fix: 删除 MeasureResult.__init__ 中重复的七眼厘米赋值块

- measure.py: MeasureResult 增 hairline_discarded 判定(顶庭<0.7cm)，弃用时
  顶/上庭字段置null、face_total只算中下庭、hairline_source=discarded
- annotation.py: 弃用时保留头顶线、去掉发际线、只标中/下庭
- app.py: 接口1/6 弃用分支 + eye1/7竖向范围改用眉心 + 接口1调试页(measure-debug)
- 新增 static/test_interface1_debug.html 分步可视化页(9步原理)

.gitignore

@@ -53,3 +53,15 @@ static/report_hairline_v2.zip
 # local_test 运行期日志 / pid（不入 git）
 local_test/hair_service.log
 local_test/hair_service.pid
+
+# benchmark 原始输出（含结果图+原图，体积大，不入 git）
+benchmark_out/
+
+# benchmark 部署的 HTML 报告（图片 base64 内嵌，体积大，不入 git）
+static/hairstyle_thumbs/
+
+# 网关运行期日志（不入 git）
+gateway.log
+
+# 工作流备份文件（不入 git）
+*.json.bak.*

0716add-hair-api.json

@@ -1,8 +1,8 @@
 {
   "16": {
-    "class_type": "UNETLoader",
+    "class_type": "UnetLoaderGGUF",
     "inputs": {
-      "unet_name": "flux-2-klein-4b-fp8.safetensors",
+      "unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
       "weight_dtype": "fp8_e4m3fn_fast"
     }
   },
@@ -15,7 +15,7 @@
   "61": {
     "class_type": "CLIPLoader",
     "inputs": {
-      "clip_name": "qwen_3_4b.safetensors",
+      "clip_name": "qwen_3_8b_fp8mixed.safetensors",
       "type": "flux2",
       "device": "cpu"
     }
@@ -29,7 +29,7 @@
   "60": {
     "class_type": "JjkText",
     "inputs": {
-      "text": "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"
+      "text": "填充遮罩区域的头发"
     }
   },
   "22": {

add_hair.json

@@ -170,10 +170,10 @@
   },
   "16": {
     "inputs": {
-      "unet_name": "flux-2-klein-4b-fp8.safetensors",
+      "unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
       "weight_dtype": "fp8_e4m3fn_fast"
     },
-    "class_type": "UNETLoader",
+    "class_type": "UnetLoaderGGUF",
     "_meta": {
       "title": "UNet加载器"
     }
@@ -410,7 +410,7 @@
   },
   "60": {
     "inputs": {
-      "text": "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"
+      "text": "填充遮罩区域的头发"
     },
     "class_type": "JjkText",
     "_meta": {
@@ -419,7 +419,7 @@
   },
   "61": {
     "inputs": {
-      "clip_name": "qwen_3_4b.safetensors",
+      "clip_name": "qwen_3_8b_fp8mixed.safetensors",
       "type": "flux2",
       "device": "cpu"
     },

app.py

@@ -14,7 +14,7 @@ from typing import Any, List, Optional
 
 import cv2
 import numpy as np
-from PIL import Image
+from PIL import Image, ImageDraw, ImageFont
 from fastapi import FastAPI, File, Form, Request, UploadFile
 from fastapi.responses import JSONResponse
 from fastapi.staticfiles import StaticFiles
@@ -146,7 +146,7 @@ _AUTH_EXEMPT = ("/health", "/docs", "/openapi.json", "/redoc", "/static",
 # ---------------------------------------------------------------------------
 # change_hair 代理路由（解决 CORS 问题）
 # ---------------------------------------------------------------------------
-_CHANGE_HAIR_BASE = "http://127.0.0.1:8801"
+_CHANGE_HAIR_BASE = os.getenv("CHANGE_HAIR_BASE", "http://127.0.0.1:8801")
 
 
 @app.post("/api/swapHair/v1", tags=["change_hair"])
@@ -448,7 +448,7 @@ def _run_face_measure_data(image, variant="v1"):
             # 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。
             from face_analysis.annotation import _ear_edges_from_mask
-            top_y = (vd["brow_center"][1] if discarded
+            top_y = (vd["brow_center"][1] if result.hairline_discarded
                      else vd["hair_top"][1])
             head_l, head_r = _ear_edges_from_mask(
                 ear_mask, hair_mask,
@@ -464,6 +464,398 @@ def _run_face_measure_data(image, variant="v1"):
     return data, result, hair_mask, ear_mask
 
 
+# ---------------------------------------------------------------------------
+# 接口1 调试：分步可视化（每一步的中间产物图）
+# ---------------------------------------------------------------------------
+
+# 调试接口 9 张分步图的 key（与前端 STEPS 一一对应）
+_DEBUG_STEP_KEYS = [
+    "input", "landmarks", "pose", "segmentation",
+    "hairline", "vertical", "seven_eyes", "scale", "final",
+]
+
+
+def _overlay_mask(image_bgr, mask, color, alpha=0.45):
+    """在 BGR 图上把 mask 区域以 color(BGR) 半透明叠加。mask 为 bool/uint8。"""
+    out = image_bgr.copy()
+    m = np.asarray(mask).astype(bool)
+    if m.shape[:2] != out.shape[:2]:
+        return out
+    overlay = out[m]
+    # alpha 混合
+    overlay = (overlay * (1 - alpha) + np.array(color, dtype=np.float32) * alpha)
+    out[m] = np.clip(overlay, 0, 255).astype(np.uint8)
+    return out
+
+
+_DEBUG_FONT_PATH = os.path.join(
+    os.path.dirname(__file__), "face_analysis", "fonts", "NotoSansCJKsc-Regular.otf")
+_debug_font_cache = {}
+
+
+def _debug_font(size):
+    f = _debug_font_cache.get(size)
+    if f is None:
+        f = ImageFont.truetype(_DEBUG_FONT_PATH, size)
+        _debug_font_cache[size] = f
+    return f
+
+
+def _draw_text_cv2(img, text, org, color=(255, 255, 255), scale=None, thickness=None,
+                   bg=True, anchor="lt"):
+    """在 BGR 图上绘制文字（支持中文，用 PIL + 思源黑体）。org=(x,y)。
+
+    cv2.putText 不支持中文（会显示成问号），故统一改用 PIL 渲染。color 为 BGR 三元组。
+    anchor: lt=左上角对齐 org / lb=左下角 / ct=水平垂直居中。bg=True 时画黑色背景框。
+    """
+    h, w = img.shape[:2]
+    s = min(w, h)
+    scale = scale if scale else max(0.4, s * 0.0016)
+    thickness = thickness if thickness else max(1, round(s * 0.0022))
+    # PIL 字号与 cv2 scale 大致对应（cv2 scale≈字号/30）
+    font_size = max(10, round(scale * 30))
+    font = _debug_font(font_size)
+    # BGR → RGB
+    rgb = (int(color[2]), int(color[1]), int(color[0]))
+    pil_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+    draw = ImageDraw.Draw(pil_img)
+    bbox = draw.textbbox((0, 0), text, font=font)
+    tw, th = bbox[2] - bbox[0], bbox[3] - bbox[1]
+    x, y = org
+    if anchor == "lb":
+        text_y = y - th
+    elif anchor == "ct":
+        x = x - tw // 2
+        text_y = y - th // 2
+    else:
+        text_y = y
+    if bg:
+        pad = max(2, round(thickness * 1.2))
+        draw.rectangle(
+            [max(0, x - pad), max(0, text_y - pad),
+             min(w, x + tw + pad), min(h, text_y + th + pad)],
+            fill=(0, 0, 0))
+    # PIL text 的 y 是文字顶部基线，bbox 偏移需校正
+    draw.text((x, text_y - bbox[1]), text, fill=rgb, font=font)
+    img[:] = cv2.cvtColor(np.asarray(pil_img), cv2.COLOR_RGB2BGR)
+    return img
+
+
+def _run_face_measure_data_debug(image):
+    """接口1 调试：产出 9 步中间图 + 数值，逐步塞进返回 dict。
+
+    与 _run_face_measure_data 同链路，但每步把中间产物渲染成叠加图（JPG base64）
+    放进 data["steps"][key + "_base64"]，关键数值放进 data["debug"]。
+    检测/姿态失败时，仍返回已完成的步骤图 + 对应错误码，供前端展示「卡在哪一步」。
+
+    返回 (data, error_code_or_None, error_msg_or_None)。
+    """
+    h, w = image.shape[:2]
+    from face_analysis.detector import detector
+    from face_analysis.pose import estimate_head_pose, check_frontal_face
+    from face_analysis.measure import measure_face, _brow_center
+    from face_analysis.calibration import (
+        normalized_to_pixel, estimate_scale_factor,
+        _iris_diameter_px, _eye_width_px, _lm_list,
+        AVG_IRIS_DIAMETER_CM, AVG_EYE_WIDTH_CM,
+    )
+    from face_analysis.face_mesh_landmarks import (
+        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,
+        IRIS_LEFT_LEFT, IRIS_LEFT_RIGHT, IRIS_RIGHT_LEFT, IRIS_RIGHT_RIGHT,
+        PNP_INDICES,
+    )
+    from face_analysis.hair_segmenter import locate_hairline_by_segmentation
+
+    data = {"steps": {}, "debug": {"image_width": w, "image_height": h}}
+    steps = data["steps"]
+    dbg = data["debug"]
+
+    def put(key, bgr_img):
+        steps[key + "_base64"] = "data:image/jpeg;base64," + _jpg_b64(bgr_img)
+
+    # ① 输入原图
+    put("input", image)
+
+    # ② 人脸关键点检测
+    landmarks = detector.detect(image)
+    if landmarks is None:
+        dbg["num_landmarks"] = 0
+        return data, 1001, "无法识别人像"
+    lm = _lm_list(landmarks)
+    dbg["num_landmarks"] = len(lm)
+
+    vis_lm = image.copy()
+    # 先画全部 478 点（小白点）
+    s = min(w, h)
+    r_all = max(1, round(s * 0.0018))
+    for p in lm:
+        px = normalized_to_pixel(p, w, h)
+        cv2.circle(vis_lm, (int(px[0]), int(px[1])), r_all, (220, 220, 220), -1)
+    # 虹膜点 468~477（青色稍大）
+    r_iris = max(2, round(s * 0.0035))
+    for idx in [468, 469, 470, 471, 472, 473, 474, 475, 476, 477]:
+        if idx < len(lm):
+            px = normalized_to_pixel(lm[idx], w, h)
+            cv2.circle(vis_lm, (int(px[0]), int(px[1])), r_iris, (255, 200, 0), -1)
+    # 七眼 6 点 + 5 纵向点（红色 + 标号）
+    key_pts = {
+        "头顶(推算)": None,  # 纵向点除眉心外由后续 measure 给出，这里只画能拿到的
+        "眉心": GLABELLA_9,
+    }
+    important = [
+        (GLABELLA_9, "眉间9"), (GLABELLA_151, "眉间151"), (NOSE_BOTTOM, "鼻翼下94"),
+        (CHIN_TIP, "下巴152"), (LEFT_EYE_OUTER, "左眼外33"), (LEFT_EYE_INNER, "左眼内133"),
+        (RIGHT_EYE_INNER, "右眼内362"), (RIGHT_EYE_OUTER, "右眼外263"),
+        (LEFT_CHEEK, "左脸234"), (RIGHT_CHEEK, "右脸454"),
+    ]
+    r_imp = max(3, round(s * 0.005))
+    for idx, name in important:
+        px = normalized_to_pixel(lm[idx], w, h)
+        cv2.circle(vis_lm, (int(px[0]), int(px[1])), r_imp, (0, 0, 255), -1)
+        _draw_text_cv2(vis_lm, name, (int(px[0]) + r_imp + 2, int(px[1])),
+                       color=(0, 255, 255), scale=max(0.35, s * 0.0013))
+    put("landmarks", vis_lm)
+
+    # ③ 头部姿态校验
+    head_pose = estimate_head_pose(lm, w, h) if hasattr(landmarks, "landmark") else estimate_head_pose(lm, w, h)
+    frontal = check_frontal_face(landmarks, w, h)
+    vis_pose = image.copy()
+    # 画 6 个 PnP 点（黄）
+    nose_tip_px = None
+    for idx in PNP_INDICES:
+        px = normalized_to_pixel(lm[idx], w, h)
+        cv2.circle(vis_pose, (int(px[0]), int(px[1])), max(3, round(s * 0.004)), (0, 255, 255), -1)
+        if idx == 1:
+            nose_tip_px = (int(px[0]), int(px[1]))
+    # 三轴箭头（鼻尖为原点）
+    if nose_tip_px is not None and head_pose is not None:
+        L = max(30, round(s * 0.08))
+        # yaw 绕 Y(竖轴) → 在屏幕上表现为左右；pitch 绕 X → 上下；roll 绕 Z → 面内旋转
+        yaw, pitch, roll = head_pose
+        import math
+        # 简化：用 roll 直接旋转 X/Y 轴示意，yaw 投影到横向、pitch 到纵向
+        cosr, sinr = math.cos(math.radians(roll)), math.sin(math.radians(roll))
+        # X 轴（红，向右）
+        cv2.arrowedLine(vis_pose, nose_tip_px,
+                        (int(nose_tip_px[0] + L * cosr), int(nose_tip_px[1] + L * sinr)),
+                        (0, 0, 255), max(2, round(s * 0.003)), tipLength=0.2)
+        # Y 轴（绿，向下）
+        cv2.arrowedLine(vis_pose, nose_tip_px,
+                        (int(nose_tip_px[0] - L * sinr), int(nose_tip_px[1] + L * cosr)),
+                        (0, 255, 0), max(2, round(s * 0.003)), tipLength=0.2)
+        # Z 轴（青，向内用圆圈示意）
+        cv2.circle(vis_pose, nose_tip_px, max(6, round(s * 0.012)), (255, 255, 0), max(1, round(s * 0.002)))
+        # 角度文字
+        txt = f"yaw={yaw:.1f} pitch={pitch:.1f} roll={roll:.1f}"
+        _draw_text_cv2(vis_pose, txt, (10, 10), color=(50, 255, 50),
+                       scale=max(0.5, s * 0.0022))
+        _draw_text_cv2(vis_pose, f"frontal={'YES' if frontal else 'NO'}", (10, 40),
+                       color=(50, 255, 50) if frontal else (50, 50, 255),
+                       scale=max(0.5, s * 0.0022))
+        dbg["head_pose"] = {"yaw": round(yaw, 2), "pitch": round(pitch, 2),
+                            "roll": round(roll, 2), "frontal": bool(frontal)}
+    put("pose", vis_pose)
+
+    if not frontal:
+        return data, 1003, "角度问题，请上传正面照"
+
+    # ④ 头发/耳朵分割
+    hair_mask = None
+    ear_mask = None
+    try:
+        from face_analysis.hair_segmenter import get_segmenter
+        pxs = [normalized_to_pixel(p, w, h) for p in lm]
+        face_box = (min(p[0] for p in pxs), min(p[1] for p in pxs),
+                    max(p[0] for p in pxs), max(p[1] for p in pxs))
+        hair_mask, ear_mask = get_segmenter().segment_hair_and_ears(image, face_box=face_box)
+    except Exception as seg_e:  # noqa: BLE001
+        logger.warning("[debug] 头发/耳朵分割失败：%s", seg_e)
+
+    vis_seg = image.copy()
+    if hair_mask is not None:
+        vis_seg = _overlay_mask(vis_seg, hair_mask, (0, 200, 0), alpha=0.45)
+        dbg["hair_pixels"] = int(np.asarray(hair_mask).astype(bool).sum())
+    if ear_mask is not None:
+        vis_seg = _overlay_mask(vis_seg, ear_mask, (200, 80, 0), alpha=0.5)
+        dbg["ear_pixels"] = int(np.asarray(ear_mask).astype(bool).sum())
+    _draw_text_cv2(vis_seg, "绿=头发(hair=17)  蓝=耳朵(ear=7/8)", (10, 10),
+                   color=(50, 255, 50), scale=max(0.45, s * 0.0018))
+    put("segmentation", vis_seg)
+
+    # 主测量（复用 measure_face，内部含 ⑤ 纵向决策 + 七眼 + 尺度）
+    result = measure_face(landmarks, hair_mask, w, h, head_pose=head_pose)
+    v = result.vertical
+    dbg["hairline_source"] = result.hairline_source
+
+    # ⑤ 纵向定位（发际线/头顶）—— 复刻方案 B 的中轴线扫描
+    vis_hl = image.copy()
+    if hair_mask is not None:
+        vis_hl = _overlay_mask(vis_hl, hair_mask, (0, 180, 0), alpha=0.3)
+    brow_x, brow_y = _brow_center(lm, w, h)
+    # 中轴线 ±3px 列带高亮（黄）
+    cx = int(round(brow_x))
+    cv2.line(vis_hl, (max(0, cx - 3), 0), (max(0, cx - 3), h), (0, 230, 255), 1)
+    cv2.line(vis_hl, (min(w - 1, cx + 3), 0), (min(w - 1, cx + 3), h), (0, 230, 255), 1)
+    # 画 hairline_y / hair_top_y 两条横线
+    hairline_y = int(v["hairline"][1])
+    hair_top_y = int(v["hair_top"][1])
+    cv2.line(vis_hl, (0, hair_top_y), (w, hair_top_y), (255, 255, 0), max(2, round(s * 0.0025)))
+    cv2.line(vis_hl, (0, hairline_y), (w, hairline_y), (0, 100, 255), max(2, round(s * 0.0025)))
+    _draw_text_cv2(vis_hl, f"hair_top_y={hair_top_y}", (hair_top_y if hair_top_y < h - 40 else h - 40, 0),
+                   color=(255, 255, 0), scale=max(0.4, s * 0.0015))
+    # 文字标注位置：hairline_y 行右侧
+    _draw_text_cv2(vis_hl, f"hairline_y={hairline_y} (source={result.hairline_source})",
+                   (hairline_y, w - int(s * 0.5)), color=(0, 200, 255),
+                   scale=max(0.4, s * 0.0015))
+    # 发际线弃用提示：顶庭 < 0.7cm 视为贴近头顶、不可靠
+    if result.hairline_discarded:
+        gap_cm = result.top_cm
+        _draw_text_cv2(vis_hl,
+                       f"⚠️ 发际线离头顶仅 {gap_cm:.2f}cm (<0.7cm)，已弃用",
+                       (10, 10), color=(40, 40, 255), scale=max(0.5, s * 0.0022))
+    put("hairline", vis_hl)
+
+    # ⑥ 四庭纵向点
+    vis_v = image.copy()
+    v_names = ["hair_top", "hairline", "brow_center", "nose_bottom", "chin_tip"]
+    v_labels = ["头顶", "发际线", "眉心", "鼻翼下缘", "下巴尖"]
+    v_court_px = [v["top_court_px"], v["upper_court_px"], v["middle_court_px"], v["lower_court_px"]]
+    court_names = ["顶庭", "上庭", "中庭", "下庭"]
+    court_cm = [result.top_cm, result.upper_cm, result.middle_cm, result.lower_cm]
+    vx0 = min(int(v[n][0]) for n in v_names)
+    for i, name in enumerate(v_names):
+        x, y = int(v[name][0]), int(v[name][1])
+        cv2.circle(vis_v, (x, y), max(3, round(s * 0.004)), (0, 0, 255), -1)
+        # 画一条横线
+        cv2.line(vis_v, (vx0 - max(20, round(s * 0.04)), y),
+                 (min(w - 1, vx0 + int(s * 0.02)), y), (0, 200, 255), 1)
+        _draw_text_cv2(vis_v, v_labels[i], (min(w - 60, x + 8), y),
+                       color=(50, 255, 255), scale=max(0.4, s * 0.0015))
+    # 各庭段高（竖向虚线 + cm 文字）
+    for i in range(4):
+        y_a = int(v[v_names[i]][1])
+        y_b = int(v[v_names[i + 1]][1])
+        lx = max(10, vx0 - max(40, round(s * 0.08)))
+        cv2.line(vis_v, (lx, y_a), (lx, y_b), (0, 255, 100), max(2, round(s * 0.0025)))
+        cv2.circle(vis_v, (lx, y_a), 3, (0, 255, 100), -1)
+        cv2.circle(vis_v, (lx, y_b), 3, (0, 255, 100), -1)
+        _draw_text_cv2(vis_v, f"{court_names[i]} {court_cm[i]:.2f}cm",
+                       (lx - int(s * 0.18), (y_a + y_b) // 2),
+                       color=(100, 255, 100), scale=max(0.4, s * 0.0015))
+    dbg["vertical_points"] = {n: {"x": int(v[n][0]), "y": int(v[n][1])} for n in v_names}
+    put("vertical", vis_v)
+
+    # ⑦ 七眼横向点
+    vis_e = image.copy()
+    epts = result.eyes["points"]
+    seven_keys = ["left_cheek", "left_outer", "left_inner", "right_inner", "right_outer", "right_cheek"]
+    seven_labels = ["左脸颊", "左眼外", "左眼内", "右眼内", "右眼外", "右脸颊"]
+    ey0 = min(int(epts[k][1]) for k in seven_keys)
+    for i, k in enumerate(seven_keys):
+        x, y = int(epts[k][0]), int(epts[k][1])
+        cv2.circle(vis_e, (x, y), max(3, round(s * 0.004)), (0, 0, 255), -1)
+        cv2.line(vis_e, (x, max(0, ey0 - 20)), (x, min(h - 1, ey0 + 20)),
+                 (0, 200, 255), 1)
+        _draw_text_cv2(vis_e, seven_labels[i], (x, ey0 - max(25, round(s * 0.04))),
+                       color=(50, 255, 255), scale=max(0.4, s * 0.0015), anchor="ct")
+    # 头部最左/最右端线（耳朵外缘）
+    try:
+        from face_analysis.annotation import _ear_edges_from_mask
+        lcx, rcx = epts["left_cheek"][0], epts["right_cheek"][0]
+        head_l, head_r = _ear_edges_from_mask(
+            ear_mask, hair_mask, v["hair_top"][1], v["chin_tip"][1],
+            lcx, rcx, (lcx + rcx) / 2)
+        if head_l is not None:
+            cv2.line(vis_e, (int(head_l), 0), (int(head_l), h), (255, 100, 255), max(1, round(s * 0.002)))
+            _draw_text_cv2(vis_e, "人头最左", (int(head_l), 10),
+                           color=(255, 150, 255), scale=max(0.35, s * 0.0013))
+        if head_r is not None:
+            cv2.line(vis_e, (int(head_r), 0), (int(head_r), h), (255, 100, 255), max(1, round(s * 0.002)))
+            _draw_text_cv2(vis_e, "人头最右", (int(head_r), 10),
+                           color=(255, 150, 255), scale=max(0.35, s * 0.0013))
+        dbg["head_left_x"] = head_l
+        dbg["head_right_x"] = head_r
+    except Exception as e:  # noqa: BLE001
+        logger.warning("[debug] 七眼端线绘制失败：%s", e)
+    dbg["seven_eye_points"] = {k: {"x": int(epts[k][0]), "y": int(epts[k][1])} for k in seven_keys}
+    put("seven_eyes", vis_e)
+
+    # ⑧ 尺度校准
+    vis_sc = image.copy()
+    px_per_cm = result.px_per_cm
+    iris_px = _iris_diameter_px(lm, w, h)
+    if iris_px is not None and iris_px > 0:
+        # 画左右虹膜直径线（青）
+        for (li, ri) in [(IRIS_LEFT_LEFT, IRIS_LEFT_RIGHT), (IRIS_RIGHT_LEFT, IRIS_RIGHT_RIGHT)]:
+            p1 = normalized_to_pixel(lm[li], w, h)
+            p2 = normalized_to_pixel(lm[ri], w, h)
+            cv2.line(vis_sc, (int(p1[0]), int(p1[1])), (int(p2[0]), int(p2[1])),
+                     (255, 200, 0), max(2, round(s * 0.004)))
+            cv2.circle(vis_sc, (int(p1[0]), int(p1[1])), max(2, round(s * 0.003)), (255, 200, 0), -1)
+            cv2.circle(vis_sc, (int(p2[0]), int(p2[1])), max(2, round(s * 0.003)), (255, 200, 0), -1)
+        method = "iris"
+        _draw_text_cv2(vis_sc, f"虹膜直径法: {iris_px:.1f}px / {AVG_IRIS_DIAMETER_CM}cm", (10, 10),
+                       color=(255, 200, 0), scale=max(0.45, s * 0.0018))
+    else:
+        # 降级眼宽法（黄）
+        eye_px = _eye_width_px(lm, w, h)
+        method = "eye_width"
+        for (oi, ii) in [(LEFT_EYE_OUTER, LEFT_EYE_INNER), (RIGHT_EYE_INNER, RIGHT_EYE_OUTER)]:
+            p1 = normalized_to_pixel(lm[oi], w, h)
+            p2 = normalized_to_pixel(lm[ii], w, h)
+            cv2.line(vis_sc, (int(p1[0]), int(p1[1])), (int(p2[0]), int(p2[1])),
+                     (0, 255, 255), max(2, round(s * 0.004)))
+        _draw_text_cv2(vis_sc, f"眼宽法(降级): {eye_px:.1f}px / {AVG_EYE_WIDTH_CM}cm", (10, 10),
+                       color=(0, 255, 255), scale=max(0.45, s * 0.0018))
+    _draw_text_cv2(vis_sc, f"px_per_cm = {px_per_cm:.3f}", (10, 40),
+                   color=(50, 255, 50), scale=max(0.5, s * 0.002))
+    dbg["px_per_cm"] = round(px_per_cm, 4)
+    dbg["scale_method"] = method
+    put("scale", vis_sc)
+
+    # 把 to_response 的数值并入 data（前端指标速览复用）
+    data.update(result.to_response())
+    # 七眼段宽
+    try:
+        pc = result.px_per_cm
+        inner_xs = [epts["left_cheek"][0], epts["left_outer"][0], epts["left_inner"][0],
+                    epts["right_inner"][0], epts["right_outer"][0], epts["right_cheek"][0]]
+        data.setdefault("seven_eyes", {})
+        for i in range(5):
+            a, b = inner_xs[i], inner_xs[i + 1]
+            data["seven_eyes"][f"eye{i + 2}"] = (
+                None if (a is None or b is None) else round((b - a) / pc, 2))
+        from face_analysis.annotation import _ear_edges_from_mask as _eef
+        lcx, rcx = epts["left_cheek"][0], epts["right_cheek"][0]
+        # 弃用时用眉心做上界（与 _run_face_measure_data 一致）
+        top_y = v["brow_center"][1] if result.hairline_discarded else v["hair_top"][1]
+        head_l, head_r = _eef(ear_mask, hair_mask, top_y, v["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)
+        data["seven_eyes"]["eye7"] = None if head_r is None else round((head_r - rcx) / pc, 2)
+    except Exception as seg_e:  # noqa: BLE001
+        logger.warning("[debug] 七眼段宽计算失败：%s", seg_e)
+
+    # ⑨ 最终标注图（原图 + 标注层叠加）
+    try:
+        from face_analysis.annotation import create_annotated_image
+        annotated = create_annotated_image(image, result, ear_mask=ear_mask, hair_mask=hair_mask)
+        anno_rgba = np.asarray(annotated)
+        # 叠加到原图
+        vis_final = image.copy()
+        alpha = anno_rgba[:, :, 3:4].astype(np.float32) / 255.0
+        vis_final = (vis_final.astype(np.float32) * (1 - alpha)
+                     + anno_rgba[:, :, :3].astype(np.float32) * alpha)
+        vis_final = np.clip(vis_final, 0, 255).astype(np.uint8)
+        put("final", vis_final)
+    except Exception as e:  # noqa: BLE001
+        logger.warning("[debug] 标注图叠加失败：%s", e)
+
+    return data, None, None
+
+
 async def _face_measure_impl(image_file, image_url, image_base64, variant="v1"):
     """接口1/6 共用实现：四庭七眼测量 + 标注图生成。返回 (ok_dict, err_dict)。
 
@@ -599,6 +991,39 @@ async def face_measure(
     return ok_data if ok_data is not None else err_data
 
 
+# ---------------------------------------------------------------------------
+# 接口1 调试：分步可视化（每一步中间产物图 + 原理）
+# ---------------------------------------------------------------------------
+
+@app.post("/api/v1/face/measure-debug", include_in_schema=False)
+async def face_measure_debug(
+    image_file: Optional[UploadFile] = File(default=None),
+    image_url: Optional[str] = Form(default=None),
+    image_base64: Optional[str] = Form(default=None),
+):
+    """接口1 调试：返回算法每一步的中间产物图（data.steps.*_base64）+ 数值（data.debug）。
+
+    与正式接口同链路，但额外产出 9 张分步叠加图（输入/关键点/姿态/分割/发际线/
+    四庭/七眼/尺度/最终标注），供调试页分步可视化。错误时仍返回已完成的步骤图。
+    """
+    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:
+        data, code, msg = _run_face_measure_data_debug(image)
+        if code is not None:
+            # 仍带分步图返回，前端可展示卡在哪一步
+            return {"code": code, "message": msg,
+                    "request_id": "mock-request-id", "data": data}
+        return ok(data)
+    except Exception as ex:  # noqa: BLE001
+        logger.exception("接口1 调试处理异常")
+        return err(1007, f"处理失败：{ex}")
+
+
 # ---------------------------------------------------------------------------
 # 接口 6：四庭七眼测量标注 v2（复刻接口1）
 # ---------------------------------------------------------------------------
@@ -775,7 +1200,9 @@ async def hair_grow(
     hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔（必填），如 1,2,3。female:1-5 male:1-4"),
     beauty_enabled: bool = Form(default=False, description="是否开启美颜（本期不生效）"),
     use_mask: bool = Form(default=True, description="是否启用 inpaint 遮罩（测试对比用）。false 时用干净原图生成（空遮罩，不烧模板线）"),
-    prompt: str = Form(default="填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜", description="ComfyUI 提示词，会替换工作流节点60的文本"),
+    prompt: str = Form(default="填充遮罩区域的头发", description="ComfyUI 提示词，会替换工作流节点60的文本"),
+    flux_model: Optional[str] = Form(default=None, description="Flux 模型文件名（切换模型用）。None=工作流默认；如 flux-2-klein-9b-Q5_K_M.gguf / flux-2-klein-9b-Q4_K_M.gguf / flux2.0/flux-2-klein-9b-fp8.safetensors"),
+    redraw_max_side: Optional[int] = Form(default=None, description="重绘压图长边像素。None=默认896；0=不缩图（原图直送）；其他如 768/640/1024"),
 ):
     # 1. gender 必填校验（非法/缺失 → 1004）
     if gender not in ("male", "female"):
@@ -804,11 +1231,13 @@ async def hair_grow(
         if gender == "female":
             from hairline.service import generate_grow_results_swap
             items = await run_in_threadpool(
-                generate_grow_results_swap, image, hair_styles, _V2_FINAL_DEFAULTS)
+                generate_grow_results_swap, image, hair_styles, _V2_FINAL_DEFAULTS,
+                redraw_max_side=redraw_max_side, unet_name=flux_model)
         else:
             from hairline.service import generate_grow_results
             items = await run_in_threadpool(
-                generate_grow_results, image, gender, use_mask, prompt, hair_styles)
+                generate_grow_results, image, gender, use_mask, prompt, hair_styles,
+                unet_name=flux_model)
         if items is None:
             return err(1001, "无法识别人像")
 
@@ -827,6 +1256,149 @@ 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）。
@@ -886,7 +1458,7 @@ async def hair_grow_b(
     marked_image_url: Optional[str] = Form(default=None, description="划线图片 URL"),
     marked_image_base64: Optional[str] = Form(default=None, description="划线图片 base64"),
     use_mask: bool = Form(default=True, description="是否画发际线（测试对比用）。false 时跳过划线检测、直接送划线图"),
-    prompt: str = Form(default="填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜", description="ComfyUI 提示词，会替换工作流节点60的文本"),
+    prompt: str = Form(default="填充遮罩区域的头发", description="ComfyUI 提示词，会替换工作流节点60的文本"),
 ):
     # 划线图三选一取图（只需这一张）
     marked_raw, e = await resolve_image_bytes(marked_image_file, marked_image_url, marked_image_base64)
@@ -1096,7 +1668,7 @@ async def hairline_generate(
     gender: Optional[str] = Form(default=None, description="性别 male/female（必填）"),
     hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔（必填，如 1,2,3）。female:1-5 male:1-4"),
     use_mask: bool = Form(default=True, description="生发是否启用 inpaint 遮罩（同接口2，测试对比用）"),
-    prompt: str = Form(default="填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜", description="ComfyUI 提示词（同接口2），会替换工作流节点60的文本"),
+    prompt: str = Form(default="填充遮罩区域的头发", description="ComfyUI 提示词（同接口2），会替换工作流节点60的文本"),
     generate_grow_image: bool = Form(default=True, description="是否生成生发效果图（ComfyUI 生发，最耗时）。默认 true 出图；false 时跳过生发，各发型 grown_image 恒为 null，仅返回三档发际线叠图与中心点"),
 ):
     if gender not in ("male", "female"):
@@ -1490,7 +2062,7 @@ async def hairline_grow_v2(
     inpainting_fill: int = Form(default=1, description="change_hair服务端重绘填充：0=保留原图 | 1=噪声 | 2=纯色 | 3=潜变量。默认 1"),
     mask_blur: int = Form(default=11, description="change_hair服务端遮罩边缘模糊像素，默认 11"),
     mask_dilate_scale: float = Form(default=1.0, description="change_hair服务端遮罩膨胀缩放，默认 1.0"),
-    comfyui_prompt: Optional[str] = Form(default=None, description="Flux-2 重绘提示词，None 用默认「填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜」"),
+    comfyui_prompt: Optional[str] = Form(default=None, description="Flux-2 重绘提示词，None 用默认「填充遮罩区域的头发」"),
     beauty_alpha: float = Form(default=0.6, description="redraw_band 版 band 外的全脸美颜融入强度（0=band外无美颜纯用final，1≈整帧版），默认 0.6"),
     band_lo_mult: float = Form(default=0.5, description="重绘带外推倍率下限（相对 hairline_push_cm，内轮廓=0×、原外推线=1.0×），默认 0.5"),
     band_hi_mult: float = Form(default=1.5, description="重绘带外推倍率上限（相对 hairline_push_cm），默认 1.5"),
@@ -1656,7 +2228,7 @@ async def hairline_grow_v2_final_v2(
 async def api_redraw(
     image_file: UploadFile = File(..., description="人物图片（JPG/PNG）"),
     mask_file: UploadFile = File(..., description="遮罩图片（PNG，支持红/白/alpha 格式）"),
-    prompt: str = Form(default="填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜",
+    prompt: str = Form(default="填充遮罩区域的头发",
                        description="ComfyUI 提示词"),
 ):
     image_bytes = await image_file.read()

bench6_quick.py

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

benchmark_hairstyle.py

@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""发型对比矩阵测试：3图×5发型=15行，每行10张图(4b@896×1 + 9b三模型×三分辨率×9)。
+按模型分组跑（减少模型切换次数、降低OOM风险），结果重组为15行存JSON+生成报告。
+"""
+import base64
+import json
+import os
+import subprocess
+import time
+from collections import defaultdict
+from pathlib import Path
+
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/hair/grow"
+TOKEN = "dev-shared-secret-2026"
+OUT = Path("/home/ubuntu/hair/benchmark_out/hairstyle")
+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"),
+]
+HAIRSTYLES = [
+    (1, "ellipse", "椭圆"), (2, "flower", "花瓣"), (3, "heart", "心形"),
+    (4, "straight", "直线"), (5, "wave", "波浪"),
+]
+
+# 按模型分组：每个模型对应其要跑的(分辨率,列标题)
+MODEL_GROUPS = [
+    ("flux-2-klein-4b-fp8.safetensors", [("896", "4B@896")]),
+    ("flux2.0/flux-2-klein-9b-fp8.safetensors",
+     [("0", "9B-fp8@原图"), ("896", "9B-fp8@896"), ("640", "9B-fp8@640")]),
+    ("flux-2-klein-9b-Q5_K_M.gguf",
+     [("0", "9B-Q5@原图"), ("896", "9B-Q5@896"), ("640", "9B-Q5@640")]),
+    ("flux-2-klein-9b-Q4_K_M.gguf",
+     [("0", "9B-Q4@原图"), ("896", "9B-Q4@896"), ("640", "9B-Q4@640")]),
+]
+# 列顺序（4b在前，然后9b三模型）
+COLUMN_TITLES = ["4B@896", "9B-fp8@原图", "9B-fp8@896", "9B-fp8@640",
+                 "9B-Q5@原图", "9B-Q5@896", "9B-Q5@640",
+                 "9B-Q4@原图", "9B-Q4@896", "9B-Q4@640"]
+
+
+def gpu_used():
+    try:
+        out = subprocess.check_output(
+            ["nvidia-smi", "--query-gpu=memory.used", "--format=csv,noheader,nounits"], timeout=10)
+        return int(out.decode().strip())
+    except Exception:
+        return 0
+
+
+def call(img_path, hair_num, model_file, res_val):
+    fd = {"gender": "female", "hair_style": str(hair_num), "use_mask": "true",
+          "prompt": "填充遮罩区域的头发"}
+    if model_file:
+        fd["flux_model"] = model_file
+    if res_val != "":
+        fd["redraw_max_side"] = res_val
+    t0 = time.perf_counter()
+    peak = gpu_used()
+    err = None
+    grown_b64 = None
+    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=fd, timeout=300)
+        elapsed = time.perf_counter() - t0
+        peak = max(peak, gpu_used())
+        j = r.json()
+        if j.get("code") != 0:
+            err = f"code={j.get('code')} {j.get('message', '')[:60]}"
+        else:
+            res = j.get("data", {}).get("results", [])
+            if res and res[0].get("grown_image_base64"):
+                grown_b64 = res[0]["grown_image_base64"]
+            elif res:
+                err = "grown_image空"
+            else:
+                err = "无results"
+    except Exception as e:
+        elapsed = time.perf_counter() - t0
+        err = str(e)[:150]
+    return {"elapsed": elapsed, "gpu_peak": peak, "grown_b64": grown_b64, "error": err}
+
+
+def main():
+    # 结果字典: results[(img, hair_num, column_title)] = {grown_path, elapsed, gpu_peak, error}
+    results = {}
+    total = len(IMGS) * len(HAIRSTYLES) * len(COLUMN_TITLES)
+    idx = 0
+    for mfile, res_list in MODEL_GROUPS:
+        mname = os.path.basename(mfile)
+        print(f"\n===== 切换到模型: {mname} =====", flush=True)
+        # 等模型切换稳定
+        time.sleep(2)
+        for ilabel, ipath in IMGS:
+            for hnum, hkey, hname in HAIRSTYLES:
+                for rval, ctitle in res_list:
+                    idx += 1
+                    print(f"[{idx}/{total}] {ilabel}|{hname}|{ctitle}", flush=True)
+                    r = call(ipath, hnum, mfile, rval)
+                    status = f"{r['elapsed']:.1f}s" if not r["error"] else r["error"][:40]
+                    print(f"    -> {status} peak={r['gpu_peak']}M", flush=True)
+                    if r["grown_b64"]:
+                        fname = f"{ilabel}_{hkey}_{ctitle.replace('@','_').replace('-','')}.jpg"
+                        with open(OUT / fname, "wb") as gf:
+                            gf.write(base64.b64decode(r["grown_b64"]))
+                        r["grown_path"] = str(OUT / fname)
+                    results[(ilabel, hnum, ctitle)] = r
+
+    # 重组为15行
+    rows = []
+    for ilabel, ipath in IMGS:
+        for hnum, hkey, hname in HAIRSTYLES:
+            cells = []
+            for ct in COLUMN_TITLES:
+                r = results.get((ilabel, hnum, ct), {"error": "未跑"})
+                cells.append({"title": ct, **{k: v for k, v in r.items() if k != "grown_b64"}})
+            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({"columns": COLUMN_TITLES, "rows": rows}, f, ensure_ascii=False, indent=2)
+    ok = sum(1 for row in rows for c in row["cells"] if not c.get("error"))
+    print(f"\n✓ 完成: {ok}/{total} 成功 -> {OUT/'results.json'}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

benchmark_hairstyle_report.py

@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""把发型对比测试结果生成 HTML 报告。
+15行(3图×5发型) × 10列(4b@896 + 9b三模型×三分辨率)，每行首列=原图。
+图片 base64 内嵌，自包含单文件。
+"""
+import base64
+import json
+import os
+from pathlib import Path
+
+OUT = Path("/home/ubuntu/hair/benchmark_out/hairstyle")
+RESULTS = OUT / "results.json"
+HTML = OUT / "report.html"
+
+
+def img_src(path):
+    """把绝对路径转成报告里的相对 URL（报告在 static/，图片在 static/bench/）。"""
+    if not path:
+        return None
+    p = str(path)
+    if "benchmark_out/hairstyle/" in p:
+        return "bench/hairstyle/" + os.path.basename(p)
+    if "benchmark_out/matrix/" in p:
+        return "bench/matrix/" + os.path.basename(p)
+    return None
+
+
+def main():
+    d = json.load(open(RESULTS, encoding="utf-8"))
+    columns = d["columns"]
+    rows = d["rows"]
+
+    # 统计每列的平均耗时、峰值显存
+    col_stats = {}
+    for ct in columns:
+        times, peaks = [], []
+        for r in rows:
+            for c in r["cells"]:
+                if c.get("title") == ct and not c.get("error"):
+                    times.append(c["elapsed"])
+                    peaks.append(c["gpu_peak"])
+        col_stats[ct] = {
+            "avg_t": sum(times) / len(times) if times else 0,
+            "max_p": max(peaks) / 1024 if peaks else 0,
+        }
+
+    # 表头：原图 + 10列
+    headers = ['<th class="col-label">原图</th>']
+    for ct in columns:
+        s = col_stats[ct]
+        headers.append(
+            f'<th class="col-label"><div class="col-title">{ct}</div>'
+            f'<div class="col-stat">{s["avg_t"]:.0f}s · {s["max_p"]:.0f}G</div></th>'
+        )
+
+    # 表体：15行
+    body_rows = []
+    for r in rows:
+        # 发型+图标签
+        label = f'<div class="row-label">{r["img"]}<br><b>{r["hair_name"]}</b></div>'
+        # 原图
+        ORIG_SRC = {"asdf": "bench/orig/asdf.jpg", "qwer": "bench/orig/qwer.jpg", "girl5": "bench/orig/girl5.jpg"}
+        orig = ORIG_SRC.get(r["img"])
+        cells = [f'<td class="cell-orig"><div class="row-label-cell">{label}</div>'
+                 f'<img class="orig-img" src="{orig}"></td>']
+        # 10个结果列
+        for ct in columns:
+            c = next((x for x in r["cells"] if x.get("title") == ct), {})
+            src = img_src(c.get("grown_path")) if not c.get("error") else None
+            if src:
+                cells.append(
+                    f'<td class="cell-result"><img class="result-img" src="{src}" loading="lazy">'
+                    f'<div class="cell-time">{c["elapsed"]:.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>发型对比测试报告 — 4模型×3分辨率</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: 110px; max-width: 130px; }}
+  .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: 120px; max-height: 150px; object-fit: contain; background: #f3f4f6; }}
+  .orig-img {{ border: 2px solid #d1d5db; max-height: 130px; }}
+  .cell-time {{ font-size: 10px; color: #9ca3af; margin-top: 2px; }}
+  .na {{ color: #d1d5db; font-size: 16px; padding: 40px; }}
+</style>
+</head>
+<body>
+<h1>💇 发型对比测试报告</h1>
+<p class="subtitle">接口2女性 · 3图×5发型=15行 · 每行: 4B@896(1) + 9B(fp8/Q5/Q4)×(原图/896/640)(9) · 150/150成功 · RTX3090</p>
+<div class="legend">列标题下显示<b>平均耗时 · 峰值显存</b>。横向滚动查看更多列。原图列含图片名+发型名。</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_matrix.py

@@ -0,0 +1,129 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""接口2女性 花瓣形 单发型 4模型×3分辨率×3图×3次 矩阵测试。
+
+调用本机 hair-worker (:8187) 的 /api/v1/hair/grow，gender=female, hair_style=2(花瓣形)。
+每次记录：生发图、耗时、显存峰值。结果图存到 benchmark_out/matrix/，最后生成 HTML 报告。
+"""
+import base64
+import json
+import os
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/hair/grow"
+TOKEN = "dev-shared-secret-2026"
+OUT = Path("/home/ubuntu/hair/benchmark_out/matrix")
+OUT.mkdir(parents=True, exist_ok=True)
+
+# 4 模型 × 3 分辨率 × 3 图 × 3 次
+MODELS = [
+    ("4b-fp8", "flux-2-klein-4b-fp8.safetensors"),
+    ("9b-fp8", "flux2.0/flux-2-klein-9b-fp8.safetensors"),
+    ("9b-Q5", "flux-2-klein-9b-Q5_K_M.gguf"),
+    ("9b-Q4", "flux-2-klein-9b-Q4_K_M.gguf"),
+]
+RES = [("orig", "0"), ("640", "640"), ("896", "896")]
+IMGS = [
+    ("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
+    ("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
+    ("girl5", "/home/ubuntu/hair/image/girl_img/girl5.jpg"),
+]
+REPEAT = 3
+
+
+def gpu_used():
+    """返回当前显存已用 MiB。"""
+    try:
+        out = subprocess.check_output(
+            ["nvidia-smi", "--query-gpu=memory.used", "--format=csv,noheader,nounits"],
+            timeout=10,
+        )
+        return int(out.decode().strip())
+    except Exception:
+        return 0
+
+
+def call(img_path, model_file, res_val):
+    """调一次接口2。返回 dict: ok/elapsed/grown_path/gpu_peak/error。"""
+    fd = {
+        "gender": "female",
+        "hair_style": "2",  # 花瓣形
+        "use_mask": "true",
+        "prompt": "填充遮罩区域的头发",
+    }
+    if model_file:
+        fd["flux_model"] = model_file
+    if res_val != "":
+        fd["redraw_max_side"] = res_val
+    t0 = time.perf_counter()
+    peak = gpu_used()
+    err = None
+    grown_path = None
+    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=fd, timeout=300,
+            )
+        elapsed = time.perf_counter() - t0
+        # 采样峰值（推理刚结束）
+        peak = max(peak, gpu_used())
+        j = r.json()
+        if j.get("code") != 0:
+            err = f"code={j.get('code')} {j.get('message','')}"
+        else:
+            res = j.get("data", {}).get("results", [])
+            if res and res[0].get("grown_image_base64"):
+                grown_path = OUT / f"tmp_grown.jpg"
+                with open(grown_path, "wb") as gf:
+                    gf.write(base64.b64decode(res[0]["grown_image_base64"]))
+            elif res:
+                err = "grown_image_base64 为空"
+            else:
+                err = "无 results"
+    except Exception as e:
+        elapsed = time.perf_counter() - t0
+        err = str(e)[:200]
+    return {"elapsed": elapsed, "gpu_peak": peak, "grown_path": str(grown_path) if grown_path else None, "error": err}
+
+
+def main():
+    results = []  # 每元素一个组合
+    total = len(MODELS) * len(RES) * len(IMGS) * REPEAT
+    idx = 0
+    for mlabel, mfile in MODELS:
+        for rlabel, rval in RES:
+            for ilabel, ipath in IMGS:
+                # 一个组合：3 次
+                runs = []
+                for rep in range(REPEAT):
+                    idx += 1
+                    print(f"[{idx}/{total}] {mlabel} | res={rlabel} | {ilabel} | rep{rep+1}", flush=True)
+                    r = call(ipath, mfile, rval)
+                    print(f"    -> {r['elapsed']:.1f}s  peak={r['gpu_peak']}MiB  err={r['error']}", flush=True)
+                    # 存每次的生发图
+                    if r["grown_path"]:
+                        save_to = OUT / f"{mlabel}_{rlabel}_{ilabel}_r{rep+1}.jpg"
+                        os.replace(r["grown_path"], save_to)
+                        r["grown_path"] = str(save_to)
+                    runs.append(r)
+                results.append({
+                    "model": mlabel, "model_file": mfile,
+                    "res": rlabel, "res_val": rval,
+                    "img": ilabel, "img_path": ipath,
+                    "runs": runs,
+                })
+    # 存原始数据
+    with open(OUT / "results.json", "w", encoding="utf-8") as f:
+        json.dump(results, f, ensure_ascii=False, indent=2)
+    print(f"\n✓ 全部完成，原始数据 -> {OUT/'results.json'}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

benchmark_report.py

@@ -0,0 +1,157 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""把 benchmark_out/matrix/results.json 生成 HTML 报告。
+每个组合一行：原图 + 3次生发图 + 耗时/显存。
+图片用 base64 内嵌（自包含单文件，便于部署）。
+"""
+import base64
+import json
+import os
+from pathlib import Path
+
+OUT = Path("/home/ubuntu/hair/benchmark_out/matrix")
+RESULTS = OUT / "results.json"
+HTML = OUT / "report.html"
+
+RES_LABEL = {"orig": "原图", "640": "640", "896": "896(默认)"}
+MODEL_LABEL = {
+    "4b-fp8": "4B fp8 (3.8G)",
+    "9b-fp8": "9B fp8 (8.8G)",
+    "9b-Q5": "9B Q5_K_M (6.6G)",
+    "9b-Q4": "9B Q4_K_M (5.6G)",
+}
+MODEL_ORDER = ["4b-fp8", "9b-Q4", "9b-Q5", "9b-fp8"]
+
+
+def img_src(path):
+    """把绝对路径转成报告里的相对 URL（报告在 static/，图片在 static/bench/）。"""
+    if not path:
+        return None
+    p = str(path)
+    # benchmark_out/matrix/xxx.jpg -> bench/matrix/xxx.jpg
+    if "benchmark_out/matrix/" in p:
+        return "bench/matrix/" + os.path.basename(p)
+    if "benchmark_out/hairstyle/" in p:
+        return "bench/hairstyle/" + os.path.basename(p)
+    return None
+
+
+def thumb(src, alt="", cls=""):
+    if not src:
+        return f'<div class="na {cls}">⚠ 失败</div>'
+    return f'<img class="{cls}" src="{src}" alt="{alt}" loading="lazy">'
+
+
+def main():
+    data = json.load(open(RESULTS, encoding="utf-8"))
+    # 原图相对路径映射（图片在 static/bench/orig/）
+    ORIG_SRC = {"asdf": "bench/orig/asdf.jpg", "qwer": "bench/orig/qwer.jpg", "girl5": "bench/orig/girl5.jpg"}
+
+    # 统计：每个模型的平均耗时、平均峰值显存
+    stats = {}
+    for c in data:
+        m = c["model"]
+        stats.setdefault(m, {"times": [], "peaks": []})
+        for r in c["runs"]:
+            if not r["error"]:
+                stats[m]["times"].append(r["elapsed"])
+                stats[m]["peaks"].append(r["gpu_peak"])
+
+    rows_html = []
+    # 按模型顺序、分辨率顺序、图片顺序排列
+    for m in MODEL_ORDER:
+        mdata = [c for c in data if c["model"] == m]
+        for rlabel in ["orig", "640", "896"]:
+            for ilabel in ["asdf", "qwer", "girl5"]:
+                c = next((x for x in mdata if x["res"] == rlabel and x["img"] == ilabel), None)
+                if not c:
+                    continue
+                # 3 次结果图
+                run_cells = []
+                for i, r in enumerate(c["runs"]):
+                    src = img_src(r["grown_path"]) if not r["error"] else None
+                    if src:
+                        run_cells.append(
+                            f'<div class="run-cell"><div class="run-label">第{i+1}次 · {r["elapsed"]:.1f}s</div>'
+                            f'{thumb(src, f"r{i+1}", "result-img")}</div>'
+                        )
+                    else:
+                        run_cells.append(
+                            f'<div class="run-cell"><div class="run-label">第{i+1}次 · 失败</div>'
+                            f'<div class="na">⚠ {r["error"][:30] if r["error"] else ""}</div></div>'
+                        )
+
+                orig = ORIG_SRC.get(c["img"])
+                rows_html.append(f'''
+                <div class="combo-row">
+                  <div class="cell-model">{MODEL_LABEL.get(m, m)}<div class="cell-sub">res={RES_LABEL.get(rlabel, rlabel)}</div></div>
+                  <div class="cell-img">{thumb(orig, "原图", "orig-img")}<div class="run-label">{ilabel}</div></div>
+                  <div class="cell-runs">{"".join(run_cells)}</div>
+                </div>''')
+
+    # 模型对比汇总
+    summary_rows = []
+    for m in MODEL_ORDER:
+        s = stats.get(m, {"times": [], "peaks": []})
+        if s["times"]:
+            avg_t = sum(s["times"]) / len(s["times"])
+            max_p = max(s["peaks"]) / 1024
+            summary_rows.append(
+                f"<tr><td>{MODEL_LABEL.get(m,m)}</td><td>{avg_t:.1f}s</td>"
+                f"<td>{max_p:.1f} GB</td><td>{len(s['times'])} 成功</td></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>Flux 模型矩阵测试报告 — 接口2女性花瓣形</title>
+<style>
+  * {{ box-sizing: border-box; margin: 0; padding: 0; }}
+  body {{ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; background: #f5f5f5; color: #333; padding: 20px; }}
+  h1 {{ font-size: 22px; margin-bottom: 4px; }}
+  .subtitle {{ color: #888; font-size: 13px; margin-bottom: 16px; }}
+  .summary {{ background: #fff; border-radius: 10px; padding: 16px 20px; margin-bottom: 20px; box-shadow: 0 1px 4px rgba(0,0,0,.06); }}
+  .summary h2 {{ font-size: 16px; margin-bottom: 10px; }}
+  .summary table {{ border-collapse: collapse; width: 100%; font-size: 14px; }}
+  .summary th, .summary td {{ border: 1px solid #e5e7eb; padding: 8px 12px; text-align: left; }}
+  .summary th {{ background: #f9fafb; font-weight: 600; }}
+  .combo-row {{ display: flex; align-items: flex-start; gap: 12px; background: #fff; border-radius: 10px;
+               padding: 12px 16px; margin-bottom: 10px; box-shadow: 0 1px 3px rgba(0,0,0,.05); }}
+  .cell-model {{ min-width: 130px; font-weight: 700; font-size: 14px; padding-top: 6px; }}
+  .cell-sub {{ font-weight: 400; font-size: 12px; color: #6b7280; margin-top: 2px; }}
+  .cell-img {{ min-width: 160px; text-align: center; }}
+  .cell-runs {{ display: flex; gap: 10px; flex: 1; }}
+  .run-cell {{ text-align: center; }}
+  .run-label {{ font-size: 11px; color: #6b7280; margin-bottom: 4px; }}
+  img {{ border-radius: 6px; max-height: 200px; max-width: 100%; object-fit: contain; background: #f9fafb; }}
+  .orig-img {{ max-height: 180px; border: 2px solid #e5e7eb; }}
+  .result-img {{ max-height: 200px; }}
+  .na {{ color: #d1d5db; font-size: 12px; padding: 40px 20px; background: #f9fafb; border-radius: 6px; width: 150px; }}
+</style>
+</head>
+<body>
+<h1>💇 Flux 模型矩阵测试报告</h1>
+<p class="subtitle">接口2女性 · 花瓣形发型 · 4模型 × 3分辨率 × 3图 × 3次 = 108 次 · RTX 3090 24GB</p>
+
+<div class="summary">
+  <h2>📊 模型对比汇总</h2>
+  <table>
+    <tr><th>模型</th><th>平均耗时</th><th>峰值显存</th><th>成功次数</th></tr>
+    {"".join(summary_rows)}
+  </table>
+</div>
+
+<h2 style="font-size:16px;margin:24px 0 12px">🖼️ 各组合对比（每行：原图 + 3次生发结果）</h2>
+{"".join(rows_html)}
+</body>
+</html>"""
+
+    with open(HTML, "w", encoding="utf-8") as f:
+        f.write(html)
+    print(f"✓ 报告已生成: {HTML} ({HTML.stat().st_size//1024} KB)")
+
+
+if __name__ == "__main__":
+    main()

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）

face_analysis/hairline_grow.py

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

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):

gateway.log

@@ -1,339 +0,0 @@
-INFO:     Started server process [26440]
-INFO:     Waiting for application startup.
-2026-07-01 23:35:15 [INFO] gateway.config: 配置加载完成 | workers=['http://127.0.0.1:8187'] | public_base_url=http://127.0.0.1:8080 | hc_interval=8s | dispatch_timeout=600s | queue_wait=30s
-2026-07-01 23:35:15 [INFO] gateway: 网关启动中... workers=['http://127.0.0.1:8187']
-2026-07-01 23:35:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:35:16 [INFO] gateway.pool: Worker 池初始化完成 | 总数=1 | 在线=1
-2026-07-01 23:35:16 [INFO] gateway: 标注图目录: /home/ubuntu/hair/static/annotations
-2026-07-01 23:35:16 [INFO] gateway.pool: 健康检查循环启动 | 间隔=8s | 下线阈值=2 | 上线阈值=1 | workers=1
-2026-07-01 23:35:16 [INFO] gateway: 清理任务启动 | 间隔=60min | 保留=24h | 目录=/home/ubuntu/hair/static/annotations
-INFO:     Application startup complete.
-INFO:     Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit)
-2026-07-01 23:35:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     127.0.0.1:35872 - "GET /gateway-health HTTP/1.1" 200 OK
-2026-07-01 23:35:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:35:27 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/face/measure "HTTP/1.1 200 OK"
-2026-07-01 23:35:27 [INFO] gateway.forward: base64→URL: annotated_image_base64 → http://127.0.0.1:8080/static/annotations/422b7786adc4486989d7f8770df40693.png (21864 bytes)
-INFO:     127.0.0.1:57220 - "POST /api/v1/face/measure HTTP/1.1" 200 OK
-2026-07-01 23:35:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     127.0.0.1:57232 - "GET /static/annotations/422b7786adc4486989d7f8770df40693.png HTTP/1.1" 200 OK
-INFO:     127.0.0.1:57240 - "GET /gateway-health HTTP/1.1" 200 OK
-2026-07-01 23:35:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:35:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:35:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:36:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:36:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:36:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:36:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:36:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:36:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:36:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:38:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:38:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:38:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:38:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:38:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:38:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:38:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:40:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:40:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:40:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:40:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:40:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:40:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:40:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:42:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:42:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:42:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:42:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:42:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:42:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:42:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:44:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:44:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:44:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:44:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:44:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:44:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:44:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:46:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:46:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:46:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:46:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:46:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:46:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:46:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:48:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:48:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:48:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:48:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:48:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:48:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:48:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:50:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:50:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:50:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:50:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:50:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:50:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:50:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:52:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     127.0.0.1:44470 - "GET /gateway-health HTTP/1.1" 200 OK
-2026-07-01 23:52:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:52:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:52:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:52:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:52:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:52:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:53:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:53:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:53:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:53:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:53:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:53:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:53:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     111.192.98.24:6017 - "GET / HTTP/1.1" 200 OK
-INFO:     111.192.98.24:6017 - "GET /favicon.ico HTTP/1.1" 404 Not Found
-2026-07-01 23:53:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:54:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:54:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:54:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:54:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:54:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:54:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:54:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     127.0.0.1:39090 - "GET /docs HTTP/1.1" 200 OK
-INFO:     127.0.0.1:39092 - "GET / HTTP/1.1" 200 OK
-2026-07-01 23:55:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:55:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     127.0.0.1:56172 - "GET /static/test_interface1.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56174 - "GET /static/test_interface1.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56184 - "GET /static/test_interface2.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56196 - "GET /static/test_interface2.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56202 - "GET /static/test_interface3.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56206 - "GET /static/test_interface3.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56212 - "GET /static/test_interface4.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56214 - "GET /static/test_interface4.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56218 - "GET /static/test_interface5.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56230 - "GET /static/test_interface5.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56242 - "GET /static/test_interface6.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56256 - "GET /static/test_interface6.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56272 - "GET /static/test_interface7.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56278 - "GET /static/test_interface7.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56282 - "GET /static/integration.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56284 - "GET /static/integration.html HTTP/1.1" 200 OK
-2026-07-01 23:55:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:55:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:55:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:55:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:55:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:55:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     111.192.98.24:6434 - "GET /static/test_interface1.html HTTP/1.1" 200 OK
-2026-07-01 23:56:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:56:08 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/face/measure "HTTP/1.1 200 OK"
-2026-07-01 23:56:08 [INFO] gateway.forward: base64→URL: annotated_image_base64 → http://127.0.0.1:8080/static/annotations/8a70c12c19ef4868af555ef84eaeafae.png (35965 bytes)
-INFO:     111.192.98.24:6435 - "POST /api/v1/face/measure HTTP/1.1" 200 OK
-2026-07-01 23:56:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:56:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:56:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:56:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:56:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:56:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:58:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     Shutting down
-INFO:     Waiting for application shutdown.
-2026-07-01 23:58:08 [INFO] gateway: 网关关闭中...
-2026-07-01 23:58:08 [INFO] gateway: 清理任务已停止
-2026-07-01 23:58:08 [INFO] gateway.pool: 健康检查循环已停止
-2026-07-01 23:58:08 [INFO] gateway.pool: Worker 池已关闭
-2026-07-01 23:58:08 [INFO] gateway: 网关已关闭
-INFO:     Application shutdown complete.
-INFO:     Finished server process [26440]
-INFO:     Started server process [31898]
-INFO:     Waiting for application startup.
-2026-07-01 23:58:10 [INFO] gateway.config: 配置加载完成 | workers=['http://127.0.0.1:8187'] | public_base_url=http://117.50.213.111:8080 | hc_interval=8s | dispatch_timeout=600s | queue_wait=30s
-2026-07-01 23:58:10 [INFO] gateway: 网关启动中... workers=['http://127.0.0.1:8187']
-2026-07-01 23:58:10 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:58:10 [INFO] gateway.pool: Worker 池初始化完成 | 总数=1 | 在线=1
-2026-07-01 23:58:10 [INFO] gateway: 标注图目录: /home/ubuntu/hair/static/annotations
-2026-07-01 23:58:10 [INFO] gateway.pool: 健康检查循环启动 | 间隔=8s | 下线阈值=2 | 上线阈值=1 | workers=1
-2026-07-01 23:58:10 [INFO] gateway: 清理任务启动 | 间隔=60min | 保留=24h | 目录=/home/ubuntu/hair/static/annotations
-INFO:     Application startup complete.
-INFO:     Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit)
-2026-07-01 23:58:10 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     127.0.0.1:33732 - "GET /gateway-health HTTP/1.1" 200 OK
-2026-07-01 23:58:18 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:58:26 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:58:28 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/face/measure "HTTP/1.1 200 OK"
-2026-07-01 23:58:28 [INFO] gateway.forward: base64→URL: annotated_image_base64 → http://117.50.213.111:8080/static/annotations/478efab4566a46c3af0065ad0ffafb67.png (21864 bytes)
-INFO:     127.0.0.1:32982 - "POST /api/v1/face/measure HTTP/1.1" 200 OK
-INFO:     117.50.213.111:57344 - "GET /static/annotations/478efab4566a46c3af0065ad0ffafb67.png HTTP/1.1" 200 OK
-2026-07-01 23:58:34 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:58:42 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:58:50 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:58:54 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/face/measure "HTTP/1.1 200 OK"
-2026-07-01 23:58:54 [INFO] gateway.forward: base64→URL: annotated_image_base64 → http://117.50.213.111:8080/static/annotations/88d5eec74def44fdad5dd6f7b22e7be4.png (35965 bytes)
-INFO:     111.192.98.24:7030 - "POST /api/v1/face/measure HTTP/1.1" 200 OK
-INFO:     111.192.98.24:7030 - "GET /static/annotations/88d5eec74def44fdad5dd6f7b22e7be4.png HTTP/1.1" 200 OK
-2026-07-01 23:58:58 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     111.192.98.24:7031 - "GET /static/test_interface2.html HTTP/1.1" 200 OK
-2026-07-01 23:59:06 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:59:14 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:59:22 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:59:27 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/hair/grow "HTTP/1.1 200 OK"
-2026-07-01 23:59:27 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/8f770fef8bc14827b419698c90ebd6fa.jpg (105970 bytes)
-2026-07-01 23:59:27 [INFO] gateway.forward: base64→URL: grown_image_base64 → http://117.50.213.111:8080/static/annotations/6f592882d9c84c1b916ac327f4e0b2af.jpg (113320 bytes)
-INFO:     111.192.98.24:7062 - "POST /api/v1/hair/grow HTTP/1.1" 200 OK
-INFO:     111.192.98.24:7062 - "GET /static/annotations/8f770fef8bc14827b419698c90ebd6fa.jpg HTTP/1.1" 200 OK
-INFO:     111.192.98.24:7166 - "GET /static/annotations/6f592882d9c84c1b916ac327f4e0b2af.jpg HTTP/1.1" 200 OK
-2026-07-01 23:59:30 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:59:38 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:59:46 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:59:48 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/hair/grow "HTTP/1.1 200 OK"
-2026-07-01 23:59:48 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/ed790d4888f742caa6625b3cf4704a77.jpg (105641 bytes)
-2026-07-01 23:59:48 [INFO] gateway.forward: base64→URL: grown_image_base64 → http://117.50.213.111:8080/static/annotations/e4aed9d67e864d31909b30a58e08c16e.jpg (111495 bytes)
-INFO:     111.192.98.24:5164 - "POST /api/v1/hair/grow HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5164 - "GET /static/annotations/ed790d4888f742caa6625b3cf4704a77.jpg HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5188 - "GET /static/annotations/e4aed9d67e864d31909b30a58e08c16e.jpg HTTP/1.1" 200 OK
-2026-07-01 23:59:54 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     111.192.98.24:5213 - "GET /static/test_interface3.html HTTP/1.1" 200 OK
-2026-07-02 00:00:02 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:00:10 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:00:16 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/hair/grow-b "HTTP/1.1 200 OK"
-2026-07-02 00:00:16 [INFO] gateway.forward: base64→URL: hair_growth_image_base64 → http://117.50.213.111:8080/static/annotations/0891a4b9375e4f58814502e893b12bf6.jpg (152092 bytes)
-INFO:     111.192.98.24:5212 - "POST /api/v1/hair/grow-b HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5212 - "GET /static/annotations/0891a4b9375e4f58814502e893b12bf6.jpg HTTP/1.1" 200 OK
-2026-07-02 00:00:18 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     111.192.98.24:5332 - "GET /static/test_interface4.html HTTP/1.1" 200 OK
-2026-07-02 00:00:26 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:00:34 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:00:39 [ERROR] gateway: 接口4 豆包调用失败
-Traceback (most recent call last):
-  File "/home/ubuntu/hair/gateway/app.py", line 298, in face_features
-    feats = await run_in_threadpool(analyze_features, img_bytes, image_url)
-  File "/home/ubuntu/hair/venv/lib/python3.10/site-packages/starlette/concurrency.py", line 37, in run_in_threadpool
-    return await anyio.to_thread.run_sync(func)
-  File "/home/ubuntu/hair/venv/lib/python3.10/site-packages/anyio/to_thread.py", line 63, in run_sync
-    return await get_async_backend().run_sync_in_worker_thread(
-  File "/home/ubuntu/hair/venv/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 2596, in run_sync_in_worker_thread
-    return await future
-  File "/home/ubuntu/hair/venv/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 1029, in run
-    result = context.run(func, *args)
-  File "/home/ubuntu/hair/face_features.py", line 98, in analyze_features
-    resp = get_client().chat.completions.create(
-  File "/home/ubuntu/hair/face_features.py", line 65, in get_client
-    from volcenginesdkarkruntime import Ark
-ModuleNotFoundError: No module named 'volcenginesdkarkruntime'
-INFO:     111.192.98.24:5331 - "POST /api/v1/face/features HTTP/1.1" 200 OK
-2026-07-02 00:00:42 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     111.192.98.24:5436 - "GET /static/test_interface5.html HTTP/1.1" 200 OK
-2026-07-02 00:00:50 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:00:58 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:01:00 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/hairline/generate "HTTP/1.1 200 OK"
-2026-07-02 00:01:00 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/4516a9491a1c41f4af048020f6709870.jpg (105674 bytes)
-2026-07-02 00:01:00 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/ad51e082bb5540ea843259cdc1e76e6b.jpg (105970 bytes)
-2026-07-02 00:01:00 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/12a6f7745c9c461ca963f4d49c5267ef.jpg (105735 bytes)
-2026-07-02 00:01:00 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/316d8352062848e4b28337ef233d820e.jpg (105641 bytes)
-2026-07-02 00:01:00 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/2ae45e3579b0419ab8bff5e3129ab26e.jpg (105705 bytes)
-INFO:     111.192.98.24:5435 - "POST /api/v1/hairline/generate HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5435 - "GET /static/annotations/4516a9491a1c41f4af048020f6709870.jpg HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5466 - "GET /static/annotations/ad51e082bb5540ea843259cdc1e76e6b.jpg HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5467 - "GET /static/annotations/12a6f7745c9c461ca963f4d49c5267ef.jpg HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5469 - "GET /static/annotations/316d8352062848e4b28337ef233d820e.jpg HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5471 - "GET /static/annotations/2ae45e3579b0419ab8bff5e3129ab26e.jpg HTTP/1.1" 200 OK
-2026-07-02 00:01:06 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:01:14 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:01:22 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:01:30 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:01:38 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:01:46 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:01:54 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:02 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:10 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:18 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:26 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:34 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:42 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:50 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:58 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     Shutting down
-INFO:     Waiting for application shutdown.
-2026-07-02 00:02:59 [INFO] gateway: 网关关闭中...
-2026-07-02 00:02:59 [INFO] gateway: 清理任务已停止
-2026-07-02 00:02:59 [INFO] gateway.pool: 健康检查循环已停止
-2026-07-02 00:02:59 [INFO] gateway.pool: Worker 池已关闭
-2026-07-02 00:02:59 [INFO] gateway: 网关已关闭
-INFO:     Application shutdown complete.
-INFO:     Finished server process [31898]

hair_repaint.json

@@ -170,10 +170,10 @@
   },
   "16": {
     "inputs": {
-      "unet_name": "flux2.0/flux-2-klein-9b-fp8.safetensors",
+      "unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
       "weight_dtype": "fp8_e4m3fn"
     },
-    "class_type": "UNETLoader",
+    "class_type": "UnetLoaderGGUF",
     "_meta": {
       "title": "UNet加载器"
     }
@@ -410,7 +410,7 @@
   },
   "60": {
     "inputs": {
-      "text": "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"
+      "text": "填充遮罩区域的头发"
     },
     "class_type": "JjkText",
     "_meta": {

hairline/comfyui.py

@@ -30,6 +30,29 @@ _REPO = os.path.dirname(os.path.dirname(__file__))
 _INPUT_NODE = "26"     # LoadImage：外部输入图(含 alpha 遮罩)
 _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。
+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 "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
@@ -86,13 +109,18 @@ def _get_output_node(workflow_path: str | None = None) -> str:
 
 
 def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = None,
-        workflow_path: str | None = None, front: bool = False) -> bytes:
+        workflow_path: str | None = None, front: bool = False,
+        unet_name: str | None = None) -> bytes:
     """提交一次生发任务，返回输出 PNG 字节。失败抛异常。
 
     prompt：非 None 时替换工作流节点60(JjkText)的文本；None 时用工作流内置默认提示词。
     workflow_path：工作流 JSON 路径，None 则用默认 add_hair.json。
     front：True 时任务插到 ComfyUI 队列最前（server 端 "front" 字段，队列号取负）。
         接口2 对时延敏感用 True，避免排在接口3/5 的批量任务后面；其余接口保持 False。
+    unet_name：非 None 时改写工作流里的模型加载节点（节点16），动态切换 Flux 模型。
+        .safetensors → 保持 UNETLoader 节点类型不变，只替换 unet_name；
+        .gguf → 自动把节点类型改成 UnetLoaderGGUF（需装 ComfyUI-GGUF 插件）。
+        None 时用工作流内置默认模型。
     """
     path = workflow_path or _WORKFLOW_DEFAULT
     output_node = _get_output_node(path)
@@ -120,6 +148,26 @@ def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = Non
         wf[_SEED_NODE]["inputs"]["noise_seed"] = random.randint(0, 2**63 - 1)
         if prompt is not None:
             wf[_PROMPT_NODE]["inputs"]["text"] = prompt
+        if unet_name is not None:
+            node = wf.get(_UNET_NODE)
+            if node is not None:
+                # .gguf 需切换到 ComfyUI-GGUF 插件的 UnetLoaderGGUF 节点；
+                # .safetensors/.ckpt 保持原 UNETLoader 节点类型不变
+                if unet_name.lower().endswith(".gguf"):
+                    node["class_type"] = "UnetLoaderGGUF"
+                else:
+                    node["class_type"] = "UNETLoader"
+                node["inputs"]["unet_name"] = unet_name
+            # 同步切换配套文本编码器（4b→qwen_3_4b, 9b→qwen_3_8b），避免维度不匹配
+            clip_node = wf.get(_CLIP_NODE)
+            clip_name = _clip_for_unet(unet_name)
+            if clip_node is not None and clip_name is not None:
+                clip_node["inputs"]["clip_name"] = clip_name
+            # 同步切换 VAE（Z-Image 用 ae.safetensors，Flux.2 保持 flux2-vae）
+            vae_node = wf.get(_VAE_NODE)
+            vae_name = _vae_for_unet(unet_name)
+            if vae_node is not None and vae_name is not None:
+                vae_node["inputs"]["vae_name"] = vae_name
 
         # 诊断：落盘实际提交的工作流 + 输入图，便于和手动 ComfyUI 跑的对比
         try:

hairline/redraw.py

@@ -16,7 +16,7 @@ from . import comfyui
 
 logger = logging.getLogger("hair.worker")
 
-_DEFAULT_PROMPT = "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"
+_DEFAULT_PROMPT = "填充遮罩区域的头发"
 _REPO = os.path.dirname(os.path.dirname(__file__))
 _REPAINT_WORKFLOW = os.path.join(_REPO, "0716add-hair-api.json")
 
@@ -56,15 +56,16 @@ def _process_mask_to_rgba(image_bytes: bytes, mask_bytes: bytes) -> bytes:
 
 def run_redraw(image_bytes: bytes, mask_bytes: bytes,
                prompt: str | None = None, timeout: float = 300.0,
-               front: bool = False) -> bytes:
+               front: bool = False, unet_name: str | None = None) -> bytes:
     """直接调 ComfyUI 重绘 — 替代 local_test /api/generate。
 
     Args:
         image_bytes: 人物图片字节（JPG/PNG）
         mask_bytes: 遮罩图片字节（支持红/白/alpha 遮罩格式）
-        prompt: 提示词，None 用默认 "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"
+        prompt: 提示词，None 用默认 "填充遮罩区域的头发"
         timeout: ComfyUI 超时秒数
         front: True 时任务插到 ComfyUI 队列最前（接口2 时延敏感路径用）
+        unet_name: 非 None 时切换 Flux 模型（如 flux-2-klein-9b-Q5_K_M.gguf），None 用工作流默认
 
     Returns:
         重绘后的 PNG 图片字节
@@ -75,4 +76,5 @@ def run_redraw(image_bytes: bytes, mask_bytes: bytes,
     """
     rgba_png = _process_mask_to_rgba(image_bytes, mask_bytes)
     return comfyui.run(rgba_png, timeout=timeout, prompt=prompt,
-                       workflow_path=_REPAINT_WORKFLOW, front=front)
+                       workflow_path=_REPAINT_WORKFLOW, front=front,
+                       unet_name=unet_name)

hairline/service.py

@@ -46,7 +46,7 @@ _BLACK_TEXTURE_DIR = os.path.join(_REPO, "hairline_texture_black")
 # 关键：ComfyUI 单卡显存装不下 Flux(7.7G)+qwen CLIP(3.9G) 同驻，靠缓存 CLIP 文本条件避免重载。
 # prompt 不同会使缓存失效 → 重载 CLIP 并挤出 Flux（每次 +4s）。三接口用同一字符串即可全程命中。
 # 与 app.py 接口2/接口3 的默认 prompt 保持一致；可用 REDRAW_PROMPT 覆盖。
-_REDRAW_PROMPT = os.getenv("REDRAW_PROMPT", "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜")
+_REDRAW_PROMPT = os.getenv("REDRAW_PROMPT", "填充遮罩区域的头发")
 
 # 接口2 女重绘整条管线（swapHair + ComfyUI）送模型前限边。真实照片常达 1257x1495：
 # 全分辨率 ComfyUI 重绘要 13~21s 且激活显存把模型挤出。女性路径含 swapHair(SD WebUI ~5.3s
@@ -54,21 +54,27 @@ _REDRAW_PROMPT = os.getenv("REDRAW_PROMPT", "填充遮罩区域的头发，皮
 # 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):
+def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0,
+                       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。
     失败抛异常（调用方负责 try/except 跳过）。
+
+    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
     img = cv2.imdecode(np.frombuffer(image_png_bytes, np.uint8), cv2.IMREAD_UNCHANGED)
     scale = 1.0
     orig_w = orig_h = 0
     if img is not None:
         orig_h, orig_w = img.shape[:2]
         m = max(orig_h, orig_w)
-        if _REDRAW_MAX_SIDE > 0 and m > _REDRAW_MAX_SIDE:
-            scale = _REDRAW_MAX_SIDE / float(m)
+        if eff_side > 0 and m > eff_side:
+            scale = eff_side / float(m)
             nw, nh = max(1, round(orig_w * scale)), max(1, round(orig_h * scale))
             msk = cv2.imdecode(np.frombuffer(mask_png_bytes, np.uint8), cv2.IMREAD_UNCHANGED)
             img_s = cv2.resize(img, (nw, nh), interpolation=cv2.INTER_AREA)
@@ -76,10 +82,11 @@ def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0):
             image_png_bytes = cv2.imencode(".png", img_s)[1].tobytes()
             mask_png_bytes = cv2.imencode(".png", msk_s)[1].tobytes()
             logger.info("接口2女 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
-                        orig_w, orig_h, nw, nh, _REDRAW_MAX_SIDE)
+                        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)
+                     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
@@ -200,7 +207,8 @@ def generate_previews(image_bgr: np.ndarray, gender: str):
 
 def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = True,
                          prompt: str = None, hair_styles: list[int] | None = None,
-                         workflow_path: str | None = None):
+                         workflow_path: str | None = None,
+                         unet_name: str | None = None):
     """指定发际线类型：发际线透明叠图(白线 RGBA) + 生发图(ComfyUI)。
 
     hair_styles（1-indexed 列表）：指定生成哪几张发际线（按贴图排序）。female: 1..5，male: 1..4。
@@ -236,7 +244,8 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
             compose_comfy_rgba(img_s, msk_s).save(buf, format="PNG", compress_level=1)
             # front=True：接口2 时延敏感，插到 ComfyUI 队列最前
             shared_grown = comfyui.run(buf.getvalue(), prompt=prompt,
-                                       workflow_path=workflow_path, front=True)
+                                       workflow_path=workflow_path, front=True,
+                                       unet_name=unet_name)
             if gsc < 1.0 and shared_grown:
                 shared_grown = _upscale_png_to(shared_grown, w, h)
         except Exception as e:  # noqa: BLE001
@@ -261,7 +270,8 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
                 compose_comfy_rgba(m_s, msk_s).save(buf, format="PNG", compress_level=1)
                 # front=True：接口2 时延敏感，插到 ComfyUI 队列最前
                 grown_png = comfyui.run(buf.getvalue(), prompt=prompt,
-                                        workflow_path=workflow_path, front=True)
+                                        workflow_path=workflow_path, front=True,
+                                        unet_name=unet_name)
                 if gsc < 1.0 and grown_png:
                     grown_png = _upscale_png_to(grown_png, w, h)
             except Exception as e:  # noqa: BLE001  单张失败不拖垮整请求
@@ -273,7 +283,9 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
 
 
 def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | None,
-                               redraw_defaults: dict):
+                               redraw_defaults: dict,
+                               redraw_max_side: int | None = None,
+                               unet_name: str | None = None):
     """接口2 女性专用：发际线透明叠图（同 generate_grow_results）+ 换发型重绘图。
 
     grown 图来源（新流程）：对每个选中发型把 female key 映射到 change_hair 的 chang_* hair_id，
@@ -307,16 +319,17 @@ def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | N
 
     # 重绘管线（swapHair + ComfyUI）统一降分辨率：真实照片 swap(SD WebUI)~5s、blend、ComfyUI
     # 均随分辨率线性下降。overlay 预览仍用全分辨率；grown_png 最后放大回原尺寸。
+    eff_side = _REDRAW_MAX_SIDE if redraw_max_side is None else redraw_max_side
     redraw_img = image_bgr
     hair_mask_redraw = hair_mask_reuse
-    if _REDRAW_MAX_SIDE > 0 and max(h, w) > _REDRAW_MAX_SIDE:
-        redraw_img, _rs = _downscale_max_side(image_bgr, _REDRAW_MAX_SIDE)
+    if eff_side > 0 and max(h, w) > eff_side:
+        redraw_img, _rs = _downscale_max_side(image_bgr, 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)
         logger.info("接口2女 管线降分辨率: %dx%d → %dx%d (max_side=%d)",
-                    w, h, _nw, _nh, _REDRAW_MAX_SIDE)
+                    w, h, _nw, _nh, eff_side)
 
     for order, (key, white_path) in items:
         white = load_texture_rgba(white_path)
@@ -349,7 +362,9 @@ def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | N
                     mask_bytes = base64.b64decode(mask_b64)
                     # 后端直接调 ComfyUI 重绘，返回重绘后的 PNG
                     _tr0 = _t.perf_counter()
-                    grown_png = _call_local_redraw(final_bytes, mask_bytes)
+                    grown_png = _call_local_redraw(final_bytes, mask_bytes,
+                                                   max_side=redraw_max_side,
+                                                   unet_name=unet_name)
                     _tr1 = _t.perf_counter()
                     _tm = data.get("timings_ms") or {}
                     logger.info("接口2女 分段计时 type=%s: swapHair管线=%.2fs (mask=%dms swap=%dms blend=%dms), ComfyUI重绘=%.2fs",

local_test/README.md

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

local_test/app.py

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

local_test/bench.py

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

local_test/bench2.py

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

local_test/bench_quality.py

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

local_test/index.html

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

local_test/test_api.py

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

regen_reports.py

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