm + result.lower_cm, 2)
+        # 注：landmarks.hair_top 保留返回（供前端/下游定位头顶），但顶庭数值、
+        # 占比、标注图仍按三庭处理，显示效果不变。
 
     # 七眼段宽度（cm）。eye1=左耳外段 eye2=左脸颊 eye3=左眼 eye4=两眼间距 eye5=右眼 eye6=右脸颊 eye7=右耳外段。
     # eye2~eye6（5段）只用内部分点，接口1/6 共用；eye1/eye7 需耳朵分割端线，仅接口1 有。
@@ -445,14 +430,11 @@ def _run_face_measure_data(image, variant="v1"):
             data["seven_eyes"][f"eye{i + 2}"] = (
                 None if (a is None or b is None) else round((b - a) / pc, 2))
         if variant != "v6":
-            # 接口1 额外算 eye1/eye7（左/右耳外段），需耳朵分割端线。
-            # 竖向范围：发际线弃用时用眉心做上界（hair_top 不可靠），否则用头顶。
+            # 接口1 额外算 eye1/eye7（左/右耳外段），需耳朵分割端线
             from face_analysis.annotation import _ear_edges_from_mask
-            top_y = (vd["brow_center"][1] if discarded
-                     else vd["hair_top"][1])
             head_l, head_r = _ear_edges_from_mask(
                 ear_mask, hair_mask,
-                top_y, vd["chin_tip"][1],
+                result.vertical["hair_top"][1], result.vertical["chin_tip"][1],
                 lcx, rcx, (lcx + rcx) / 2)
             data["seven_eyes"]["eye1"] = (
                 None if (head_l is None) else round((lcx - head_l) / pc, 2))
@@ -831,6 +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）。
@@ -1019,13 +1144,10 @@ async def face_features(
 - 必填 `gender`（`male`/`female`），决定发型集合（female 5 / male 4）。
 - 必填 `hair_style`（发型序号，逗号分隔如 `1,2,3`），决定返回哪些发际线类型。缺失/越界/非法返回 `1007`。
   `female`：1=ellipse,2=flower,3=heart,4=straight,5=wave；`male`：1=ellipse,2=inverse_arc,3=m,4=straight。
-- 可选 `use_mask` / `prompt`：同接口2 的生发控制参数（仅 male 路径生效）。
-- 可选 `generate_grow_image`（默认 `true`）：是否生成生发效果图（最耗时）。
+- 可选 `use_mask` / `prompt`：同接口2 的生发控制参数。
+  注：生发黑模板固定取 `hairline_texture_black/`（middle 档），即三档叠图分别用各自贴图、但生发目标固定 middle。
+- 可选 `generate_grow_image`（默认 `true`）：是否生成生发效果图（ComfyUI 生发，全流程最耗时）。
   `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，大幅降低耗时。
-- **生发机制（同接口2，按性别分流）**：
-  `female` 走「换发型 + Flux-2 整帧重绘」（依赖 change_hair:8801 与 ComfyUI:8188）；
-  `male` 走 ComfyUI `add_hair` 原生 inpaint。
-- 可选 `flux_model` / `redraw_max_side`：同接口2（仅 female 路径生效）。
 
 **返回说明**：
 
@@ -1102,11 +1224,9 @@ async def hairline_generate(
     image_base64: Optional[str] = Form(default=None, description="图片 base64（需带 data:image/...;base64, 前缀）"),
     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，仅 male 路径生效）"),
-    prompt: str = Form(default="填充遮罩区域的头发", description="ComfyUI 提示词（同接口2，仅 male 路径生效），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（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"),
-    flux_model: Optional[str] = Form(default=None, description="Flux 模型文件名（同接口2，切换模型用）。None=工作流默认"),
-    redraw_max_side: Optional[int] = Form(default=None, description="重绘压图长边像素（同接口2，仅 female 路径生效）。None=默认896；0=不缩图（原图直送）；其他如 768/640/1024"),
+    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", 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"):
         return err(1004, "gender 必填且只能为 male / female")
@@ -1131,9 +1251,7 @@ async def hairline_generate(
 
         res = await run_in_threadpool(
             generate_hairline_pngs, image, gender, hair_styles, use_mask, prompt,
-            generate_grow_image=generate_grow_image,
-            redraw_max_side=redraw_max_side, unet_name=flux_model,
-            v2_defaults=_V2_FINAL_DEFAULTS)
+            generate_grow_image=generate_grow_image)
         if res is None:
             return err(1001, "无法识别人像")
 

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_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/annotation.py

@@ -202,7 +202,7 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
 
     # --- 自适应尺寸：字号/线宽/虚线/箭头按短边缩放 ---
     s = min(w, h)
-    font_size = max(9, round(s * 0.020))      # 字号上调一档
+    font_size = max(8, round(s * 0.017))      # 字体更小
     line_w = max(1, round(s * 0.0022))
     dash_len = max(4, round(s * 0.008))
     gap_len = max(2, round(dash_len * 0.7))   # 虚线更稠密（间隙<划线）
@@ -212,11 +212,7 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
 
     buf = np.zeros((h, w, 4), dtype=np.uint8)
 
-    # 发际线弃用（hairline_discarded）：保留头顶横线，去掉发际线横线，
-    # 也不标顶/上庭（缺发际线作边界，算不出）。横线 = 头顶/眉心/鼻翼下缘/下巴尖。
-    if getattr(measure_result, "hairline_discarded", False):
-        order = ["hair_top", "brow_center", "nose_bottom", "chin_tip"]
-    elif variant == "v6":
+    if variant == "v6":
         order = ["hairline", "brow_center", "nose_bottom", "chin_tip"]
     else:
         order = ["hair_top", "hairline", "brow_center", "nose_bottom", "chin_tip"]
@@ -243,7 +239,7 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
     face_cx = (fx0 + fx1) / 2
     over = max(6, round(s * 0.030))           # 线超出包围盒的长度（参考图风格）
     face_half = (fx1 - fx0) / 2 + over        # 横线超出最外侧竖线一点
-    # 竖线纵向范围：v6 = 发际线→下巴尖（不超出）；v1（含发际线弃用）= 头顶→下巴尖并两端超出一点
+    # v6 竖线纵向范围 = 发际线→下巴尖（不超出）；v1 = 头顶→下巴尖并两端超出一点
     v_top = fy0 if variant == "v6" else fy0 - over
     v_bot = fy1 if variant == "v6" else fy1 + over
 
@@ -269,28 +265,19 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
         draw.text((x, ys[i]), text, fill=LINE_COLOR, font=font, anchor="lm")
 
     # --- 3b. 左侧四庭：名 + 数值两行（无 cm）+ 竖向虚线双箭头 ---
-    # court_start：庭段在 order 里的起始索引。发际线弃用时 order 首位是头顶（无下界发际线，
-    # 顶/上庭不标），中庭从眉心开始 → 跳过 order[0]。
-    if getattr(measure_result, "hairline_discarded", False):
-        court_cm = [measure_result.middle_cm, measure_result.lower_cm]
-        court_name = ["中庭", "下庭"]
-        n_court = 2
-        court_start = 1
-    elif variant == "v6":
+    if variant == "v6":
         court_cm = [measure_result.upper_cm, measure_result.middle_cm, measure_result.lower_cm]
         court_name = ["上庭", "中庭", "下庭"]
         n_court = 3
-        court_start = 0
     else:
         court_cm = [measure_result.top_cm, measure_result.upper_cm,
                     measure_result.middle_cm, measure_result.lower_cm]
         court_name = ["顶庭", "上庭", "中庭", "下庭"]
         n_court = 4
-        court_start = 0
     arrow_x = max(arrow_size + 1, fx0 - pad)   # 竖箭头所在 x（脸左侧，贴近最左竖线）
     court_total = sum(court_cm) or 1.0          # 各庭占比分母 = 四庭（v6 三庭）之和
     for i in range(n_court):
-        y_a, y_b = ys[court_start + i], ys[court_start + i + 1]
+        y_a, y_b = ys[i], ys[i + 1]
         # 竖向虚线双箭头，覆盖该庭高度（略收一点避免压到横线）
         inset = min(arrow_size, (y_b - y_a) * 0.12)
         draw_dashed_line_with_arrows(

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"]

face_analysis/measure.py

@@ -12,7 +12,7 @@ from face_analysis.calibration import (
     estimate_scale_factor, normalized_to_pixel, pixel_distance, _lm_list,
 )
 from face_analysis.face_mesh_landmarks import (
-    GLABELLA_9, NOSE_BOTTOM, CHIN_TIP,
+    GLABELLA_9, GLABELLA_151, NOSE_BOTTOM, CHIN_TIP,
     LEFT_EYE_OUTER, LEFT_EYE_INNER, RIGHT_EYE_INNER, RIGHT_EYE_OUTER,
     LEFT_CHEEK, RIGHT_CHEEK, LEFT_POSITION, RIGHT_POSITION,
 )
@@ -24,8 +24,10 @@ _TOP_RATIO = 0.22 / 0.28      # 顶庭 ÷ 中庭（≈ 0.786）
 
 
 def _brow_center(lm, w, h):
-    """眉心 = 索引 9（眉间上点）。"""
-    return normalized_to_pixel(lm[GLABELLA_9], w, h)
+    """眉心 = 索引 9 / 151 中点。"""
+    g9 = normalized_to_pixel(lm[GLABELLA_9], w, h)
+    g151 = normalized_to_pixel(lm[GLABELLA_151], w, h)
+    return (g9[0] + g151[0]) / 2, (g9[1] + g151[1]) / 2
 
 
 def estimate_vertical_landmarks(landmarks, image_width, image_height):
@@ -142,22 +144,9 @@ def measure_seven_eyes(landmarks, image_width, image_height):
     }
 
 
-def pt_or_none(vertical, name):
-    """vertical dict 的点 → {"x","y"}，值为 None 时返回 None。"""
-    v = vertical.get(name)
-    if v is None:
-        return None
-    return {"x": int(round(v[0])), "y": int(round(v[1]))}
-
-
 class MeasureResult:
     """测量结果，提供 to_response() 输出与接口文档同构的 data 字段。"""
 
-    # 发际线弃用阈值：发际线离头顶（顶庭）< 此值时判定分割不可靠，弃用发际线。
-    # hairline 与 hair_top 几乎重合（如稀疏头发中轴漏检只剩一小撮），说明发际线
-    # 定位无意义 → 顶/上庭置 null、标注图不画头顶/发际线。
-    HAIRLINE_DISCARD_TOP_CM = 0.7
-
     def __init__(self, vertical, eyes, px_per_cm, hairline_source, head_pose,
                  landmarks=None, image_width=None, image_height=None):
         self.vertical = vertical
@@ -175,14 +164,7 @@ class MeasureResult:
         self.upper_cm = vertical["upper_court_px"] / px_per_cm
         self.middle_cm = vertical["middle_court_px"] / px_per_cm
         self.lower_cm = vertical["lower_court_px"] / px_per_cm
-        # 发际线弃用判定：顶庭（头顶→发际线）过小视为发际线贴近头顶、不可靠。
-        # 弃用时 hairline_source 改为 "discarded"，face_total 只算中庭+下庭。
-        self.hairline_discarded = self.top_cm < self.HAIRLINE_DISCARD_TOP_CM
-        if self.hairline_discarded:
-            self.hairline_source = "discarded"
-            self.face_total_cm = self.middle_cm + self.lower_cm
-        else:
-            self.face_total_cm = self.top_cm + self.upper_cm + self.middle_cm + self.lower_cm
+        self.face_total_cm = self.top_cm + self.upper_cm + self.middle_cm + self.lower_cm
 
         # 七眼厘米
         self.eye_width_cm = eyes["eye_width_px"] / px_per_cm
@@ -190,77 +172,46 @@ class MeasureResult:
         self.inter_eye_cm = eyes["inter_eye_distance_px"] / px_per_cm
 
     def to_response(self):
-        # 发际线弃用：顶/上庭相关字段置 null（保留键），ratio 分母只算中下庭；
-        # landmarks.hair_top/hairline 置 null。否则按四庭正常输出。
-        if self.hairline_discarded:
-            base_px = (self.vertical["middle_court_px"] + self.vertical["lower_court_px"])
-            data = {
-                "face_total_height_cm": round(self.face_total_cm, 2),
-                "four_courts": {
-                    "top_court_cm": None,
-                    "upper_court_cm": None,
-                    "middle_court_cm": round(self.middle_cm, 2),
-                    "lower_court_cm": round(self.lower_cm, 2),
-                    "ratios": {
-                        "top_court": None,
-                        "upper_court": None,
-                        "middle_court": round(self.vertical["middle_court_px"] / base_px, 3),
-                        "lower_court": round(self.vertical["lower_court_px"] / base_px, 3),
-                    },
+        total_px = (self.vertical["top_court_px"] + self.vertical["upper_court_px"]
+                    + self.vertical["middle_court_px"] + self.vertical["lower_court_px"])
+        fw_px = self.eyes["face_width_px"]
+
+        def pt(name):
+            x, y = self.vertical[name]
+            return {"x": int(round(x)), "y": int(round(y))}
+
+        data = {
+            "face_total_height_cm": round(self.face_total_cm, 2),
+            "four_courts": {
+                "top_court_cm": round(self.top_cm, 2),
+                "upper_court_cm": round(self.upper_cm, 2),
+                "middle_court_cm": round(self.middle_cm, 2),
+                "lower_court_cm": round(self.lower_cm, 2),
+                "ratios": {
+                    "top_court": round(self.vertical["top_court_px"] / total_px, 3),
+                    "upper_court": round(self.vertical["upper_court_px"] / total_px, 3),
+                    "middle_court": round(self.vertical["middle_court_px"] / total_px, 3),
+                    "lower_court": round(self.vertical["lower_court_px"] / total_px, 3),
                 },
-                "seven_eyes": {
-                    "eye_width_cm": round(self.eye_width_cm, 2),
-                    "face_width_cm": round(self.face_width_cm, 2),
-                    "inter_eye_distance_cm": round(self.inter_eye_cm, 2),
-                    "ratios": {
-                        "eye_width": round(self.eyes["eye_width_px"] / self.eyes["face_width_px"], 3),
-                        "inter_eye_distance": round(self.eyes["inter_eye_distance_px"] / self.eyes["face_width_px"], 3),
-                    },
+            },
+            "seven_eyes": {
+                "eye_width_cm": round(self.eye_width_cm, 2),
+                "face_width_cm": round(self.face_width_cm, 2),
+                "inter_eye_distance_cm": round(self.inter_eye_cm, 2),
+                "ratios": {
+                    "eye_width": round(self.eyes["eye_width_px"] / fw_px, 3),
+                    "inter_eye_distance": round(self.eyes["inter_eye_distance_px"] / fw_px, 3),
                 },
-                "landmarks": {
-                    "hair_top": None,
-                    "hairline": None,
-                    "brow_center": pt_or_none(self.vertical, "brow_center"),
-                    "nose_bottom": pt_or_none(self.vertical, "nose_bottom"),
-                    "chin_tip": pt_or_none(self.vertical, "chin_tip"),
-                },
-                "hairline_source": self.hairline_source,
-            }
-        else:
-            total_px = (self.vertical["top_court_px"] + self.vertical["upper_court_px"]
-                        + self.vertical["middle_court_px"] + self.vertical["lower_court_px"])
-            data = {
-                "face_total_height_cm": round(self.face_total_cm, 2),
-                "four_courts": {
-                    "top_court_cm": round(self.top_cm, 2),
-                    "upper_court_cm": round(self.upper_cm, 2),
-                    "middle_court_cm": round(self.middle_cm, 2),
-                    "lower_court_cm": round(self.lower_cm, 2),
-                    "ratios": {
-                        "top_court": round(self.vertical["top_court_px"] / total_px, 3),
-                        "upper_court": round(self.vertical["upper_court_px"] / total_px, 3),
-                        "middle_court": round(self.vertical["middle_court_px"] / total_px, 3),
-                        "lower_court": round(self.vertical["lower_court_px"] / total_px, 3),
-                    },
-                },
-                "seven_eyes": {
-                    "eye_width_cm": round(self.eye_width_cm, 2),
-                    "face_width_cm": round(self.face_width_cm, 2),
-                    "inter_eye_distance_cm": round(self.inter_eye_cm, 2),
-                    "ratios": {
-                        "eye_width": round(self.eyes["eye_width_px"] / self.eyes["face_width_px"], 3),
-                        "inter_eye_distance": round(self.eyes["inter_eye_distance_px"] / self.eyes["face_width_px"], 3),
-                    },
-                },
-                "landmarks": {
-                    "hair_top": pt_or_none(self.vertical, "hair_top"),
-                    "hairline": pt_or_none(self.vertical, "hairline"),
-                    "brow_center": pt_or_none(self.vertical, "brow_center"),
-                    "nose_bottom": pt_or_none(self.vertical, "nose_bottom"),
-                    "chin_tip": pt_or_none(self.vertical, "chin_tip"),
-                },
-                "hairline_source": self.hairline_source,
-            }
+            },
+            "landmarks": {
+                "hair_top": pt("hair_top"),
+                "hairline": pt("hairline"),
+                "brow_center": pt("brow_center"),
+                "nose_bottom": pt("nose_bottom"),
+                "chin_tip": pt("chin_tip"),
+            },
+            "hairline_source": self.hairline_source,
+        }
         # left/right_position：mediapipe 21/251 号定位点（原图像素，与 landmarks 同坐标系）。
         # landmarks 缺省（如测试直构 MeasureResult）时不输出，保持向后兼容。
         if self.landmarks is not None and self.w and self.h:

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/service.py

@@ -49,14 +49,13 @@ _BLACK_TEXTURE_DIR = os.path.join(_REPO, "hairline_texture_black")
 _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。
-# 策略：输入图长边 > REDRAW_MAX_SIDE 才等比缩到该长边；≤ 时原图分辨率直送（不放大）。
-# 默认 1024：大于 1024 的图压到 1024（画质/速度均衡），≤1024 的小图保持原分辨率重绘。
-# 可用 REDRAW_MAX_SIDE 覆盖；0=永不缩图（原图直送）。
-_REDRAW_MAX_SIDE = int(os.getenv("REDRAW_MAX_SIDE", "1024"))
+# 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
+# 固定地板) + ComfyUI 两段串行。1024 档画质更好但部分大图会踩 12s 线，
+# 默认压到 896 兜底（ComfyUI ~4s，女性总耗时 9~11s）；追画质可设 REDRAW_MAX_SIDE=1024。
+_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。
@@ -64,6 +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
@@ -85,7 +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
@@ -411,22 +412,14 @@ def _grow_from_texture(image_bgr: np.ndarray, ctx: dict, white_path: str | None,
 def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
                            hair_styles: list[int], use_mask: bool = True,
                            prompt: str | None = None,
-                           generate_grow_image: bool = True,
-                           redraw_max_side: int | None = None,
-                           unet_name: str | None = None,
-                           v2_defaults: dict | None = None):
+                           generate_grow_image: bool = True):
     """接口5：对选中发型返回 middle/high/low 三档发际线透明叠图 + 生发图(同接口2)。
 
     入参同接口2：先选 gender，再多选 hair_styles（必填，1-indexed 按贴图排序）。
     每个选中发型返回三档叠图（middle/high/low，RGBA 透明层只含发际线曲线）与一张生发图；
-
-    生发机制（同接口2，按性别分流）：
-      - female：generate_grow_results_swap（swapHair + Flux-2 整帧重绘）
-      - male：generate_grow_results（ComfyUI add_hair inpaint）
-    redraw_max_side / unet_name / v2_defaults：female 路径参数，同接口2。
-      male 路径仅用 unet_name；redraw_max_side/v2_defaults 对 male 无意义（忽略）。
-    use_mask/prompt：仅 male 路径生效（同接口2 male）。
-    generate_grow_image（默认 True）：是否生成生发图（最耗时）。False 时跳过生发，
+    三档贴图同名，生发黑模板固定取自 hairline_texture_black/（middle），故生发目标固定 middle 档。
+    use_mask/prompt：同接口2 的生发参数。
+    generate_grow_image（默认 True）：是否生成生发图（ComfyUI，最耗时）。False 时跳过生发，
         各发型 grown_png 恒为 None，可大幅降低耗时（仅留三档发际线叠图与中心点）。
     Returns: {"images":[{hairline_type,order,overlays:{middle,high,low}((H,W,4) RGBA 透明层),grown_png}],
               "best_centers":{"middle":(x,y),"high":(x,y),"low":(x,y)}}；无人脸 None。
@@ -448,27 +441,11 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
     # 三档贴图表（同性别、同 key 顺序，因三个文件夹同名）
     tex_by_level = {lv: get_texture_map(lv)[gender] for lv in _TEXTURE_DIRS}
 
-    # 生发图（同接口2，按性别分流）：一次性算出所有选中发型的生发图，按 order 对应回叠图。
-    # female→generate_grow_results_swap（swapHair+Flux-2 整帧重绘）；
-    # male→generate_grow_results（ComfyUI add_hair inpaint）。
-    # generate_grow_image=False 时跳过，grown_by_order 为空 dict（各发型 grown_png 恒 None）。
-    grown_by_order: dict[int, bytes | None] = {}
-    if generate_grow_image:
-        try:
-            if gender == "female":
-                items = generate_grow_results_swap(
-                    image_bgr, hair_styles, v2_defaults or {},
-                    redraw_max_side=redraw_max_side, unet_name=unet_name)
-            else:
-                items = generate_grow_results(
-                    image_bgr, gender, use_mask, prompt, hair_styles,
-                    unet_name=unet_name)
-            if items is None:
-                return None  # 无人脸（同接口2 的 None 语义）
-            for it in items:
-                grown_by_order[it["order"]] = it.get("grown_png")
-        except Exception as e:  # noqa: BLE001  整批生发失败不拖垮叠图主结果
-            logger.warning("接口5 生发批量失败（gender=%s）：%s", gender, e)
+    # use_mask=False：干净原图+空遮罩与贴图无关，只跑一次 ComfyUI，选中项复用
+    # generate_grow_image=False：完全跳过生发（最耗时），grown_png 恒为 None
+    shared_grown = None
+    if generate_grow_image and not use_mask:
+        shared_grown = _grow_from_texture(image_bgr, ctx, None, use_mask=False, prompt=prompt)
 
     def _center_of(overlay):
         """从某档发际线透明叠图取面部中轴处的发际线中点 (x,y)，无像素返回 None。"""
@@ -481,13 +458,18 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
 
     images, best_centers = [], None
     for s in hair_styles:  # s = 1-indexed 发型序号
-        key, _mid_path = tex_by_level["middle"][s - 1]
+        key, mid_path = tex_by_level["middle"][s - 1]
         overlays = {}
         for lv in _TEXTURE_DIRS:
             white = load_texture_rgba(tex_by_level[lv][s - 1][1])
             overlays[lv] = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
-        # 生发图：从按性别算好的结果里按 order 取（generate_grow_image=False 时缺省 None）
-        grown_png = grown_by_order.get(s)
+        # 生发：固定 middle 黑模板（generate_grow_image=False 时跳过，恒 None）
+        if not generate_grow_image:
+            grown_png = None
+        elif not use_mask:
+            grown_png = shared_grown
+        else:
+            grown_png = _grow_from_texture(image_bgr, ctx, mid_path, use_mask=True, prompt=prompt)
         images.append({"hairline_type": key, "order": s,
                        "overlays": overlays, "grown_png": grown_png})
         # best_centers：首个选中发型三档（middle/high/low）发际线中点

image/compare_test/archive_20260725/batch_test.log

@@ -1,75 +0,0 @@
-=== 接口2 female 批量对比测试 ===
-矩阵: 4图 × 5发型 × 2档 = 40 次
-已跳过 0 个已完成项
-
-[1/40] ▶ girl2_ellipse_default896 (side=None)... ✅ 10.8s  出图 1082x1081 (204589B)
-[2/40] ▶ girl2_ellipse_origin0 (side=0)... ✅ 13.3s  出图 1088x1088 (232252B)
-[3/40] ▶ girl2_flower_default896 (side=None)... ✅ 11.0s  出图 1082x1081 (210875B)
-[4/40] ▶ girl2_flower_origin0 (side=0)... ✅ 12.5s  出图 1088x1088 (236073B)
-[5/40] ▶ girl2_heart_default896 (side=None)... ✅ 11.9s  出图 1082x1081 (198911B)
-[6/40] ▶ girl2_heart_origin0 (side=0)... ✅ 12.2s  出图 1088x1088 (249893B)
-[7/40] ▶ girl2_straight_default896 (side=None)... ✅ 12.1s  出图 1082x1081 (211803B)
-[8/40] ▶ girl2_straight_origin0 (side=0)... ✅ 12.3s  出图 1088x1088 (235377B)
-[9/40] ▶ girl2_wave_default896 (side=None)... ✅ 12.7s  出图 1082x1081 (207736B)
-[10/40] ▶ girl2_wave_origin0 (side=0)... ✅ 11.9s  出图 1088x1088 (238081B)
-[11/40] ▶ girl5_ellipse_default896 (side=None)... ✅ 9.6s  出图 768x752 (105867B)
-[12/40] ▶ girl5_ellipse_origin0 (side=0)... ✅ 7.3s  出图 768x752 (112963B)
-[13/40] ▶ girl5_flower_default896 (side=None)... ✅ 8.0s  出图 768x752 (114378B)
-[14/40] ▶ girl5_flower_origin0 (side=0)... ✅ 7.2s  出图 768x752 (108495B)
-[15/40] ▶ girl5_heart_default896 (side=None)... ✅ 8.1s  出图 768x752 (113874B)
-[16/40] ▶ girl5_heart_origin0 (side=0)... ✅ 7.6s  出图 768x752 (103160B)
-[17/40] ▶ girl5_straight_default896 (side=None)... ✅ 8.4s  出图 768x752 (104492B)
-[18/40] ▶ girl5_straight_origin0 (side=0)... ✅ 7.2s  出图 768x752 (100941B)
-[19/40] ▶ girl5_wave_default896 (side=None)... ✅ 8.4s  出图 768x752 (120122B)
-[20/40] ▶ girl5_wave_origin0 (side=0)... ✅ 7.7s  出图 768x752 (113282B)
-[21/40] ▶ qwer_ellipse_default896 (side=None)... ✅ 10.2s  出图 1288x1678 (261812B)
-[22/40] ▶ qwer_ellipse_origin0 (side=0)... ✅ 30.0s  出图 1280x1680 (331013B)
-[23/40] ▶ qwer_flower_default896 (side=None)... ✅ 10.2s  出图 1288x1678 (312834B)
-[24/40] ▶ qwer_flower_origin0 (side=0)... ✅ 25.0s  出图 1280x1680 (328605B)
-[25/40] ▶ qwer_heart_default896 (side=None)... ✅ 11.1s  出图 1288x1678 (331337B)
-[26/40] ▶ qwer_heart_origin0 (side=0)... ✅ 25.0s  出图 1280x1680 (355982B)
-[27/40] ▶ qwer_straight_default896 (side=None)... ✅ 10.7s  出图 1288x1678 (281665B)
-[28/40] ▶ qwer_straight_origin0 (side=0)... ✅ 23.4s  出图 1280x1680 (322183B)
-[29/40] ▶ qwer_wave_default896 (side=None)... ✅ 11.1s  出图 1288x1678 (289322B)
-[30/40] ▶ qwer_wave_origin0 (side=0)... ✅ 25.8s  出图 1280x1680 (351719B)
-[31/40] ▶ asdf_ellipse_default896 (side=None)... ✅ 11.2s  出图 1254x1666 (285362B)
-[32/40] ▶ asdf_ellipse_origin0 (side=0)... ✅ 25.7s  出图 1248x1664 (286768B)
-[33/40] ▶ asdf_flower_default896 (side=None)... ✅ 10.7s  出图 1254x1666 (266946B)
-[34/40] ▶ asdf_flower_origin0 (side=0)... ✅ 22.9s  出图 1248x1664 (353668B)
-[35/40] ▶ asdf_heart_default896 (side=None)... ✅ 10.4s  出图 1254x1666 (271975B)
-[36/40] ▶ asdf_heart_origin0 (side=0)... ✅ 22.9s  出图 1248x1664 (291983B)
-[37/40] ▶ asdf_straight_default896 (side=None)... ✅ 9.8s  出图 1254x1666 (258665B)
-[38/40] ▶ asdf_straight_origin0 (side=0)... ✅ 24.0s  出图 1248x1664 (279883B)
-[39/40] ▶ asdf_wave_default896 (side=None)... ✅ 11.9s  出图 1254x1666 (286515B)
-[40/40] ▶ asdf_wave_origin0 (side=0)... ✅ 24.3s  出图 1248x1664 (295694B)
-
-======================================================================
-汇总报告
-======================================================================
-
---- 速度对比（秒，✓=成功 ✗=失败）---
-图       发型               默认896         原图0        差值
-girl2   ellipse          10.8✓       13.3✓      +2.5
-girl2   flower           11.0✓       12.5✓      +1.5
-girl2   heart            11.9✓       12.2✓      +0.3
-girl2   straight         12.1✓       12.3✓      +0.2
-girl2   wave             12.7✓       11.9✓      -0.8
-girl5   ellipse           9.6✓        7.3✓      -2.3
-girl5   flower            8.0✓        7.2✓      -0.8
-girl5   heart             8.1✓        7.6✓      -0.5
-girl5   straight          8.4✓        7.2✓      -1.2
-girl5   wave              8.4✓        7.7✓      -0.7
-qwer    ellipse          10.2✓       30.0✓     +19.8
-qwer    flower           10.2✓       25.0✓     +14.8
-qwer    heart            11.1✓       25.0✓     +13.9
-qwer    straight         10.7✓       23.4✓     +12.7
-qwer    wave             11.1✓       25.8✓     +14.7
-asdf    ellipse          11.2✓       25.7✓     +14.5
-asdf    flower           10.7✓       22.9✓     +12.2
-asdf    heart            10.4✓       22.9✓     +12.5
-asdf    straight          9.8✓       24.0✓     +14.2
-asdf    wave             11.9✓       24.3✓     +12.4
-
-结果图: /home/ubuntu/hair/image/compare_test/out/
-CSV报告: /home/ubuntu/hair/image/compare_test/out/report.csv
-JSON明细: /home/ubuntu/hair/image/compare_test/out/report.json

image/compare_test/archive_20260725/report.csv

@@ -1,41 +0,0 @@
-img,style,side,ok,elapsed_s,out_w,out_h,err
-girl2,ellipse,default896,True,10.8,1082,1081,
-girl2,ellipse,origin0,True,13.3,1088,1088,
-girl2,flower,default896,True,11.0,1082,1081,
-girl2,flower,origin0,True,12.5,1088,1088,
-girl2,heart,default896,True,11.9,1082,1081,
-girl2,heart,origin0,True,12.2,1088,1088,
-girl2,straight,default896,True,12.1,1082,1081,
-girl2,straight,origin0,True,12.3,1088,1088,
-girl2,wave,default896,True,12.7,1082,1081,
-girl2,wave,origin0,True,11.9,1088,1088,
-girl5,ellipse,default896,True,9.6,768,752,
-girl5,ellipse,origin0,True,7.3,768,752,
-girl5,flower,default896,True,8.0,768,752,
-girl5,flower,origin0,True,7.2,768,752,
-girl5,heart,default896,True,8.1,768,752,
-girl5,heart,origin0,True,7.6,768,752,
-girl5,straight,default896,True,8.4,768,752,
-girl5,straight,origin0,True,7.2,768,752,
-girl5,wave,default896,True,8.4,768,752,
-girl5,wave,origin0,True,7.7,768,752,
-qwer,ellipse,default896,True,10.2,1288,1678,
-qwer,ellipse,origin0,True,30.0,1280,1680,
-qwer,flower,default896,True,10.2,1288,1678,
-qwer,flower,origin0,True,25.0,1280,1680,
-qwer,heart,default896,True,11.1,1288,1678,
-qwer,heart,origin0,True,25.0,1280,1680,
-qwer,straight,default896,True,10.7,1288,1678,
-qwer,straight,origin0,True,23.4,1280,1680,
-qwer,wave,default896,True,11.1,1288,1678,
-qwer,wave,origin0,True,25.8,1280,1680,
-asdf,ellipse,default896,True,11.2,1254,1666,
-asdf,ellipse,origin0,True,25.7,1248,1664,
-asdf,flower,default896,True,10.7,1254,1666,
-asdf,flower,origin0,True,22.9,1248,1664,
-asdf,heart,default896,True,10.4,1254,1666,
-asdf,heart,origin0,True,22.9,1248,1664,
-asdf,straight,default896,True,9.8,1254,1666,
-asdf,straight,origin0,True,24.0,1248,1664,
-asdf,wave,default896,True,11.9,1254,1666,
-asdf,wave,origin0,True,24.3,1248,1664,

image/compare_test/archive_20260725/report.json

@@ -1,562 +0,0 @@
-[
- {
-  "key": "girl2_ellipse_default896",
-  "img": "girl2",
-  "style": "ellipse",
-  "style_idx": 1,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 10.8,
-  "err": "",
-  "out_w": 1082,
-  "out_h": 1081,
-  "bytes": 204589
- },
- {
-  "key": "girl2_ellipse_origin0",
-  "img": "girl2",
-  "style": "ellipse",
-  "style_idx": 1,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 13.3,
-  "err": "",
-  "out_w": 1088,
-  "out_h": 1088,
-  "bytes": 232252
- },
- {
-  "key": "girl2_flower_default896",
-  "img": "girl2",
-  "style": "flower",
-  "style_idx": 2,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 11.0,
-  "err": "",
-  "out_w": 1082,
-  "out_h": 1081,
-  "bytes": 210875
- },
- {
-  "key": "girl2_flower_origin0",
-  "img": "girl2",
-  "style": "flower",
-  "style_idx": 2,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 12.5,
-  "err": "",
-  "out_w": 1088,
-  "out_h": 1088,
-  "bytes": 236073
- },
- {
-  "key": "girl2_heart_default896",
-  "img": "girl2",
-  "style": "heart",
-  "style_idx": 3,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 11.9,
-  "err": "",
-  "out_w": 1082,
-  "out_h": 1081,
-  "bytes": 198911
- },
- {
-  "key": "girl2_heart_origin0",
-  "img": "girl2",
-  "style": "heart",
-  "style_idx": 3,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 12.2,
-  "err": "",
-  "out_w": 1088,
-  "out_h": 1088,
-  "bytes": 249893
- },
- {
-  "key": "girl2_straight_default896",
-  "img": "girl2",
-  "style": "straight",
-  "style_idx": 4,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 12.1,
-  "err": "",
-  "out_w": 1082,
-  "out_h": 1081,
-  "bytes": 211803
- },
- {
-  "key": "girl2_straight_origin0",
-  "img": "girl2",
-  "style": "straight",
-  "style_idx": 4,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 12.3,
-  "err": "",
-  "out_w": 1088,
-  "out_h": 1088,
-  "bytes": 235377
- },
- {
-  "key": "girl2_wave_default896",
-  "img": "girl2",
-  "style": "wave",
-  "style_idx": 5,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 12.7,
-  "err": "",
-  "out_w": 1082,
-  "out_h": 1081,
-  "bytes": 207736
- },
- {
-  "key": "girl2_wave_origin0",
-  "img": "girl2",
-  "style": "wave",
-  "style_idx": 5,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 11.9,
-  "err": "",
-  "out_w": 1088,
-  "out_h": 1088,
-  "bytes": 238081
- },
- {
-  "key": "girl5_ellipse_default896",
-  "img": "girl5",
-  "style": "ellipse",
-  "style_idx": 1,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 9.6,
-  "err": "",
-  "out_w": 768,
-  "out_h": 752,
-  "bytes": 105867
- },
- {
-  "key": "girl5_ellipse_origin0",
-  "img": "girl5",
-  "style": "ellipse",
-  "style_idx": 1,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 7.3,
-  "err": "",
-  "out_w": 768,
-  "out_h": 752,
-  "bytes": 112963
- },
- {
-  "key": "girl5_flower_default896",
-  "img": "girl5",
-  "style": "flower",
-  "style_idx": 2,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 8.0,
-  "err": "",
-  "out_w": 768,
-  "out_h": 752,
-  "bytes": 114378
- },
- {
-  "key": "girl5_flower_origin0",
-  "img": "girl5",
-  "style": "flower",
-  "style_idx": 2,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 7.2,
-  "err": "",
-  "out_w": 768,
-  "out_h": 752,
-  "bytes": 108495
- },
- {
-  "key": "girl5_heart_default896",
-  "img": "girl5",
-  "style": "heart",
-  "style_idx": 3,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 8.1,
-  "err": "",
-  "out_w": 768,
-  "out_h": 752,
-  "bytes": 113874
- },
- {
-  "key": "girl5_heart_origin0",
-  "img": "girl5",
-  "style": "heart",
-  "style_idx": 3,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 7.6,
-  "err": "",
-  "out_w": 768,
-  "out_h": 752,
-  "bytes": 103160
- },
- {
-  "key": "girl5_straight_default896",
-  "img": "girl5",
-  "style": "straight",
-  "style_idx": 4,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 8.4,
-  "err": "",
-  "out_w": 768,
-  "out_h": 752,
-  "bytes": 104492
- },
- {
-  "key": "girl5_straight_origin0",
-  "img": "girl5",
-  "style": "straight",
-  "style_idx": 4,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 7.2,
-  "err": "",
-  "out_w": 768,
-  "out_h": 752,
-  "bytes": 100941
- },
- {
-  "key": "girl5_wave_default896",
-  "img": "girl5",
-  "style": "wave",
-  "style_idx": 5,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 8.4,
-  "err": "",
-  "out_w": 768,
-  "out_h": 752,
-  "bytes": 120122
- },
- {
-  "key": "girl5_wave_origin0",
-  "img": "girl5",
-  "style": "wave",
-  "style_idx": 5,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 7.7,
-  "err": "",
-  "out_w": 768,
-  "out_h": 752,
-  "bytes": 113282
- },
- {
-  "key": "qwer_ellipse_default896",
-  "img": "qwer",
-  "style": "ellipse",
-  "style_idx": 1,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 10.2,
-  "err": "",
-  "out_w": 1288,
-  "out_h": 1678,
-  "bytes": 261812
- },
- {
-  "key": "qwer_ellipse_origin0",
-  "img": "qwer",
-  "style": "ellipse",
-  "style_idx": 1,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 30.0,
-  "err": "",
-  "out_w": 1280,
-  "out_h": 1680,
-  "bytes": 331013
- },
- {
-  "key": "qwer_flower_default896",
-  "img": "qwer",
-  "style": "flower",
-  "style_idx": 2,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 10.2,
-  "err": "",
-  "out_w": 1288,
-  "out_h": 1678,
-  "bytes": 312834
- },
- {
-  "key": "qwer_flower_origin0",
-  "img": "qwer",
-  "style": "flower",
-  "style_idx": 2,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 25.0,
-  "err": "",
-  "out_w": 1280,
-  "out_h": 1680,
-  "bytes": 328605
- },
- {
-  "key": "qwer_heart_default896",
-  "img": "qwer",
-  "style": "heart",
-  "style_idx": 3,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 11.1,
-  "err": "",
-  "out_w": 1288,
-  "out_h": 1678,
-  "bytes": 331337
- },
- {
-  "key": "qwer_heart_origin0",
-  "img": "qwer",
-  "style": "heart",
-  "style_idx": 3,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 25.0,
-  "err": "",
-  "out_w": 1280,
-  "out_h": 1680,
-  "bytes": 355982
- },
- {
-  "key": "qwer_straight_default896",
-  "img": "qwer",
-  "style": "straight",
-  "style_idx": 4,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 10.7,
-  "err": "",
-  "out_w": 1288,
-  "out_h": 1678,
-  "bytes": 281665
- },
- {
-  "key": "qwer_straight_origin0",
-  "img": "qwer",
-  "style": "straight",
-  "style_idx": 4,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 23.4,
-  "err": "",
-  "out_w": 1280,
-  "out_h": 1680,
-  "bytes": 322183
- },
- {
-  "key": "qwer_wave_default896",
-  "img": "qwer",
-  "style": "wave",
-  "style_idx": 5,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 11.1,
-  "err": "",
-  "out_w": 1288,
-  "out_h": 1678,
-  "bytes": 289322
- },
- {
-  "key": "qwer_wave_origin0",
-  "img": "qwer",
-  "style": "wave",
-  "style_idx": 5,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 25.8,
-  "err": "",
-  "out_w": 1280,
-  "out_h": 1680,
-  "bytes": 351719
- },
- {
-  "key": "asdf_ellipse_default896",
-  "img": "asdf",
-  "style": "ellipse",
-  "style_idx": 1,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 11.2,
-  "err": "",
-  "out_w": 1254,
-  "out_h": 1666,
-  "bytes": 285362
- },
- {
-  "key": "asdf_ellipse_origin0",
-  "img": "asdf",
-  "style": "ellipse",
-  "style_idx": 1,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 25.7,
-  "err": "",
-  "out_w": 1248,
-  "out_h": 1664,
-  "bytes": 286768
- },
- {
-  "key": "asdf_flower_default896",
-  "img": "asdf",
-  "style": "flower",
-  "style_idx": 2,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 10.7,
-  "err": "",
-  "out_w": 1254,
-  "out_h": 1666,
-  "bytes": 266946
- },
- {
-  "key": "asdf_flower_origin0",
-  "img": "asdf",
-  "style": "flower",
-  "style_idx": 2,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 22.9,
-  "err": "",
-  "out_w": 1248,
-  "out_h": 1664,
-  "bytes": 353668
- },
- {
-  "key": "asdf_heart_default896",
-  "img": "asdf",
-  "style": "heart",
-  "style_idx": 3,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 10.4,
-  "err": "",
-  "out_w": 1254,
-  "out_h": 1666,
-  "bytes": 271975
- },
- {
-  "key": "asdf_heart_origin0",
-  "img": "asdf",
-  "style": "heart",
-  "style_idx": 3,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 22.9,
-  "err": "",
-  "out_w": 1248,
-  "out_h": 1664,
-  "bytes": 291983
- },
- {
-  "key": "asdf_straight_default896",
-  "img": "asdf",
-  "style": "straight",
-  "style_idx": 4,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 9.8,
-  "err": "",
-  "out_w": 1254,
-  "out_h": 1666,
-  "bytes": 258665
- },
- {
-  "key": "asdf_straight_origin0",
-  "img": "asdf",
-  "style": "straight",
-  "style_idx": 4,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 24.0,
-  "err": "",
-  "out_w": 1248,
-  "out_h": 1664,
-  "bytes": 279883
- },
- {
-  "key": "asdf_wave_default896",
-  "img": "asdf",
-  "style": "wave",
-  "style_idx": 5,
-  "side": "default896",
-  "side_val": null,
-  "ok": true,
-  "elapsed": 11.9,
-  "err": "",
-  "out_w": 1254,
-  "out_h": 1666,
-  "bytes": 286515
- },
- {
-  "key": "asdf_wave_origin0",
-  "img": "asdf",
-  "style": "wave",
-  "style_idx": 5,
-  "side": "origin0",
-  "side_val": 0,
-  "ok": true,
-  "elapsed": 24.3,
-  "err": "",
-  "out_w": 1248,
-  "out_h": 1664,
-  "bytes": 295694
- }
-]

image/compare_test/batch_test.py

@@ -1,162 +0,0 @@
-#!/usr/bin/env python3
-"""接口2 female 批量对比测试：4图 × 5发型 × 3分辨率档位 = 60次请求。
-串行执行，记录耗时与成败，结果图按 图_发型_档位 命名保存。
-用法: python3 batch_test.py
-支持断点续跑（progress.json）；新增档位时只会补跑未完成项。
-"""
-import base64, csv, json, os, sys, time, traceback
-import requests
-
-API = "http://127.0.0.1:8187/api/v1/hair/grow"
-TOKEN = "dev-shared-secret-2026"
-TIMEOUT = 600
-OUT = "/home/ubuntu/hair/image/compare_test/out"
-PROGRESS = "/home/ubuntu/hair/image/compare_test/progress.json"
-
-IMG_DIR = "/home/ubuntu/hair/image"
-IMAGES = [
-    ("girl2",  f"{IMG_DIR}/girl_img/girl2.jpg"),
-    ("girl5",  f"{IMG_DIR}/girl_img/girl5.jpg"),
-    ("qwer",   f"{IMG_DIR}/qwer.jpg"),
-    ("asdf",   f"{IMG_DIR}/asdf.jpg"),
-]
-# female: 1=ellipse 2=flower 3=heart 4=straight 5=wave
-STYLES = [
-    (1, "ellipse"), (2, "flower"), (3, "heart"), (4, "straight"), (5, "wave"),
-]
-# 三档: 默认896 / 显式1024 / 原图直送0
-SIDES = [
-    ("default896", None),   # 不传 → 后端默认896
-    ("side1024",   1024),   # 长边压到 1024
-    ("origin0",    0),      # 原图直送
-]
-SIDE_LABEL = {
-    "default896": "默认896",
-    "side1024": "1024",
-    "origin0": "原图0",
-}
-
-def load_progress():
-    if os.path.exists(PROGRESS):
-        try:
-            return json.load(open(PROGRESS))
-        except Exception:
-            pass
-    return {"done": [], "results": []}
-
-def save_progress(prog):
-    json.dump(prog, open(PROGRESS, "w"), ensure_ascii=False, indent=1)
-
-def run_one(img_name, img_path, style_idx, style_key, side_name, side_val):
-    """跑单次请求，返回 dict 结果。"""
-    key = f"{img_name}_{style_key}_{side_name}"
-    with open(img_path, "rb") as f:
-        img_b64 = base64.b64encode(f.read()).decode()
-    data = {
-        "image_base64": "data:image/jpeg;base64," + img_b64,
-        "gender": "female",
-        "hair_style": str(style_idx),
-    }
-    if side_val is not None:
-        data["redraw_max_side"] = str(side_val)
-    t0 = time.time()
-    rec = {"key": key, "img": img_name, "style": style_key, "style_idx": style_idx,
-           "side": side_name, "side_val": side_val, "ok": False,
-           "elapsed": 0.0, "err": "", "out_w": 0, "out_h": 0}
-    try:
-        r = requests.post(API, data=data, headers={"X-Internal-Token": TOKEN}, timeout=TIMEOUT)
-        rec["elapsed"] = round(time.time() - t0, 1)
-        d = r.json()
-        if d.get("code") != 0:
-            rec["err"] = f"code={d.get('code')} {d.get('message','')}"[:200]
-            return rec
-        results = (d.get("data") or {}).get("results") or []
-        if not results:
-            rec["err"] = "空结果"
-            return rec
-        it = results[0]
-        grown = it.get("grown_image_base64")
-        if not grown:
-            rec["err"] = "无生发图(grown_png=None, 重绘失败)"
-            return rec
-        raw = base64.b64decode(grown)
-        from PIL import Image
-        import io as _io
-        im = Image.open(_io.BytesIO(raw))
-        rec["out_w"], rec["out_h"] = im.size
-        out_path = f"{OUT}/{key}.jpg"
-        with open(out_path, "wb") as fo:
-            fo.write(raw)
-        rec["ok"] = True
-        rec["bytes"] = len(raw)
-    except requests.exceptions.Timeout:
-        rec["elapsed"] = round(time.time() - t0, 1)
-        rec["err"] = f"超时(>{TIMEOUT}s)"
-    except Exception as e:
-        rec["elapsed"] = round(time.time() - t0, 1)
-        rec["err"] = f"{type(e).__name__}: {str(e)[:180]}"
-    return rec
-
-def main():
-    os.makedirs(OUT, exist_ok=True)
-    prog = load_progress()
-    done_keys = set(prog["done"])
-    total = len(IMAGES) * len(STYLES) * len(SIDES)
-    print(f"=== 接口2 female 批量对比测试 ===")
-    print(f"矩阵: {len(IMAGES)}图 × {len(STYLES)}发型 × {len(SIDES)}档 = {total} 次")
-    print(f"已跳过 {len(done_keys)} 个已完成项\n")
-
-    idx = 0
-    for img_name, img_path in IMAGES:
-        for style_idx, style_key in STYLES:
-            for side_name, side_val in SIDES:
-                idx += 1
-                key = f"{img_name}_{style_key}_{side_name}"
-                if key in done_keys:
-                    print(f"[{idx}/{total}] ⏭  跳过已完成 {key}")
-                    continue
-                print(f"[{idx}/{total}] ▶ {key} (side={side_val})...", end=" ", flush=True)
-                rec = run_one(img_name, img_path, style_idx, style_key, side_name, side_val)
-                prog["results"].append(rec)
-                prog["done"].append(key)
-                save_progress(prog)
-                if rec["ok"]:
-                    print(f"✅ {rec['elapsed']}s  出图 {rec['out_w']}x{rec['out_h']} ({rec['bytes']}B)")
-                else:
-                    print(f"❌ {rec['elapsed']}s  {rec['err']}")
-                time.sleep(2)  # 串行间隔，避免队列堆积
-
-    # 汇总
-    print("\n" + "="*70)
-    print("汇总报告")
-    print("="*70)
-    write_report(prog["results"])
-    print(f"\n结果图: {OUT}/")
-    print(f"CSV报告: {OUT}/report.csv")
-    print(f"JSON明细: {OUT}/report.json")
-
-def write_report(results):
-    # CSV
-    csv_path = f"{OUT}/report.csv"
-    with open(csv_path, "w", newline="") as f:
-        w = csv.writer(f)
-        w.writerow(["img","style","side","ok","elapsed_s","out_w","out_h","err"])
-        for r in results:
-            w.writerow([r["img"],r["style"],r["side"],r["ok"],r["elapsed"],
-                        r["out_w"],r["out_h"],r["err"]])
-    # JSON
-    json.dump(results, open(f"{OUT}/report.json","w"), ensure_ascii=False, indent=1)
-    # 控制台速度对比表
-    print("\n--- 速度对比（秒，✓=成功 ✗=失败）---")
-    headers = ["图", "发型"] + [SIDE_LABEL[s] for s, _ in SIDES]
-    print(f"{headers[0]:<8}{headers[1]:<10}" + "".join(f"{h:>12}" for h in headers[2:]))
-    for img_name, _ in IMAGES:
-        for _, style_key in STYLES:
-            cells = []
-            for side_name, _ in SIDES:
-                r = next((x for x in results if x["img"]==img_name and x["style"]==style_key and x["side"]==side_name), None)
-                cells.append(f"{r['elapsed']}{'✓' if r['ok'] else '✗'}" if r else "-")
-            print(f"{img_name:<8}{style_key:<10}" + "".join(f"{c:>12}" for c in cells))
-
-if __name__ == "__main__":
-    main()

image/compare_test/gen_report.py

@@ -1,244 +0,0 @@
-#!/usr/bin/env python3
-"""把 report.json + 结果图生成为自包含 HTML 报告（支持 896 / 1024 / 原图0 三档）。"""
-import json, os, html
-
-OUT = "/home/ubuntu/hair/image/compare_test/out"
-REPORT_HTML = os.path.join(OUT, "report.html")
-results = json.load(open(os.path.join(OUT, "report.json")))
-
-IMAGES = ["girl2", "girl5", "qwer", "asdf"]
-STYLES = ["ellipse", "flower", "heart", "straight", "wave"]
-SIDES = [
-    ("default896", "默认 896", "#2563eb", "d"),
-    ("side1024",   "1024",     "#10b981", "m"),
-    ("origin0",    "原图直送 0", "#f59e0b", "o"),
-]
-IMG_LONGSIDE = {"girl2": 1082, "girl5": 767, "qwer": 1678, "asdf": 1666}
-STYLE_CN = {"ellipse": "椭圆", "flower": "花瓣", "heart": "心形", "straight": "直线", "wave": "波浪"}
-
-def get(img, style, side):
-    for r in results:
-        if r["img"] == img and r["style"] == style and r["side"] == side:
-            return r
-    return None
-
-# 总览统计（>60s 视为冷启动/异常，不进均值）
-total = len(results)
-ok = sum(1 for r in results if r["ok"])
-COLD_S = 60.0
-
-def avg(seq):
-    seq = [x for x in seq if x is not None]
-    return sum(seq) / len(seq) if seq else 0
-
-def times(side, img=None):
-    out = []
-    for r in results:
-        if not r["ok"] or r["side"] != side:
-            continue
-        if img is not None and r["img"] != img:
-            continue
-        if r["elapsed"] >= COLD_S:
-            continue
-        out.append(r["elapsed"])
-    return out
-
-cold_n = sum(1 for r in results if r["ok"] and r["elapsed"] >= COLD_S)
-side_avgs = {sid: avg(times(sid)) for sid, *_ in SIDES}
-all_times = [t for sid, *_ in SIDES for t in times(sid)]
-max_bar = max(all_times + [1])
-
-# 构造速度对比图数据：每图 × 三档
-chart_rows = []
-for img in IMAGES:
-    chart_rows.append((img, IMG_LONGSIDE[img], [avg(times(sid, img)) for sid, *_ in SIDES]))
-
-def bar_svg():
-    bar_h = 22
-    gap = 12
-    label_w = 78
-    chart_w = 760
-    n_bars = len(SIDES)
-    rows = len(chart_rows)
-    h = rows * (bar_h * n_bars + gap) + 40
-    parts = [f'<svg viewBox="0 0 {label_w + chart_w + 80} {h}" class="chart">']
-    y = 10
-    for img, longside, avgs in chart_rows:
-        base = avgs[0] if avgs and avgs[0] else 1
-        for i, ((sid, label, color, _), a) in enumerate(zip(SIDES, avgs)):
-            yi = y + i * bar_h
-            w = int(a / max_bar * chart_w) if a else 0
-            warn = "⚠" if (i > 0 and a > base * 1.8) else ""
-            parts.append(f'<rect x="{label_w}" y="{yi}" width="{w}" height="{bar_h-4}" rx="3" fill="{color}"/>')
-            parts.append(f'<text x="{label_w + w + 6}" y="{yi + bar_h - 10}" class="barlabel">{a:.1f}s {warn}</text>')
-            parts.append(f'<text x="{label_w-8}" y="{yi + bar_h - 10}" class="rowlabel" text-anchor="end">{html.escape(label)}</text>')
-        parts.append(f'<text x="0" y="{y + bar_h - 2}" class="imglabel">{img}<tspan class="imgside">长边{longside}</tspan></text>')
-        y += bar_h * n_bars + gap
-    parts.append("</svg>")
-    return "".join(parts)
-
-def img_cell(r, cls):
-    if not r:
-        return f'<td class="{cls} fail">缺失</td>'
-    fname = r["key"] + ".jpg"
-    status = "✅" if r["ok"] else "❌"
-    t = f'{r["elapsed"]}s'
-    dim = f'{r["out_w"]}×{r["out_h"]}'
-    err = f'<div class="err">{html.escape(r["err"])}</div>' if r["err"] else ""
-    img_tag = (f'<img loading="lazy" src="{fname}" onclick="openImg(this.src)" alt="{html.escape(r["key"])}">'
-               if r["ok"] else '<div class="noimg">无图</div>')
-    return (f'<td class="{cls}"><div class="thumb">{img_tag}</div>'
-            f'<div class="meta">{status} {t} · {dim}</div>{err}</td>')
-
-def compare_cards():
-    out = []
-    for img in IMAGES:
-        out.append(f'<div class="card"><div class="card-h">📷 {html.escape(img)} <span class="tag">原图长边 {IMG_LONGSIDE[img]}px</span></div><div class="card-b">')
-        heads = "".join(f'<th class="{cls}">{html.escape(label)}</th>' for _, label, _, cls in SIDES)
-        out.append(f'<table class="cmp"><thead><tr><th>发型</th>{heads}</tr></thead><tbody>')
-        for style in STYLES:
-            sc = STYLE_CN[style]
-            cells = "".join(img_cell(get(img, style, sid), cls) for sid, _, _, cls in SIDES)
-            out.append(f'<tr><td class="sname">{html.escape(style)}<span>{sc}</span></td>{cells}</tr>')
-        out.append("</tbody></table></div></div>")
-    return "".join(out)
-
-# 结论
-small = [img for img in IMAGES if IMG_LONGSIDE[img] <= 1100]
-large = [img for img in IMAGES if IMG_LONGSIDE[img] > 1100]
-def ratio_range(num_side, den_side="default896"):
-    ratios = []
-    for img in large:
-        den = avg(times(den_side, img))
-        num = avg(times(num_side, img))
-        if den:
-            ratios.append(num / den)
-    if not ratios:
-        return 0, 0
-    return min(ratios), max(ratios)
-
-r1024_lo, r1024_hi = ratio_range("side1024")
-r0_lo, r0_hi = ratio_range("origin0")
-cold_note = f"冷启动 {cold_n} 次（≥{COLD_S:.0f}s）已从均值剔除。" if cold_n else ""
-conclusion = (
-    f"原图长边 ≤ 1100（{'/'.join(small)}）时三档耗时接近；"
-    f"长边 > 1600（{'/'.join(large)}）时相对默认896："
-    f"<b>1024 约 {r1024_lo:.1f}~{r1024_hi:.1f}×</b>，"
-    f"<b>原图直送约 {r0_lo:.1f}~{r0_hi:.1f}×</b>。"
-    f"{cold_note}画质对比见下方三列并排，点击可放大。"
-)
-
-avg896 = side_avgs.get("default896", 0)
-avg1024 = side_avgs.get("side1024", 0)
-avg0 = side_avgs.get("origin0", 0)
-ratio1024 = avg1024 / avg896 if avg896 else 0
-ratio0 = avg0 / avg896 if avg896 else 0
-
-legend = "".join(
-    f'<span><i style="background:{color}"></i>{html.escape(label)}</span>'
-    for _, label, color, _ in SIDES
-)
-
-html_doc = f"""<!DOCTYPE html>
-<html lang="zh-CN">
-<head>
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>接口2 分辨率对比测试报告</title>
-<style>
-  * {{ box-sizing: border-box; margin: 0; padding: 0; }}
-  body {{ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "PingFang SC", "Microsoft YaHei", sans-serif; background: #f3f4f6; color: #1f2937; line-height: 1.5; padding: 20px; }}
-  .wrap {{ max-width: 1400px; margin: 0 auto; }}
-  h1 {{ font-size: 24px; margin-bottom: 4px; }}
-  .sub {{ color: #6b7280; font-size: 13px; margin-bottom: 20px; }}
-  .summary {{ display: grid; grid-template-columns: repeat(5, 1fr); gap: 12px; margin-bottom: 24px; }}
-  .stat {{ background: #fff; border-radius: 10px; padding: 16px; box-shadow: 0 1px 3px rgba(0,0,0,.06); }}
-  .stat .num {{ font-size: 26px; font-weight: 700; }}
-  .stat .lbl {{ font-size: 12px; color: #6b7280; margin-top: 2px; }}
-  .stat.d .num {{ color: #2563eb; }}
-  .stat.m .num {{ color: #10b981; }}
-  .stat.o .num {{ color: #f59e0b; }}
-  .card {{ background: #fff; border-radius: 12px; box-shadow: 0 1px 4px rgba(0,0,0,.07); margin-bottom: 20px; overflow: hidden; }}
-  .card-h {{ padding: 12px 18px; background: #fafafa; border-bottom: 1px solid #f0f0f0; font-weight: 700; font-size: 15px; display: flex; align-items: center; gap: 8px; }}
-  .card-h .tag {{ font-size: 11px; font-weight: 500; color: #6b7280; background: #f3f4f6; padding: 2px 8px; border-radius: 10px; }}
-  .card-b {{ padding: 16px; }}
-  .chart {{ width: 100%; height: auto; max-width: 920px; }}
-  .barlabel {{ font-size: 12px; fill: #374151; font-weight: 600; }}
-  .rowlabel {{ font-size: 11px; fill: #6b7280; }}
-  .imglabel {{ font-size: 14px; fill: #111; font-weight: 700; }}
-  .imgside {{ font-size: 10px; fill: #9ca3af; font-weight: 400; }}
-  table.cmp {{ width: 100%; border-collapse: collapse; table-layout: fixed; }}
-  table.cmp th {{ font-size: 12px; color: #6b7280; font-weight: 600; padding: 8px; text-align: center; border-bottom: 2px solid #f0f0f0; }}
-  table.cmp td {{ padding: 8px; border-bottom: 1px solid #f6f6f6; vertical-align: top; text-align: center; }}
-  table.cmp th.d, table.cmp td.d {{ background: #eff6ff; }}
-  table.cmp th.m, table.cmp td.m {{ background: #ecfdf5; }}
-  table.cmp th.o, table.cmp td.o {{ background: #fffbeb; }}
-  td.sname {{ font-weight: 600; text-align: left; width: 90px; }}
-  td.sname span {{ display: block; font-size: 11px; color: #9ca3af; font-weight: 400; }}
-  .thumb {{ background: #222; border-radius: 6px; overflow: hidden; margin-bottom: 4px; cursor: zoom-in; }}
-  .thumb img {{ width: 100%; height: 200px; object-fit: contain; display: block; }}
-  .noimg {{ color: #d1d5db; font-size: 12px; padding: 40px 0; }}
-  .meta {{ font-size: 11px; color: #6b7280; }}
-  .err {{ font-size: 10px; color: #dc2626; margin-top: 2px; }}
-  .note {{ background: #fef3c7; border-left: 3px solid #f59e0b; padding: 12px 16px; border-radius: 6px; font-size: 13px; margin-bottom: 20px; }}
-  .legend {{ display: flex; gap: 20px; font-size: 12px; color: #6b7280; margin-bottom: 12px; flex-wrap: wrap; }}
-  .legend span {{ display: inline-flex; align-items: center; gap: 5px; }}
-  .legend i {{ width: 12px; height: 12px; border-radius: 2px; display: inline-block; }}
-  .overlay {{ display: none; position: fixed; inset: 0; background: rgba(0,0,0,.9); z-index: 999; justify-content: center; align-items: center; cursor: zoom-out; padding: 30px; }}
-  .overlay.active {{ display: flex; }}
-  .overlay img {{ max-width: 95%; max-height: 95%; object-fit: contain; border-radius: 4px; }}
-  @media (max-width: 900px) {{
-    .summary {{ grid-template-columns: repeat(2, 1fr); }}
-    .thumb img {{ height: 160px; }}
-  }}
-</style>
-</head>
-<body>
-<div class="wrap">
-  <h1>接口2 生发 · 分辨率对比测试报告</h1>
-  <p class="sub">POST /api/v1/hair/grow · female · 4 张图 × 5 发型 × 3 档分辨率 = {total} 次 · 串行</p>
-
-  <div class="summary">
-    <div class="stat"><div class="num">{ok}/{total}</div><div class="lbl">成功 / 总数</div></div>
-    <div class="stat d"><div class="num">{avg896:.1f}s</div><div class="lbl">默认896 平均</div></div>
-    <div class="stat m"><div class="num">{avg1024:.1f}s</div><div class="lbl">1024 平均 · ×{ratio1024:.2f}</div></div>
-    <div class="stat o"><div class="num">{avg0:.1f}s</div><div class="lbl">原图直送 平均 · ×{ratio0:.2f}</div></div>
-    <div class="stat"><div class="num" style="color:{'#dc2626' if ratio0>1.3 else '#16a34a'}">×{ratio0:.2f}</div><div class="lbl">原图/默认 倍率</div></div>
-  </div>
-
-  <div class="note">
-    <b>结论：</b>{conclusion}
-  </div>
-
-  <div class="card">
-    <div class="card-h">平均耗时对比（按图分组，单位：秒）</div>
-    <div class="card-b">
-      <div class="legend">{legend}</div>
-      {bar_svg()}
-    </div>
-  </div>
-
-  <h2 style="font-size:18px;margin:8px 0 14px">画质对比（左→右：默认896 · 1024 · 原图直送）</h2>
-  {compare_cards()}
-
-</div>
-<div class="overlay" id="overlay" onclick="this.classList.remove('active')">
-  <img id="overlayImg" src="">
-</div>
-<script>
-function openImg(src) {{
-  document.getElementById('overlayImg').src = src;
-  document.getElementById('overlay').classList.add('active');
-}}
-</script>
-</body>
-</html>
-"""
-
-with open(REPORT_HTML, "w") as f:
-    f.write(html_doc)
-# 同步首页
-with open(os.path.join(OUT, "index.html"), "w") as f:
-    f.write(html_doc)
-print(f"已生成: {REPORT_HTML}")
-print(f"图片目录: {OUT}")

image/compare_test/report-server.service

@@ -1,15 +0,0 @@
-[Unit]
-Description=接口2 分辨率对比测试报告 HTTP 服务 (8848)
-After=network-online.target
-Wants=network-online.target
-
-[Service]
-Type=simple
-User=ubuntu
-WorkingDirectory=/home/ubuntu/hair/image/compare_test/out
-ExecStart=/home/ubuntu/miniconda3/envs/my_hair/bin/python -m http.server 8848 --bind 0.0.0.0
-Restart=on-failure
-RestartSec=5
-
-[Install]
-WantedBy=multi-user.target

image/reports/index.html

@@ -1,47 +0,0 @@
-<!DOCTYPE html>
-<html lang="zh-CN">
-<head>
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>测试报告索引</title>
-<style>
-  * { box-sizing: border-box; margin: 0; padding: 0; }
-  body { font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "PingFang SC", "Microsoft YaHei", sans-serif; background: #f3f4f6; color: #1f2937; padding: 40px 20px; }
-  .wrap { max-width: 720px; margin: 0 auto; }
-  h1 { font-size: 24px; margin-bottom: 4px; }
-  .sub { color: #6b7280; font-size: 13px; margin-bottom: 28px; }
-  .card { display: block; background: #fff; border-radius: 12px; box-shadow: 0 1px 4px rgba(0,0,0,.07); padding: 20px 24px; margin-bottom: 14px; text-decoration: none; color: inherit; transition: .15s; border-left: 4px solid #2563eb; }
-  .card:hover { transform: translateX(4px); box-shadow: 0 4px 12px rgba(0,0,0,.1); }
-  .card .title { font-size: 16px; font-weight: 700; margin-bottom: 4px; }
-  .card .desc { font-size: 13px; color: #6b7280; }
-  .card .url { font-size: 11px; color: #9ca3af; margin-top: 6px; font-family: monospace; }
-  .card.wave { border-left-color: #16a34a; }
-  .card.v2 { border-left-color: #f59e0b; }
-  .card.v1 { border-left-color: #2563eb; }
-</style>
-</head>
-<body>
-<div class="wrap">
-  <h1>📊 测试报告索引</h1>
-  <p class="sub">接口2 / 接口5 分辨率对比测试报告合集</p>
-
-  <a class="card wave" href="wave/">
-    <div class="title">💇 wave发型 · 5档分辨率对比（最新）</div>
-    <div class="desc">21图 × 5档(原图/1024/896/768/640) = 105次 · wave发型 · female</div>
-    <div class="url">/wave/</div>
-  </a>
-
-  <a class="card v2" href="v2/">
-    <div class="title">💄 5图 · 5档分辨率对比 v2</div>
-    <div class="desc">5图 × 3发型(花瓣/心形/波浪) × 5档 = 75次 · female</div>
-    <div class="url">/v2/</div>
-  </a>
-
-  <a class="card v1" href="v1/">
-    <div class="title">💄 4图 · 2档分辨率对比 v1</div>
-    <div class="desc">4图 × 5发型 × 2档(默认896/原图) = 40次 · female</div>
-    <div class="url">/v1/</div>
-  </a>
-</div>
-</body>
-</html>

image/reports/report-server.service

@@ -1,15 +0,0 @@
-[Unit]
-Description=测试报告统一HTTP服务 (8850, 路径区分: /wave /v2 /v1)
-After=network-online.target
-Wants=network-online.target
-
-[Service]
-Type=simple
-User=ubuntu
-WorkingDirectory=/home/ubuntu/hair/image/reports
-ExecStart=/home/ubuntu/miniconda3/envs/my_hair/bin/python -m http.server 8850 --bind 0.0.0.0
-Restart=on-failure
-RestartSec=5
-
-[Install]
-WantedBy=multi-user.target

image/reports/v1

@@ -1 +0,0 @@
-/home/ubuntu/hair/image/compare_test/out

image/reports/v2

@@ -1 +0,0 @@
-/home/ubuntu/hair/image/res_test/out

image/reports/wave

@@ -1 +0,0 @@
-/home/ubuntu/hair/image/wave_test/out

image/res_test/batch_test.py

@@ -1,148 +0,0 @@
-#!/usr/bin/env python3
-"""接口2 female 分辨率对比测试 v2: 5图 × 3发型 × 5档 = 75次。
-串行执行，记录耗时与成败，结果图按 图_发型_档位 命名保存。
-支持断点续跑（progress.json）。
-"""
-import base64, csv, json, os, sys, time, io
-import requests
-from PIL import Image
-
-API = "http://127.0.0.1:8187/api/v1/hair/grow"
-TOKEN = "dev-shared-secret-2026"
-TIMEOUT = 600
-OUT = "/home/ubuntu/hair/image/res_test/out"
-PROGRESS = "/home/ubuntu/hair/image/res_test/progress.json"
-
-IMG_DIR = "/home/ubuntu/hair/image"
-IMAGES = [
-    ("girl2", f"{IMG_DIR}/girl_img/girl2.jpg"),
-    ("girl5", f"{IMG_DIR}/girl_img/girl5.jpg"),
-    ("qwer",  f"{IMG_DIR}/qwer.jpg"),
-    ("asdf",  f"{IMG_DIR}/asdf.jpg"),
-    ("girl7", f"{IMG_DIR}/girl_img/girl7.jpg"),
-]
-# female: 2=flower(花瓣) 3=heart(心形) 5=wave(波浪)
-STYLES = [(2, "flower"), (3, "heart"), (5, "wave")]
-# 5档: 原图(0) / 1024 / 896 / 768 / 640
-SIDES = [
-    ("origin",  0),
-    ("s1024",   1024),
-    ("s896",    896),
-    ("s768",    768),
-    ("s640",    640),
-]
-
-def load_progress():
-    if os.path.exists(PROGRESS):
-        try:
-            return json.load(open(PROGRESS))
-        except Exception:
-            pass
-    return {"done": [], "results": []}
-
-def save_progress(prog):
-    json.dump(prog, open(PROGRESS, "w"), ensure_ascii=False, indent=1)
-
-def run_one(img_name, img_path, style_idx, style_key, side_name, side_val):
-    key = f"{img_name}_{style_key}_{side_name}"
-    with open(img_path, "rb") as f:
-        img_b64 = base64.b64encode(f.read()).decode()
-    data = {
-        "image_base64": "data:image/jpeg;base64," + img_b64,
-        "gender": "female",
-        "hair_style": str(style_idx),
-        "redraw_max_side": str(side_val),  # 含原图档=0
-    }
-    t0 = time.time()
-    rec = {"key": key, "img": img_name, "style": style_key, "style_idx": style_idx,
-           "side": side_name, "side_val": side_val, "ok": False,
-           "elapsed": 0.0, "err": "", "out_w": 0, "out_h": 0, "bytes": 0}
-    try:
-        r = requests.post(API, data=data, headers={"X-Internal-Token": TOKEN}, timeout=TIMEOUT)
-        rec["elapsed"] = round(time.time() - t0, 1)
-        d = r.json()
-        if d.get("code") != 0:
-            rec["err"] = f"code={d.get('code')} {d.get('message','')}"[:200]
-            return rec
-        results = (d.get("data") or {}).get("results") or []
-        if not results:
-            rec["err"] = "空结果"
-            return rec
-        it = results[0]
-        grown = it.get("grown_image_base64")
-        if not grown:
-            rec["err"] = "无生发图(重绘失败/OOM?)"
-            return rec
-        raw = base64.b64decode(grown)
-        im = Image.open(io.BytesIO(raw))
-        rec["out_w"], rec["out_h"] = im.size
-        out_path = f"{OUT}/{key}.jpg"
-        with open(out_path, "wb") as fo:
-            fo.write(raw)
-        rec["ok"] = True
-        rec["bytes"] = len(raw)
-    except requests.exceptions.Timeout:
-        rec["elapsed"] = round(time.time() - t0, 1)
-        rec["err"] = f"超时(>{TIMEOUT}s)"
-    except Exception as e:
-        rec["elapsed"] = round(time.time() - t0, 1)
-        rec["err"] = f"{type(e).__name__}: {str(e)[:180]}"
-    return rec
-
-def main():
-    prog = load_progress()
-    done_keys = set(prog["done"])
-    total = len(IMAGES) * len(STYLES) * len(SIDES)
-    print(f"=== 接口2 female 分辨率对比测试 v2 ===")
-    print(f"矩阵: {len(IMAGES)}图 × {len(STYLES)}发型 × {len(SIDES)}档 = {total} 次")
-    print(f"已跳过 {len(done_keys)} 个已完成项\n")
-
-    idx = 0
-    for img_name, img_path in IMAGES:
-        for style_idx, style_key in STYLES:
-            for side_name, side_val in SIDES:
-                idx += 1
-                key = f"{img_name}_{style_key}_{side_name}"
-                if key in done_keys:
-                    print(f"[{idx}/{total}] ⏭  跳过 {key}")
-                    continue
-                print(f"[{idx}/{total}] ▶ {key} (side={side_val})...", end=" ", flush=True)
-                rec = run_one(img_name, img_path, style_idx, style_key, side_name, side_val)
-                prog["results"].append(rec)
-                prog["done"].append(key)
-                save_progress(prog)
-                if rec["ok"]:
-                    print(f"✅ {rec['elapsed']}s  {rec['out_w']}x{rec['out_h']} ({rec['bytes']}B)")
-                else:
-                    print(f"❌ {rec['elapsed']}s  {rec['err']}")
-                time.sleep(2)
-
-    print("\n" + "=" * 70)
-    print("汇总")
-    print("=" * 70)
-    write_report(prog["results"])
-    print(f"\n结果图: {OUT}/")
-    print(f"CSV: {OUT}/report.csv   JSON: {OUT}/report.json")
-
-def write_report(results):
-    with open(f"{OUT}/report.csv", "w", newline="") as f:
-        w = csv.writer(f)
-        w.writerow(["img", "style", "side", "side_val", "ok", "elapsed_s", "out_w", "out_h", "bytes", "err"])
-        for r in results:
-            w.writerow([r["img"], r["style"], r["side"], r["side_val"], r["ok"],
-                        r["elapsed"], r["out_w"], r["out_h"], r["bytes"], r["err"]])
-    json.dump(results, open(f"{OUT}/report.json", "w"), ensure_ascii=False, indent=1)
-
-    # 控制台速度表：按 图×发型 分行，5档列
-    print("\n--- 速度对比（秒）---")
-    print(f"{'图·发型':<18}{'原图0':>8}{'1024':>8}{'896':>8}{'768':>8}{'640':>8}")
-    for img_name, _ in IMAGES:
-        for _, style_key in STYLES:
-            cells = []
-            for side_name, _ in SIDES:
-                r = next((x for x in results if x["img"] == img_name and x["style"] == style_key and x["side"] == side_name), None)
-                cells.append(f"{r['elapsed']}✓" if r and r["ok"] else (f"{r['elapsed']}✗" if r else "-"))
-            print(f"{img_name+'·'+style_key:<18}{cells[0]:>8}{cells[1]:>8}{cells[2]:>8}{cells[3]:>8}{cells[4]:>8}")
-
-if __name__ == "__main__":
-    main()

image/res_test/gen_report.py

@@ -1,204 +0,0 @@
-#!/usr/bin/env python3
-"""生成 v2 分辨率对比报告：每行=原图+5档，按图×发型组织15行。"""
-import json, os, html
-
-OUT = "/home/ubuntu/hair/image/res_test/out"
-REPORT = os.path.join(OUT, "report.html")
-results = json.load(open(os.path.join(OUT, "report.json")))
-
-IMAGES = ["girl2", "girl5", "qwer", "asdf", "girl7"]
-STYLES = [("flower", "花瓣"), ("heart", "心形"), ("wave", "波浪")]
-SIDES = [("origin", "原图直送", 0), ("s1024", "1024", 1024),
-         ("s896", "896", 896), ("s768", "768", 768), ("s640", "640", 640)]
-LONGSIDE = {"girl2": 1082, "girl5": 767, "qwer": 1678, "asdf": 1666, "girl7": 925}
-
-def get(img, style, side):
-    for r in results:
-        if r["img"] == img and r["style"] == style and r["side"] == side:
-            return r
-    return None
-
-# 统计
-total = len(results)
-ok = sum(1 for r in results if r["ok"])
-
-# 每档平均耗时（排除冷启动异常值：girl2_flower_origin=135s 明显是冷启动）
-def avg(side, exclude_first_cold=False):
-    ts = []
-    for r in results:
-        if r["side"] == side and r["ok"]:
-            if exclude_first_cold and r["key"] == "girl2_flower_origin":
-                continue  # 跳过冷启动
-            ts.append(r["elapsed"])
-    return sum(ts) / len(ts) if ts else 0
-
-avg_origin = avg("origin", exclude_first_cold=True)
-avg_1024 = avg("s1024")
-avg_896 = avg("s896")
-avg_768 = avg("s768")
-avg_640 = avg("s640")
-avgs = [("origin", "原图直送", avg_origin, 0),
-        ("s1024", "1024", avg_1024, 1024),
-        ("s896", "896", avg_896, 896),
-        ("s768", "768", avg_768, 768),
-        ("s640", "640", avg_640, 640)]
-
-# 速度色阶：以全部耗时的 min-max 映射颜色（绿→黄→红）
-all_t = sorted([r["elapsed"] for r in results if r["ok"] and r["key"] != "girl2_flower_origin"])
-tmin, tmax = all_t[0], all_t[-1]
-def speed_color(t):
-    if tmax == tmin:
-        return "#16a34a"
-    ratio = (t - tmin) / (tmax - tmin)  # 0=最快(绿) 1=最慢(红)
-    if ratio < 0.33:
-        return "#16a34a"  # 绿
-    elif ratio < 0.66:
-        return "#f59e0b"  # 橙
-    else:
-        return "#dc2626"  # 红
-
-# 表格行
-rows_html = []
-for img in IMAGES:
-    for style_key, style_cn in STYLES:
-        # 原图格
-        orig_cell = (f'<td class="cell orig">'
-                     f'<div class="thumb"><img loading="lazy" src="orig_{img}.jpg" '
-                     f'onclick="openImg(this.src)" alt="原图"></div>'
-                     f'<div class="meta">📷 原图</div></td>')
-        # 5档格
-        side_cells = []
-        for side_name, side_lbl, side_val in SIDES:
-            r = get(img, style_key, side_name)
-            if r and r["ok"]:
-                col = speed_color(r["elapsed"])
-                cell = (f'<td class="cell">'
-                        f'<div class="thumb"><img loading="lazy" src="{r["key"]}.jpg" '
-                        f'onclick="openImg(this.src)" alt="{html.escape(r["key"])}"></div>'
-                        f'<div class="meta"><b style="color:{col}">{r["elapsed"]}s</b> · '
-                        f'{r["out_w"]}×{r["out_h"]}</div></td>')
-            elif r:
-                cell = (f'<td class="cell fail"><div class="thumb noimg">❌</div>'
-                        f'<div class="meta err">{html.escape(r["err"][:30])}</div></td>')
-            else:
-                cell = '<td class="cell fail"><div class="thumb noimg">—</div></td>'
-            side_cells.append(cell)
-        row_label = (f'<td class="rowlabel">'
-                     f'<div class="rimg">{html.escape(img)}</div>'
-                     f'<div class="rs">{html.escape(style_cn)}</div>'
-                     f'<div class="rl">长边 {LONGSIDE[img]}</div></td>')
-        rows_html.append("<tr>" + row_label + orig_cell + "".join(side_cells) + "</tr>")
-
-# 顶部平均耗时卡
-def stat_card(lbl, val, col, sub=""):
-    return (f'<div class="stat" style="border-left:3px solid {col}">'
-            f'<div class="num" style="color:{col}">{val:.1f}s</div>'
-            f'<div class="lbl">{lbl}{sub}</div></div>')
-
-stat_cards = "".join([
-    stat_card("原图直送", avg_origin, "#dc2626", "<br><span class='dim'>排除冷启动</span>"),
-    stat_card("1024", avg_1024, "#f59e0b"),
-    stat_card("896 (默认)", avg_896, "#2563eb"),
-    stat_card("768", avg_768, "#16a34a"),
-    stat_card("640", avg_640, "#0d9488"),
-])
-
-html_doc = f"""<!DOCTYPE html>
-<html lang="zh-CN">
-<head>
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>接口2 分辨率对比测试 v2</title>
-<style>
-  * {{ box-sizing: border-box; margin: 0; padding: 0; }}
-  body {{ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "PingFang SC", "Microsoft YaHei", sans-serif; background: #f3f4f6; color: #1f2937; line-height: 1.5; padding: 16px; }}
-  .wrap {{ max-width: 100%; margin: 0 auto; }}
-  h1 {{ font-size: 22px; margin-bottom: 4px; }}
-  .sub {{ color: #6b7280; font-size: 12px; margin-bottom: 16px; }}
-  .stats {{ display: flex; gap: 10px; margin-bottom: 16px; flex-wrap: wrap; }}
-  .stat {{ background: #fff; border-radius: 8px; padding: 12px 14px; box-shadow: 0 1px 3px rgba(0,0,0,.06); flex: 1; min-width: 110px; }}
-  .stat .num {{ font-size: 22px; font-weight: 700; }}
-  .stat .lbl {{ font-size: 11px; color: #6b7280; margin-top: 2px; }}
-  .stat .dim {{ color: #9ca3af; font-size: 10px; }}
-  .summary-bar {{ background: #fff; border-radius: 8px; padding: 12px 16px; margin-bottom: 16px; box-shadow: 0 1px 3px rgba(0,0,0,.06); font-size: 13px; }}
-  .summary-bar b {{ color: #dc2626; }}
-  .table-wrap {{ overflow-x: auto; background: #fff; border-radius: 10px; box-shadow: 0 1px 4px rgba(0,0,0,.07); }}
-  table {{ border-collapse: collapse; min-width: 100%; }}
-  th, td {{ vertical-align: top; }}
-  thead th {{ position: sticky; top: 0; background: #f9fafb; z-index: 2; padding: 10px 8px; font-size: 12px; color: #374151; border-bottom: 2px solid #e5e7eb; text-align: center; }}
-  thead th.orig-h {{ background: #fef3c7; }}
-  tbody td {{ border-bottom: 1px solid #f3f4f6; padding: 8px; }}
-  tbody tr:hover {{ background: #f9fafb; }}
-  td.rowlabel {{ text-align: left; padding: 8px 12px; position: sticky; left: 0; background: #fff; z-index: 1; min-width: 90px; box-shadow: 2px 0 4px rgba(0,0,0,.04); }}
-  tbody tr:hover td.rowlabel {{ background: #f9fafb; }}
-  .rimg {{ font-weight: 700; font-size: 14px; }}
-  .rs {{ font-size: 12px; color: #6b7280; }}
-  .rl {{ font-size: 10px; color: #9ca3af; margin-top: 2px; }}
-  .cell {{ width: 180px; min-width: 180px; text-align: center; }}
-  .cell.orig {{ width: 180px; background: #fffbeb; }}
-  .thumb {{ background: #1f2937; border-radius: 6px; overflow: hidden; margin-bottom: 4px; cursor: zoom-in; }}
-  .thumb img {{ width: 100%; height: 240px; object-fit: contain; display: block; }}
-  .thumb.noimg {{ color: #d1d5db; font-size: 16px; padding: 100px 0; text-align: center; }}
-  .meta {{ font-size: 11px; color: #6b7280; }}
-  .meta.err {{ color: #dc2626; }}
-  .legend {{ display: inline-flex; gap: 12px; font-size: 11px; color: #6b7280; margin-left: 12px; }}
-  .legend span {{ display: inline-flex; align-items: center; gap: 4px; }}
-  .legend i {{ width: 10px; height: 10px; border-radius: 2px; display: inline-block; }}
-  .overlay {{ display: none; position: fixed; inset: 0; background: rgba(0,0,0,.92); z-index: 999; justify-content: center; align-items: center; cursor: zoom-out; padding: 24px; }}
-  .overlay.active {{ display: flex; }}
-  .overlay img {{ max-width: 96%; max-height: 96%; object-fit: contain; border-radius: 4px; }}
-</style>
-</head>
-<body>
-<div class="wrap">
-  <h1>💄 接口2 生发 · 5档分辨率对比报告</h1>
-  <p class="sub">POST /api/v1/hair/grow · female · 5 图 × 3 发型(花瓣/心形/波浪) × 5 档分辨率 = 75 次 · 串行 · 4090 (24G)</p>
-
-  <div class="stats">
-    <div class="stat" style="border-left:3px solid #16a34a"><div class="num" style="color:#16a34a">{ok}/{total}</div><div class="lbl">成功 / 总数</div></div>
-    {stat_cards}
-  </div>
-
-  <div class="summary-bar">
-    📊 <b>结论：</b>耗时随送图分辨率单调下降。<b>大图(qwer/asdf 长边~1670) 原图直送需 ~27-30s，是 896 档(10s) 的近 3 倍</b>；
-    中小图(girl2/girl5/girl7 长边 767-1082)各档差异较小(6-15s)。
-    <b>4090 24G 全程无 OOM</b>，75/75 成功。<b>画质对比</b>见下表（横向滑动），点击任意图可放大。
-    <span class="legend">
-      <span><i style="background:#16a34a"></i>快(&lt;{tmin+ (tmax-tmin)*0.33:.0f}s)</span>
-      <span><i style="background:#f59e0b"></i>中等</span>
-      <span><i style="background:#dc2626"></i>慢(&gt;{tmin+ (tmax-tmin)*0.66:.0f}s)</span>
-    </span>
-  </div>
-
-  <div class="table-wrap">
-    <table>
-      <thead>
-        <tr>
-          <th>图 · 发型</th>
-          <th class="orig-h">📷 原图</th>
-          <th>原图直送 (0)<br><span class="dim">不缩放</span></th>
-          <th>1024</th>
-          <th>896<br><span class="dim">默认</span></th>
-          <th>768</th>
-          <th>640</th>
-        </tr>
-      </thead>
-      <tbody>
-        {"".join(rows_html)}
-      </tbody>
-    </table>
-  </div>
-</div>
-<div class="overlay" id="overlay" onclick="this.classList.remove('active')"><img id="overlayImg" src=""></div>
-<script>
-function openImg(src) {{
-  document.getElementById('overlayImg').src = src;
-  document.getElementById('overlay').classList.add('active');
-}}
-</script>
-</body>
-</html>
-"""
-with open(REPORT, "w") as f:
-    f.write(html_doc)
-print(f"已生成: {REPORT}")

image/res_test/report-server-v2.service

@@ -1,15 +0,0 @@
-[Unit]
-Description=接口2 5档分辨率对比报告 v2 (8849)
-After=network-online.target
-Wants=network-online.target
-
-[Service]
-Type=simple
-User=ubuntu
-WorkingDirectory=/home/ubuntu/hair/image/res_test/out
-ExecStart=/home/ubuntu/miniconda3/envs/my_hair/bin/python -m http.server 8849 --bind 0.0.0.0
-Restart=on-failure
-RestartSec=5
-
-[Install]
-WantedBy=multi-user.target

image/wave_test/batch_test.py

@@ -1,143 +0,0 @@
-#!/usr/bin/env python3
-"""接口2 female wave发型 分辨率对比测试 v3: 21图 × 5档 = 105次。
-串行执行，记录耗时与成败，结果图按 图_档位 命名保存。支持断点续跑。
-"""
-import base64, csv, json, os, time, io
-import requests
-from PIL import Image
-
-API = "http://127.0.0.1:8187/api/v1/hair/grow"
-TOKEN = "dev-shared-secret-2026"
-TIMEOUT = 600
-OUT = "/home/ubuntu/hair/image/wave_test/out"
-PROGRESS = "/home/ubuntu/hair/image/wave_test/progress.json"
-
-IMG_DIR = "/home/ubuntu/hair/image"
-# 21张图：19张girl_img + asdf + qwer
-IMAGES = []
-for f in sorted(os.listdir(os.path.join(IMG_DIR, "girl_img"))):
-    if f.lower().endswith((".jpg", ".jpeg", ".png")):
-        IMAGES.append((os.path.splitext(f)[0], os.path.join(IMG_DIR, "girl_img", f)))
-IMAGES.append(("asdf", os.path.join(IMG_DIR, "asdf.jpg")))
-IMAGES.append(("qwer", os.path.join(IMG_DIR, "qwer.jpg")))
-
-# wave = female hair_style 5
-STYLE_IDX = 5
-# 5档: 原图(0) / 1024 / 896 / 768 / 640
-SIDES = [
-    ("origin", 0),
-    ("s1024",  1024),
-    ("s896",   896),
-    ("s768",   768),
-    ("s640",   640),
-]
-
-def load_progress():
-    if os.path.exists(PROGRESS):
-        try:
-            return json.load(open(PROGRESS))
-        except Exception:
-            pass
-    return {"done": [], "results": []}
-
-def save_progress(prog):
-    json.dump(prog, open(PROGRESS, "w"), ensure_ascii=False, indent=1)
-
-def run_one(img_name, img_path, side_name, side_val):
-    key = f"{img_name}_{side_name}"
-    with open(img_path, "rb") as f:
-        img_b64 = base64.b64encode(f.read()).decode()
-    data = {
-        "image_base64": "data:image/jpeg;base64," + img_b64,
-        "gender": "female",
-        "hair_style": str(STYLE_IDX),
-        "redraw_max_side": str(side_val),
-    }
-    t0 = time.time()
-    rec = {"key": key, "img": img_name, "side": side_name, "side_val": side_val,
-           "ok": False, "elapsed": 0.0, "err": "", "out_w": 0, "out_h": 0, "bytes": 0}
-    try:
-        r = requests.post(API, data=data, headers={"X-Internal-Token": TOKEN}, timeout=TIMEOUT)
-        rec["elapsed"] = round(time.time() - t0, 1)
-        d = r.json()
-        if d.get("code") != 0:
-            rec["err"] = f"code={d.get('code')} {d.get('message','')}"[:200]
-            return rec
-        results = (d.get("data") or {}).get("results") or []
-        if not results:
-            rec["err"] = "空结果"
-            return rec
-        it = results[0]
-        grown = it.get("grown_image_base64")
-        if not grown:
-            rec["err"] = "无生发图(重绘失败/OOM?)"
-            return rec
-        raw = base64.b64decode(grown)
-        im = Image.open(io.BytesIO(raw))
-        rec["out_w"], rec["out_h"] = im.size
-        out_path = f"{OUT}/{key}.jpg"
-        with open(out_path, "wb") as fo:
-            fo.write(raw)
-        rec["ok"] = True
-        rec["bytes"] = len(raw)
-    except requests.exceptions.Timeout:
-        rec["elapsed"] = round(time.time() - t0, 1)
-        rec["err"] = f"超时(>{TIMEOUT}s)"
-    except Exception as e:
-        rec["elapsed"] = round(time.time() - t0, 1)
-        rec["err"] = f"{type(e).__name__}: {str(e)[:180]}"
-    return rec
-
-def main():
-    prog = load_progress()
-    done_keys = set(prog["done"])
-    total = len(IMAGES) * len(SIDES)
-    print(f"=== 接口2 female wave 分辨率对比测试 v3 ===")
-    print(f"矩阵: {len(IMAGES)}图 × {len(SIDES)}档 = {total} 次 (发型固定 wave)")
-    print(f"已跳过 {len(done_keys)} 个已完成项\n")
-
-    idx = 0
-    for img_name, img_path in IMAGES:
-        for side_name, side_val in SIDES:
-            idx += 1
-            key = f"{img_name}_{side_name}"
-            if key in done_keys:
-                print(f"[{idx}/{total}] ⏭  跳过 {key}")
-                continue
-            print(f"[{idx}/{total}] ▶ {key} (side={side_val})...", end=" ", flush=True)
-            rec = run_one(img_name, img_path, side_name, side_val)
-            prog["results"].append(rec)
-            prog["done"].append(key)
-            save_progress(prog)
-            if rec["ok"]:
-                print(f"✅ {rec['elapsed']}s  {rec['out_w']}x{rec['out_h']} ({rec['bytes']}B)")
-            else:
-                print(f"❌ {rec['elapsed']}s  {rec['err']}")
-            time.sleep(2)
-
-    print("\n" + "=" * 70)
-    print("汇总")
-    print("=" * 70)
-    write_report(prog["results"])
-    print(f"\n结果图: {OUT}/")
-    print(f"CSV: {OUT}/report.csv   JSON: {OUT}/report.json")
-
-def write_report(results):
-    with open(f"{OUT}/report.csv", "w", newline="") as f:
-        w = csv.writer(f)
-        w.writerow(["img", "side", "side_val", "ok", "elapsed_s", "out_w", "out_h", "bytes", "err"])
-        for r in results:
-            w.writerow([r["img"], r["side"], r["side_val"], r["ok"],
-                        r["elapsed"], r["out_w"], r["out_h"], r["bytes"], r["err"]])
-    json.dump(results, open(f"{OUT}/report.json", "w"), ensure_ascii=False, indent=1)
-
-    # 各档平均耗时
-    print("\n--- 各档平均耗时（秒）---")
-    for side_name, side_val in SIDES:
-        ts = [r["elapsed"] for r in results if r["side"] == side_name and r["ok"]]
-        if ts:
-            avg = sum(ts) / len(ts)
-            print(f"  {side_name:8s} (={side_val:>4}): 平均 {avg:5.1f}s  [{min(ts):.1f}~{max(ts):.1f}]  成功 {len(ts)}")
-
-if __name__ == "__main__":
-    main()

image/wave_test/gen_report.py

@@ -1,176 +0,0 @@
-#!/usr/bin/env python3
-"""生成 v3 wave发型 分辨率对比报告：每行=原图+5档，21行；含各档耗时统计。"""
-import json, os, html
-
-OUT = "/home/ubuntu/hair/image/wave_test/out"
-REPORT = os.path.join(OUT, "report.html")
-results = json.load(open(os.path.join(OUT, "report.json")))
-
-SIDES = [("origin", "原图直送", 0), ("s1024", "1024", 1024),
-         ("s896", "896", 896), ("s768", "768", 768), ("s640", "640", 640)]
-
-# 图片顺序（与测试脚本一致）：girl_img 排序 + asdf + qwer
-IMG_DIR = "/home/ubuntu/hair/image"
-IMAGES = []
-for f in sorted(os.listdir(os.path.join(IMG_DIR, "girl_img"))):
-    if f.lower().endswith((".jpg", ".jpeg", ".png")):
-        IMAGES.append(os.path.splitext(f)[0])
-IMAGES.append("asdf")
-IMAGES.append("qwer")
-
-def get(img, side):
-    for r in results:
-        if r["img"] == img and r["side"] == side:
-            return r
-    return None
-
-# 统计
-total = len(results)
-ok = sum(1 for r in results if r["ok"])
-
-# 各档统计
-def avg(side):
-    ts = [r["elapsed"] for r in results if r["side"] == side and r["ok"]]
-    return sum(ts)/len(ts) if ts else 0
-avgs = {s[0]: avg(s[0]) for s in SIDES}
-
-# 速度色阶
-all_t = sorted(r["elapsed"] for r in results if r["ok"])
-tmin, tmax = all_t[0], all_t[-1]
-def speed_color(t):
-    if tmax == tmin: return "#16a34a"
-    ratio = (t - tmin) / (tmax - tmin)
-    if ratio < 0.33: return "#16a34a"
-    elif ratio < 0.66: return "#f59e0b"
-    else: return "#dc2626"
-
-# 表格行
-rows_html = []
-for img in IMAGES:
-    orig_cell = (f'<td class="cell orig">'
-                 f'<div class="thumb"><img loading="lazy" src="orig_{img}.jpg" '
-                 f'onclick="openImg(this.src)" alt="原图"></div>'
-                 f'<div class="meta">📷 原图</div></td>')
-    side_cells = []
-    for side_name, side_lbl, side_val in SIDES:
-        r = get(img, side_name)
-        if r and r["ok"]:
-            col = speed_color(r["elapsed"])
-            cell = (f'<td class="cell">'
-                    f'<div class="thumb"><img loading="lazy" src="{r["key"]}.jpg" '
-                    f'onclick="openImg(this.src)" alt="{html.escape(r["key"])}"></div>'
-                    f'<div class="meta"><b style="color:{col}">{r["elapsed"]}s</b></div></td>')
-        elif r:
-            cell = (f'<td class="cell fail"><div class="thumb noimg">❌</div>'
-                    f'<div class="meta err">{html.escape(r["err"][:30])}</div></td>')
-        else:
-            cell = '<td class="cell fail"><div class="thumb noimg">—</div></td>'
-        side_cells.append(cell)
-    rows_html.append("<tr>" + orig_cell + "".join(side_cells) + "</tr>")
-
-# 各档统计卡
-def stat_card(lbl, val, col, rng=""):
-    return (f'<div class="stat" style="border-left:3px solid {col}">'
-            f'<div class="num" style="color:{col}">{val:.1f}s</div>'
-            f'<div class="lbl">{lbl}{rng}</div></div>')
-
-stat_cards = "".join([
-    stat_card("原图直送(0)", avgs["origin"], "#dc2626"),
-    stat_card("1024", avgs["s1024"], "#f59e0b"),
-    stat_card("896", avgs["s896"], "#2563eb"),
-    stat_card("768", avgs["s768"], "#16a34a"),
-    stat_card("640", avgs["s640"], "#0d9488"),
-])
-
-html_doc = f"""<!DOCTYPE html>
-<html lang="zh-CN">
-<head>
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>wave发型 5档分辨率对比</title>
-<style>
-  * {{ box-sizing: border-box; margin: 0; padding: 0; }}
-  body {{ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "PingFang SC", "Microsoft YaHei", sans-serif; background: #f3f4f6; color: #1f2937; line-height: 1.5; padding: 16px; }}
-  .wrap {{ max-width: 100%; margin: 0 auto; }}
-  h1 {{ font-size: 22px; margin-bottom: 4px; }}
-  .sub {{ color: #6b7280; font-size: 12px; margin-bottom: 16px; }}
-  .stats {{ display: flex; gap: 10px; margin-bottom: 16px; flex-wrap: wrap; }}
-  .stat {{ background: #fff; border-radius: 8px; padding: 12px 14px; box-shadow: 0 1px 3px rgba(0,0,0,.06); flex: 1; min-width: 110px; }}
-  .stat .num {{ font-size: 22px; font-weight: 700; }}
-  .stat .lbl {{ font-size: 11px; color: #6b7280; margin-top: 2px; }}
-  .summary-bar {{ background: #fff; border-radius: 8px; padding: 12px 16px; margin-bottom: 16px; box-shadow: 0 1px 3px rgba(0,0,0,.06); font-size: 13px; }}
-  .summary-bar b {{ color: #dc2626; }}
-  .legend {{ display: inline-flex; gap: 12px; font-size: 11px; color: #6b7280; margin-left: 12px; }}
-  .legend span {{ display: inline-flex; align-items: center; gap: 4px; }}
-  .legend i {{ width: 10px; height: 10px; border-radius: 2px; display: inline-block; }}
-  .table-wrap {{ overflow-x: auto; background: #fff; border-radius: 10px; box-shadow: 0 1px 4px rgba(0,0,0,.07); }}
-  table {{ border-collapse: collapse; min-width: 100%; }}
-  th, td {{ vertical-align: top; }}
-  thead th {{ position: sticky; top: 0; background: #f9fafb; z-index: 2; padding: 10px 8px; font-size: 12px; color: #374151; border-bottom: 2px solid #e5e7eb; text-align: center; }}
-  thead th.orig-h {{ background: #fef3c7; }}
-  tbody td {{ border-bottom: 1px solid #f3f4f6; padding: 8px; }}
-  tbody tr:hover {{ background: #f9fafb; }}
-  .cell {{ width: 200px; min-width: 200px; text-align: center; }}
-  .cell.orig {{ background: #fffbeb; }}
-  .thumb {{ background: #1f2937; border-radius: 6px; overflow: hidden; margin-bottom: 4px; cursor: zoom-in; }}
-  .thumb img {{ width: 100%; height: 260px; object-fit: contain; display: block; }}
-  .thumb.noimg {{ color: #d1d5db; font-size: 16px; padding: 110px 0; text-align: center; }}
-  .meta {{ font-size: 11px; color: #6b7280; }}
-  .meta.err {{ color: #dc2626; }}
-  .overlay {{ display: none; position: fixed; inset: 0; background: rgba(0,0,0,.92); z-index: 999; justify-content: center; align-items: center; cursor: zoom-out; padding: 24px; }}
-  .overlay.active {{ display: flex; }}
-  .overlay img {{ max-width: 96%; max-height: 96%; object-fit: contain; border-radius: 4px; }}
-</style>
-</head>
-<body>
-<div class="wrap">
-  <h1>💇 wave发型 · 5档分辨率对比报告</h1>
-  <p class="sub">POST /api/v1/hair/grow · female · wave(波浪) · 21 图 × 5 档分辨率 = 105 次 · 串行 · 4090 (24G)</p>
-
-  <div class="stats">
-    <div class="stat" style="border-left:3px solid #16a34a"><div class="num" style="color:#16a34a">{ok}/{total}</div><div class="lbl">成功 / 总数</div></div>
-    {stat_cards}
-  </div>
-
-  <div class="summary-bar">
-    📊 <b>结论：</b>耗时随分辨率单调下降。<b>大图(asdf 1666/qwer 1678) 原图直送需 24~26s，是 1024 档(11s) 的 2.3 倍</b>；
-    中小图(≤1024) 各档差异较小(6~13s)，因小图本身不触发缩图。
-    <b>4090 24G 全程无 OOM</b>，105/105 成功。<b>画质对比</b>见下表（横向滑动），点击任意图可放大。
-    <span class="legend">
-      <span><i style="background:#16a34a"></i>快(&lt;{(tmin+(tmax-tmin)*0.33):.0f}s)</span>
-      <span><i style="background:#f59e0b"></i>中等</span>
-      <span><i style="background:#dc2626"></i>慢(&gt;{(tmin+(tmax-tmin)*0.66):.0f}s)</span>
-    </span>
-  </div>
-
-  <div class="table-wrap">
-    <table>
-      <thead>
-        <tr>
-          <th class="orig-h">📷 原图</th>
-          <th>原图直送 (0)<br><span class="dim">不缩放</span></th>
-          <th>1024</th>
-          <th>896<br><span class="dim">默认</span></th>
-          <th>768</th>
-          <th>640</th>
-        </tr>
-      </thead>
-      <tbody>
-        {"".join(rows_html)}
-      </tbody>
-    </table>
-  </div>
-</div>
-<div class="overlay" id="overlay" onclick="this.classList.remove('active')"><img id="overlayImg" src=""></div>
-<script>
-function openImg(src) {{
-  document.getElementById('overlayImg').src = src;
-  document.getElementById('overlay').classList.add('active');
-}}
-</script>
-</body>
-</html>
-"""
-with open(REPORT, "w") as f:
-    f.write(html_doc)
-print(f"已生成: {REPORT}")

image/wave_test/report-server-wave.service

@@ -1,15 +0,0 @@
-[Unit]
-Description=wave发型5档分辨率对比报告 (8850)
-After=network-online.target
-Wants=network-online.target
-
-[Service]
-Type=simple
-User=ubuntu
-WorkingDirectory=/home/ubuntu/hair/image/wave_test/out
-ExecStart=/home/ubuntu/miniconda3/envs/my_hair/bin/python -m http.server 8850 --bind 0.0.0.0
-Restart=on-failure
-RestartSec=5
-
-[Install]
-WantedBy=multi-user.target

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)

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>