feat: 接口1/6 标注层字号上调一档 + 眉心改用 9 号点定位

- annotation: 自适应字号系数 0.017→0.020（下限 8→9），标注文字更大更清晰
- measure: _brow_center 只取 FaceMesh 9 号点（眉间上点），不再与 151 取中点

Co-authored-by: Cursor <cursoragent@cursor.com>

.gitignore

@@ -46,5 +46,35 @@ _grow*_resp.json
 # 测试素材图（体积大，不入 git）
 image/test/
 
-# 批量报告输出（200张生成图+原图，体积大，不入 git）
+# 批量报告输出（生成图+原图，体积大，不入 git）
 static/report_hairline_v2/
+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.*
+
+# 分辨率对比测试产物（out/ 下结果图+原图副本+日志，体积大，不入 git）
+# 仅忽略 out/ 与运行期文件；测试脚本与 HTML 报告仍入库
+image/compare_test/out/
+image/res_test/out/
+image/wave_test/out/
+image/compare_test/progress.json
+image/res_test/progress.json
+image/wave_test/progress.json
+image/compare_test/batch_test.log
+image/res_test/batch_test.log
+image/wave_test/batch_test.log
+image/compare_test/http.log

0716add-hair-api.json

@@ -0,0 +1,327 @@
+{
+  "16": {
+    "class_type": "UnetLoaderGGUF",
+    "inputs": {
+      "unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
+      "weight_dtype": "fp8_e4m3fn_fast"
+    }
+  },
+  "3": {
+    "class_type": "VAELoader",
+    "inputs": {
+      "vae_name": "flux2-vae.safetensors"
+    }
+  },
+  "61": {
+    "class_type": "CLIPLoader",
+    "inputs": {
+      "clip_name": "qwen_3_8b_fp8mixed.safetensors",
+      "type": "flux2",
+      "device": "cpu"
+    }
+  },
+  "26": {
+    "class_type": "LoadImage",
+    "inputs": {
+      "image": "placeholder.png"
+    }
+  },
+  "60": {
+    "class_type": "JjkText",
+    "inputs": {
+      "text": "填充遮罩区域的头发"
+    }
+  },
+  "22": {
+    "class_type": "CLIPTextEncode",
+    "inputs": {
+      "clip": [
+        "61",
+        0
+      ],
+      "text": [
+        "60",
+        0
+      ]
+    }
+  },
+  "31": {
+    "class_type": "easy imageSize",
+    "inputs": {
+      "image": [
+        "26",
+        0
+      ]
+    }
+  },
+  "33": {
+    "class_type": "Mask Fill Holes",
+    "inputs": {
+      "masks": [
+        "26",
+        1
+      ]
+    }
+  },
+  "36": {
+    "class_type": "Convert Masks to Images",
+    "inputs": {
+      "masks": [
+        "33",
+        0
+      ]
+    }
+  },
+  "39": {
+    "class_type": "ImageScale",
+    "inputs": {
+      "image": [
+        "36",
+        0
+      ],
+      "upscale_method": "nearest-exact",
+      "width": [
+        "31",
+        0
+      ],
+      "height": [
+        "31",
+        1
+      ],
+      "crop": "disabled"
+    }
+  },
+  "37": {
+    "class_type": "Image To Mask",
+    "inputs": {
+      "image": [
+        "39",
+        0
+      ],
+      "method": "intensity"
+    }
+  },
+  "32": {
+    "class_type": "LayerUtility: ImageScaleByAspectRatio V2",
+    "inputs": {
+      "image": [
+        "26",
+        0
+      ],
+      "mask": [
+        "37",
+        0
+      ],
+      "aspect_ratio": "custom",
+      "proportional_width": [
+        "31",
+        0
+      ],
+      "proportional_height": [
+        "31",
+        1
+      ],
+      "fit": "letterbox",
+      "method": "lanczos",
+      "round_to_multiple": "8",
+      "scale_to_side": "None",
+      "scale_to_length": 1024,
+      "background_color": "#000000"
+    }
+  },
+  "44": {
+    "class_type": "ImageAndMaskPreview",
+    "inputs": {
+      "image": [
+        "32",
+        0
+      ],
+      "mask": [
+        "32",
+        1
+      ],
+      "mask_opacity": 1,
+      "mask_color": "FFFF00",
+      "pass_through": true
+    }
+  },
+  "14": {
+    "class_type": "GetImageSize+",
+    "inputs": {
+      "image": [
+        "44",
+        0
+      ]
+    }
+  },
+  "13": {
+    "class_type": "VAEEncode",
+    "inputs": {
+      "pixels": [
+        "44",
+        0
+      ],
+      "vae": [
+        "3",
+        0
+      ]
+    }
+  },
+  "2": {
+    "class_type": "ModelSamplingFlux",
+    "inputs": {
+      "model": [
+        "16",
+        0
+      ],
+      "max_shift": 1.15,
+      "base_shift": 0.5,
+      "width": [
+        "14",
+        0
+      ],
+      "height": [
+        "14",
+        1
+      ]
+    }
+  },
+  "19": {
+    "class_type": "FluxGuidance",
+    "inputs": {
+      "conditioning": [
+        "22",
+        0
+      ],
+      "guidance": 1
+    }
+  },
+  "5": {
+    "class_type": "ReferenceLatent",
+    "inputs": {
+      "conditioning": [
+        "19",
+        0
+      ],
+      "latent": [
+        "13",
+        0
+      ]
+    }
+  },
+  "7": {
+    "class_type": "EmptySD3LatentImage",
+    "inputs": {
+      "width": [
+        "14",
+        0
+      ],
+      "height": [
+        "14",
+        1
+      ],
+      "batch_size": 1
+    }
+  },
+  "1": {
+    "class_type": "BasicScheduler",
+    "inputs": {
+      "model": [
+        "2",
+        0
+      ],
+      "scheduler": "simple",
+      "steps": 4,
+      "denoise": 1
+    }
+  },
+  "20": {
+    "class_type": "BasicGuider",
+    "inputs": {
+      "model": [
+        "2",
+        0
+      ],
+      "conditioning": [
+        "5",
+        0
+      ]
+    }
+  },
+  "6": {
+    "class_type": "RandomNoise",
+    "inputs": {
+      "noise_seed": 0
+    }
+  },
+  "8": {
+    "class_type": "KSamplerSelect",
+    "inputs": {
+      "sampler_name": "euler"
+    }
+  },
+  "9": {
+    "class_type": "SamplerCustomAdvanced",
+    "inputs": {
+      "noise": [
+        "6",
+        0
+      ],
+      "guider": [
+        "20",
+        0
+      ],
+      "sampler": [
+        "8",
+        0
+      ],
+      "sigmas": [
+        "1",
+        0
+      ],
+      "latent_image": [
+        "7",
+        0
+      ]
+    }
+  },
+  "10": {
+    "class_type": "VAEDecode",
+    "inputs": {
+      "samples": [
+        "9",
+        0
+      ],
+      "vae": [
+        "3",
+        0
+      ]
+    }
+  },
+  "62": {
+    "class_type": "ColorMatch",
+    "inputs": {
+      "image_ref": [
+        "26",
+        0
+      ],
+      "image_target": [
+        "10",
+        0
+      ],
+      "method": "mkl",
+      "strength": 1,
+      "multithread": true
+    }
+  },
+  "17": {
+    "class_type": "SaveImage",
+    "inputs": {
+      "images": [
+        "62",
+        0
+      ],
+      "filename_prefix": "hair_inpaint"
+    }
+  }
+}

add_hair.json

@@ -2,7 +2,7 @@
   "1": {
     "inputs": {
       "scheduler": "simple",
-      "steps": 6,
+      "steps": 4,
       "denoise": 1,
       "model": [
         "2",
@@ -170,10 +170,10 @@
   },
   "16": {
     "inputs": {
-      "unet_name": "flux2.0/flux-2-klein-9b-fp8.safetensors",
-      "weight_dtype": "fp8_e4m3fn"
+      "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": "补充遮罩区补充遮罩区域内的头发，头发填满遮罩区域。发际线往下挡住额头"
+      "text": "填充遮罩区域的头发"
     },
     "class_type": "JjkText",
     "_meta": {
@@ -421,7 +421,7 @@
     "inputs": {
       "clip_name": "qwen_3_8b_fp8mixed.safetensors",
       "type": "flux2",
-      "device": "default"
+      "device": "cpu"
     },
     "class_type": "CLIPLoader",
     "_meta": {

app.py

@@ -14,6 +14,7 @@ from typing import Any, List, Optional
 
 import cv2
 import numpy as np
+from PIL import Image
 from fastapi import FastAPI, File, Form, Request, UploadFile
 from fastapi.responses import JSONResponse
 from fastapi.staticfiles import StaticFiles
@@ -137,7 +138,47 @@ app = FastAPI(
 app.mount("/static", StaticFiles(directory="static"), name="static")
 
 # 不校验鉴权的路径前缀（供网关探测 / 文档 / 静态）
-_AUTH_EXEMPT = ("/health", "/docs", "/openapi.json", "/redoc", "/static", "/api/v1/debug")
+_AUTH_EXEMPT = ("/health", "/docs", "/openapi.json", "/redoc", "/static",
+               "/api/v1/debug", "/api/v1/redraw",
+               "/api/swapHair", "/hairColor")
+
+
+# ---------------------------------------------------------------------------
+# change_hair 代理路由（解决 CORS 问题）
+# ---------------------------------------------------------------------------
+_CHANGE_HAIR_BASE = os.getenv("CHANGE_HAIR_BASE", "http://127.0.0.1:8801")
+
+
+@app.post("/api/swapHair/v1", tags=["change_hair"])
+async def proxy_swap_hair(request: Request):
+    """代理转发到 change_hair /api/swapHair/v1（换发型）"""
+    try:
+        import httpx
+        body = await request.body()
+        async with httpx.AsyncClient(timeout=300.0) as client:
+            resp = await client.post(f"{_CHANGE_HAIR_BASE}/api/swapHair/v1",
+                                    content=body,
+                                    headers={"Content-Type": "application/json"})
+            return JSONResponse(content=resp.json(), status_code=resp.status_code)
+    except Exception as e:
+        logger.exception("代理 swapHair 失败")
+        return err(1007, f"换发型服务异常：{e}")
+
+
+@app.post("/hairColor/v2", tags=["change_hair"])
+async def proxy_hair_color(request: Request):
+    """代理转发到 change_hair /hairColor/v2（换发色）"""
+    try:
+        import httpx
+        body = await request.body()
+        async with httpx.AsyncClient(timeout=300.0) as client:
+            resp = await client.post(f"{_CHANGE_HAIR_BASE}/hairColor/v2",
+                                    content=body,
+                                    headers={"Content-Type": "application/json"})
+            return JSONResponse(content=resp.json(), status_code=resp.status_code)
+    except Exception as e:
+        logger.exception("代理 hairColor 失败")
+        return err(1007, f"换发色服务异常：{e}")
 
 
 @app.middleware("http")
@@ -229,6 +270,20 @@ def _jpg_b64(bgr) -> str:
     return base64.b64encode(buf.tobytes()).decode()
 
 
+def _rgba_png_b64(rgba) -> str:
+    """(H,W,4) float32/uint8 RGBA 透明层 → PNG base64（保留 alpha 通道）。
+
+    用于发际线叠图/预览图：只含发际线曲线像素、背景透明，前端叠加到原图上显示。
+    """
+    arr = np.asarray(rgba)
+    if arr.dtype != np.uint8:
+        arr = np.clip(arr, 0, 255).astype(np.uint8)
+    img = Image.fromarray(arr, mode="RGBA")
+    buf = BytesIO()
+    img.save(buf, format="PNG")
+    return base64.b64encode(buf.getvalue()).decode()
+
+
 def _png_to_jpg_b64(png_bytes) -> str:
     """ComfyUI 返回的 PNG 字节 → 重编码为 JPG base64；无法解码则原样透传。"""
     img = cv2.imdecode(np.frombuffer(png_bytes, np.uint8), cv2.IMREAD_COLOR)
@@ -310,6 +365,105 @@ class ImageJsonBody(BaseModel):
 # ---------------------------------------------------------------------------
 
 
+def _run_face_measure_data(image, variant="v1"):
+    """接口1测量数值核心：detect → 姿态校验 → 头发/耳朵分割 → measure_face → eye1~eye7。
+
+    返回 (data_dict, result, hair_mask, ear_mask)，或检测/姿态失败时返回 None。
+    不含标注图（annotated_image_*），供接口5 容错复用；接口1 调用后再自行生成标注图。
+    任何分割/七眼计算异常均内部吞掉（七眼相关字段不出现），不影响主测量结果。
+
+    variant="v1"（接口1/接口5）：完整四庭七眼 + eye1~eye7（含头部端线段）。
+    variant="v6"（接口6）：去顶庭、不画端线、无 eye1~eye7。
+    """
+    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
+
+    landmarks = detector.detect(image)
+    if landmarks is None:
+        return None
+    if not check_frontal_face(landmarks, w, h):
+        return None
+    head_pose = estimate_head_pose(landmarks, w, h)
+
+    # 头发/耳朵分割（方案 B，单次推理），失败传 None 由 measure 内部回退方案 A。
+    hair_mask = None
+    ear_mask = None
+    try:
+        from face_analysis.hair_segmenter import get_segmenter
+        from face_analysis.calibration import normalized_to_pixel
+        pxs = [normalized_to_pixel(p, w, h) for p in landmarks.landmark]
+        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("头发/耳朵分割失败，回退方案A：%s", seg_e)
+
+    result = measure_face(landmarks, hair_mask, w, h, head_pose=head_pose)
+    discarded = result.hairline_discarded
+    data = result.to_response()
+    vd = result.vertical
+    if variant == "v6":
+        if discarded:
+            # 发际线弃用：接口6 的上庭也依赖发际线，一并置 null；只保留中/下庭。
+            base_px = vd["middle_court_px"] + vd["lower_court_px"]
+            data["four_courts"]["upper_court_cm"] = None
+            data["four_courts"]["ratios"] = {
+                "upper_court": None,
+                "middle_court": round(vd["middle_court_px"] / base_px, 3),
+                "lower_court": round(vd["lower_court_px"] / base_px, 3),
+            }
+            data["four_courts"].pop("top_court_cm", None)
+            data["face_total_height_cm"] = round(
+                result.middle_cm + result.lower_cm, 2)
+            data["landmarks"]["hairline"] = None
+        else:
+            base_px = vd["upper_court_px"] + vd["middle_court_px"] + vd["lower_court_px"]
+            # 接口6 是三庭：去掉顶庭相关字段（top_court_cm / ratios.top_court / landmarks.hair_top）
+            data["four_courts"]["ratios"] = {
+                "upper_court": round(vd["upper_court_px"] / base_px, 3),
+                "middle_court": round(vd["middle_court_px"] / base_px, 3),
+                "lower_court": round(vd["lower_court_px"] / base_px, 3),
+            }
+            data["four_courts"].pop("top_court_cm", None)
+            data["face_total_height_cm"] = round(
+                result.upper_cm + result.middle_cm + result.lower_cm, 2)
+            # 注：landmarks.hair_top 保留返回（供前端/下游定位头顶），但顶庭数值、
+            # 占比、标注图仍按三庭处理，显示效果不变。
+
+    # 七眼段宽度（cm）。eye1=左耳外段 eye2=左脸颊 eye3=左眼 eye4=两眼间距 eye5=右眼 eye6=右脸颊 eye7=右耳外段。
+    # eye2~eye6（5段）只用内部分点，接口1/6 共用；eye1/eye7 需耳朵分割端线，仅接口1 有。
+    try:
+        epts = result.eyes["points"]
+        lcx, rcx = epts["left_cheek"][0], epts["right_cheek"][0]
+        pc = result.px_per_cm
+        inner_xs = [lcx, epts["left_outer"][0], epts["left_inner"][0],
+                    epts["right_inner"][0], epts["right_outer"][0], rcx]
+        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))
+        if variant != "v6":
+            # 接口1 额外算 eye1/eye7（左/右耳外段），需耳朵分割端线。
+            # 竖向范围：发际线弃用时用眉心做上界（hair_top 不可靠），否则用头顶。
+            from face_analysis.annotation import _ear_edges_from_mask
+            top_y = (vd["brow_center"][1] if discarded
+                     else vd["hair_top"][1])
+            head_l, head_r = _ear_edges_from_mask(
+                ear_mask, hair_mask,
+                top_y, vd["chin_tip"][1],
+                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("七眼段宽度计算失败：%s", seg_e)
+
+    return data, result, hair_mask, ear_mask
+
+
 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)。
 
@@ -326,60 +480,24 @@ async def _face_measure_impl(image_file, image_url, image_base64, variant="v1"):
     if image is None:
         return None, err(1008, "图片格式不支持（仅 JPG / PNG）")
 
-    h, w = image.shape[:2]
-
     try:
-        from face_analysis.detector import detector
-        from face_analysis.pose import estimate_head_pose, check_frontal_face
-        from face_analysis.measure import measure_face
-        from face_analysis.annotation import create_annotated_image
-
-        # 5. 人脸检测
-        landmarks = detector.detect(image)
-        if landmarks is None:
-            return None, err(1001, "无法识别人像")
-
-        # 6. 姿态校验
-        if not check_frontal_face(landmarks, w, h):
+        ret = _run_face_measure_data(image, variant=variant)
+        if ret is None:
+            # 区分错误码：未检出人脸 vs 非正面。重新检测一次以判断。
+            from face_analysis.detector import detector
+            from face_analysis.pose import check_frontal_face
+            if detector.detect(image) is None:
+                return None, err(1001, "无法识别人像")
             return None, err(1003, "角度问题，请上传正面照")
-        head_pose = estimate_head_pose(landmarks, w, h)
+        data, result, hair_mask, ear_mask = ret
 
-        # 7. 头发/耳朵分割（方案 B，单次推理），失败传 None 由 measure 内部回退方案 A。
-        #    传人脸包围盒 → 先按人脸裁剪再分割（全身/街拍等脸偏小的图也能稳出耳朵）。
-        hair_mask = None
-        ear_mask = None
-        try:
-            from face_analysis.hair_segmenter import get_segmenter
-            from face_analysis.calibration import normalized_to_pixel
-            pxs = [normalized_to_pixel(p, w, h) for p in landmarks.landmark]
-            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("头发/耳朵分割失败，回退方案A：%s", seg_e)
-
-        # 8. 测量 + 标注图（hair_mask 定发际线/头顶；ear_mask 定人头最左/最右竖线）
-        result = measure_face(landmarks, hair_mask, w, h, head_pose=head_pose)
+        # 标注图（接口1/6 才需要；接口5 复用 _run_face_measure_data 时不生成）
+        from face_analysis.annotation import create_annotated_image
         annotated = create_annotated_image(
             image, result, ear_mask=ear_mask, hair_mask=hair_mask, variant=variant)
         buf = BytesIO()
         annotated.save(buf, format="PNG")
 
-        # 9. 拆分架构：返回 base64，不落盘不拼 URL（落盘改 URL 由网关完成）
-        data = result.to_response()
-        if variant == "v6":
-            # 接口6：数据去顶庭（重算三庭比例与总高，移除顶庭/头顶字段）
-            vd = result.vertical
-            base_px = vd["upper_court_px"] + vd["middle_court_px"] + vd["lower_court_px"]
-            data["four_courts"]["ratios"] = {
-                "upper_court": round(vd["upper_court_px"] / base_px, 3),
-                "middle_court": round(vd["middle_court_px"] / base_px, 3),
-                "lower_court": round(vd["lower_court_px"] / base_px, 3),
-            }
-            data["four_courts"].pop("top_court_cm", None)
-            data["face_total_height_cm"] = round(
-                result.upper_cm + result.middle_cm + result.lower_cm, 2)
-            data["landmarks"].pop("hair_top", None)
         data["annotated_image_base64"] = base64.b64encode(buf.getvalue()).decode()
         return ok(data), None
     except Exception as ex:  # noqa: BLE001
@@ -409,7 +527,7 @@ async def _face_measure_impl(image_file, image_url, image_base64, variant="v1"):
 **标注图片 UI 规范**（真实版本生效）：
 - 字体/线条/箭头颜色：`#FFFFFF 100%`，透明底
 - 字号/线宽/虚线/箭头按图片短边自适应缩放
-- 四庭数值（名+数值两行，不带 cm）在图片**左侧**呈现，七眼段宽**上下穿插**展示，底部标「单位cm」
+- 四庭（名 + 数值带cm + 百分比 三行）在图片**左侧**呈现，七眼段宽**上下穿插**展示（数值带cm，下方另起一行标占头宽百分比）
 - 横线/竖线渐变消失并略超出端点；段宽/庭高用虚线 + 实心三角双箭头标示
 - 竖线含人头最左/最右端线（取自头发分割轮廓），共 8 线 7 段
 """,
@@ -442,6 +560,8 @@ async def _face_measure_impl(image_file, image_url, image_base64, variant="v1"):
                                 "face_width_cm": 24.08,
                                 "inter_eye_distance_cm": 3.44,
                                 "ratios": {"eye_width": 0.143, "inter_eye_distance": 0.143},
+                                "eye1": 3.44, "eye2": 3.44, "eye3": 3.44, "eye4": 3.44,
+                                "eye5": 3.44, "eye6": 3.44, "eye7": 3.44,
                             },
                             "landmarks": {
                                 "hair_top": {"x": 540, "y": 120},
@@ -491,13 +611,13 @@ async def face_measure(
 输入用户正面照，返回：
 - 标注好四庭七眼数据的 **PNG 图片**（仅标注图层，不含人物）
 - 三庭（上庭/中庭/下庭）各段**厘米数值及占比**（**不含顶庭**）
-- 七眼（眼宽/脸宽/两眼间距）**厘米数值及占比**
+- 五眼段宽（eye2~eye6：左脸颊/左眼/两眼间距/右眼/右脸颊）**厘米数值**，另含眼宽/脸宽/两眼间距
 - 四个关键分界点的**原图像素坐标**（发际线/眉心/鼻翼下缘/下巴尖）
 
 基于接口1 的变体，与接口1 的差异：
 - **去顶庭**：不画头顶横线、不返回顶庭数据；`face_total_height_cm` 为三庭之和
 - **竖线范围**：纵向竖线从发际线画到下巴尖（接口1 为头顶→下巴尖）
-- **不画人头最左/最右端线**：仅七眼 6 点共 5 段标尺，不取头发轮廓端线（接口1 为 8 线 7 段）
+- **不画人头最左/最右端线**：仅七眼 6 点共 5 段标尺（eye2~eye6），不取头发轮廓端线（接口1 为 8 线 7 段 eye1~eye7）
 
 其余（箭头/虚线/字体/单位cm/七眼数据）与接口1 一致。
 
@@ -512,7 +632,7 @@ async def face_measure(
 **标注图片 UI 规范**（真实版本生效）：
 - 字体/线条/箭头颜色：`#FFFFFF 100%`，透明底
 - 字号/线宽/虚线/箭头按图片短边自适应缩放
-- 三庭数值（名+数值两行，不带 cm）在图片**左侧**呈现，七眼段宽**上下穿插**展示，底部标「单位cm」
+- 三庭（名 + 数值带cm + 百分比 三行）在图片**左侧**呈现，七眼段宽**上下穿插**展示（数值带cm，下方另起一行标占头宽百分比）
 - 段宽/庭高用虚线 + 实心三角双箭头标示
 """,
     responses={
@@ -542,6 +662,7 @@ async def face_measure(
                                 "face_width_cm": 24.08,
                                 "inter_eye_distance_cm": 3.44,
                                 "ratios": {"eye_width": 0.143, "inter_eye_distance": 0.143},
+                                "eye2": 3.44, "eye3": 3.44, "eye4": 3.44, "eye5": 3.44, "eye6": 3.44,
                             },
                             "landmarks": {
                                 "hairline": {"x": 540, "y": 430},
@@ -593,6 +714,11 @@ async def face_measure_v2(
 - 发际线类型 `hairline_type`（英文 key）
 - 顺序 `order`（本期固定 `1..N`，不排序）
 
+> **female 走「换发型」模式**：生发图 `grown_image_base64` 由换发型（change_hair）
+> + Flux-2 整帧重绘（= 接口12 final 管线，整帧美颜+整帧重绘）生成，其余参数用固化默认值。
+> **male 仍走原生发（ComfyUI add_hair）管线**。入参与返回结构不变。
+> female 依赖 change_hair 与 ComfyUI(:8188) 均在跑。
+
 {_image_fields_desc}
 
 图片同时支持 `multipart/form-data` 文件上传（字段名 `image_file`）。
@@ -649,7 +775,9 @@ 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="补充遮罩区域的头发", description="ComfyUI 提示词，会替换工作流节点60的文本"),
+    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"):
@@ -672,17 +800,26 @@ async def hair_grow(
 
     try:
         from fastapi.concurrency import run_in_threadpool
-        from hairline.service import generate_grow_results
 
-        # 预览 + 生发(ComfyUI) 都是阻塞且较慢，放线程池避免卡住事件循环
-        items = await run_in_threadpool(generate_grow_results, image, gender, use_mask, prompt, hair_styles)
+        # 预览 + 生发/换发型 都是阻塞且较慢，放线程池避免卡住事件循环。
+        # female：换发型 + Flux-2 整帧重绘（= 接口12 final 管线）；male：仍走原生发管线。
+        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,
+                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,
+                unet_name=flux_model)
         if items is None:
             return err(1001, "无法识别人像")
 
         results = []
         for p in items:
             results.append({
-                "image_base64": _jpg_b64(p["image_bgr"]),               # 预览图 JPG
+                "image_base64": _rgba_png_b64(p["overlay"]),            # 发际线曲线透明 PNG
                 "grown_image_base64": (_png_to_jpg_b64(p["grown_png"])  # 生发图 JPG
                                        if p["grown_png"] else None),
                 "hairline_type": p["hairline_type"],
@@ -695,117 +832,15 @@ async def hair_grow(
 
 
 # ---------------------------------------------------------------------------
-# 接口 7：C 端生发 v2（add_hair2.json 工作流）
+# 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）。
+# 保留路由返回明确错误，避免老客户端拿到裸 404。
 # ---------------------------------------------------------------------------
 
-_WORKFLOW2_PATH = os.path.join(os.path.dirname(__file__), "add_hair2.json")
-
-
-@app.post(
-    "/api/v1/hair/grow-v2",
-    summary="接口7 C端生发 v2（add_hair2 工作流）",
-    tags=["生发"],
-    description=f"""
-输入用户正面照 + **性别** + **发型序号**，使用 add_hair2.json 工作流生成指定发际线类型的预览图与生发图。
-功能与接口2 完全一致，仅 ComfyUI 工作流不同。
-
-{_image_fields_desc}
-
-图片同时支持 `multipart/form-data` 文件上传（字段名 `image_file`）。
-
----
-
-- **gender**（必填）：`male` / `female`。决定返回的贴图集合（female 5 张 / male 4 张）。
-  非法或缺失返回 `1004`。
-- **hair_style**（必填）：`int`，发型序号。`female`：1=ellipse, 2=flower, 3=heart, 4=straight, 5=wave；
-  `male`：1=ellipse, 2=inverse_arc, 3=m, 4=straight。越界返回 `1007`。
-- **beauty_enabled**：本期保留但不生效。
-
-`hairline_type` 取值：`ellipse` / `flower` / `heart` / `straight` / `wave`（female），
-`ellipse` / `m` / `straight` / `inverse_arc`（male）。
-""",
-    responses={
-        200: {
-            "description": "成功",
-            "content": {
-                "application/json": {
-                    "example": {
-                        "code": 0,
-                        "message": "success",
-                        "request_id": "mock-request-id",
-                        "data": {
-                            "results": [
-                                {"image_base64": "iVBORw0KGgo...", "hairline_type": "ellipse", "order": 1},
-                            ]
-                        },
-                    }
-                }
-            },
-        },
-        400: {
-            "description": "参数错误 / 图片识别失败",
-            "content": {
-                "application/json": {
-                    "examples": {
-                        "图片参数错误": {"value": {"code": 1007, "message": "图片参数错误：必须且只能传 image_file / image_url / image_base64 其中一个", "request_id": "x", "data": None}},
-                        "非正面照": {"value": {"code": 1003, "message": "角度问题，请上传正面照", "request_id": "x", "data": None}},
-                    }
-                }
-            },
-        },
-    },
-)
-async def hair_grow_v2(
-    image_file: Optional[UploadFile] = File(default=None, description="上传图片文件（JPG/PNG）"),
-    image_url: Optional[str] = Form(default=None, description="图片 URL"),
-    image_base64: Optional[str] = Form(default=None, description="图片 base64（需带 data:image/...;base64, 前缀）"),
-    gender: Optional[str] = Form(default=None, description="性别 male/female（必填）"),
-    hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔（必填），如 1,2,3。female:1-5 male:1-4"),
-    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="补充遮罩区域的头发", description="ComfyUI 提示词，会替换工作流节点60的文本"),
-):
-    # 1. gender 必填校验（非法/缺失 → 1004）
-    if gender not in ("male", "female"):
-        return err(1004, "gender 必填且只能为 male / female")
-
-    # 2. hair_style 必填校验（解析逗号分隔，越界 → 1007）
-    max_styles = {"female": 5, "male": 4}[gender]
-    hair_styles = _parse_hair_styles(hair_style, max_styles)
-    if hair_styles is None:
-        return err(1007, f"hair_style 必填且为 1..{max_styles} 的整数（逗号分隔），收到 {hair_style!r}")
-
-    # 3. 三选一取图
-    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:
-        from fastapi.concurrency import run_in_threadpool
-        from hairline.service import generate_grow_results
-
-        # 预览 + 生发(ComfyUI) 都是阻塞且较慢，放线程池避免卡住事件循环
-        items = await run_in_threadpool(generate_grow_results, image, gender, use_mask, prompt, hair_styles, _WORKFLOW2_PATH)
-        if items is None:
-            return err(1001, "无法识别人像")
-
-        results = []
-        for p in items:
-            results.append({
-                "image_base64": _jpg_b64(p["image_bgr"]),               # 预览图 JPG
-                "grown_image_base64": (_png_to_jpg_b64(p["grown_png"])  # 生发图 JPG
-                                       if p["grown_png"] else None),
-                "hairline_type": p["hairline_type"],
-                "order": p["order"],
-            })
-        return ok({"results": results})
-    except Exception as ex:  # noqa: BLE001
-        logger.exception("接口7 处理异常")
-        return err(1007, f"处理失败：{ex}")
+@app.post("/api/v1/hair/grow-v2", include_in_schema=False, deprecated=True)
+async def hair_grow_v2():
+    """接口7 已弃用：请改用 /api/v1/hair/grow（接口2）。"""
+    return err(1007, "接口7（/api/v1/hair/grow-v2）已弃用，请使用 /api/v1/hair/grow")
 
 
 # ---------------------------------------------------------------------------
@@ -855,7 +890,7 @@ 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="补充遮罩区域的头发", description="ComfyUI 提示词，会替换工作流节点60的文本"),
+    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)
@@ -984,8 +1019,13 @@ async def face_features(
 - 必填 `gender`（`male`/`female`），决定发型集合（female 5 / male 4）。
 - 必填 `hair_style`（发型序号，逗号分隔如 `1,2,3`），决定返回哪些发际线类型。缺失/越界/非法返回 `1007`。
   `female`：1=ellipse,2=flower,3=heart,4=straight,5=wave；`male`：1=ellipse,2=inverse_arc,3=m,4=straight。
-- 可选 `use_mask` / `prompt`：同接口2 的生发控制参数。
-  注：生发黑模板固定取 `hairline_texture_black/`（middle 档），即三档叠图分别用各自贴图、但生发目标固定 middle。
+- 可选 `use_mask` / `prompt`：同接口2 的生发控制参数（仅 male 路径生效）。
+- 可选 `generate_grow_image`（默认 `true`）：是否生成生发效果图（最耗时）。
+  `false` 时跳过生发，各发型 `grown_image_*` 恒为 `null`，仅返回三档发际线叠图与中心点，大幅降低耗时。
+- **生发机制（同接口2，按性别分流）**：
+  `female` 走「换发型 + Flux-2 整帧重绘」（依赖 change_hair:8801 与 ComfyUI:8188）；
+  `male` 走 ComfyUI `add_hair` 原生 inpaint。
+- 可选 `flux_model` / `redraw_max_side`：同接口2（仅 female 路径生效）。
 
 **返回说明**：
 
@@ -994,8 +1034,10 @@ async def face_features(
   - `grown_image_url`：该发型的**生发图**（生发失败时为 `null`）。
   - `hairline_type`：发际线类型 key。
   worker 返回 `*_base64`，网关落盘后改写为 `*_url`。
-- `best_hairline_center_point`：**首个选中发型**的 middle 档发际线曲线**面部中间点**坐标，
+- `best_hairline_center_point`：**首个选中发型**的 **middle 档**发际线曲线**面部中间点**坐标，
   以**原图像素**为基准（左上角为原点，x 向右，y 向下）。
+- `high_hairline_center_point`：同上，**high 档**发际线中点。
+- `low_hairline_center_point`：同上，**low 档**发际线中点。
 """,
     responses={
         200: {
@@ -1012,6 +1054,33 @@ async def face_features(
                                 {"hairline_type": "flower", "image_middle_base64": "iVBORw0KGgo...", "image_high_base64": "iVBORw0KGgo...", "image_low_base64": "iVBORw0KGgo...", "grown_image_base64": None, "order": 2},
                             ],
                             "best_hairline_center_point": {"x": 540, "y": 430},
+                            "high_hairline_center_point": {"x": 540, "y": 380},
+                            "low_hairline_center_point": {"x": 540, "y": 480},
+                            "face_measure": {
+                                "face_total_height_cm": 13.76,
+                                "four_courts": {
+                                    "top_court_cm": 3.44, "upper_court_cm": 3.44,
+                                    "middle_court_cm": 3.44, "lower_court_cm": 3.44,
+                                    "ratios": {"top_court": 0.25, "upper_court": 0.25,
+                                               "middle_court": 0.25, "lower_court": 0.25},
+                                },
+                                "seven_eyes": {
+                                    "eye_width_cm": 3.44, "face_width_cm": 24.08,
+                                    "inter_eye_distance_cm": 3.44,
+                                    "ratios": {"eye_width": 0.143, "inter_eye_distance": 0.143},
+                                    "eye1": 3.44, "eye2": 3.44, "eye3": 3.44, "eye4": 3.44,
+                                    "eye5": 3.44, "eye6": 3.44, "eye7": 3.44,
+                                },
+                                "landmarks": {
+                                    "hair_top": {"x": 540, "y": 120},
+                                    "hairline": {"x": 540, "y": 430},
+                                    "brow_center": {"x": 540, "y": 740},
+                                    "nose_bottom": {"x": 540, "y": 1050},
+                                    "chin_tip": {"x": 540, "y": 1360},
+                                },
+                                "hairline_source": "segmentation",
+                                "head_pose": {"yaw": -1.39, "pitch": 2.49, "roll": -0.06},
+                            },
                         },
                     }
                 }
@@ -1033,8 +1102,11 @@ async def hairline_generate(
     image_base64: Optional[str] = Form(default=None, description="图片 base64（需带 data:image/...;base64, 前缀）"),
     gender: Optional[str] = Form(default=None, description="性别 male/female（必填）"),
     hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔（必填，如 1,2,3）。female:1-5 male:1-4"),
-    use_mask: bool = Form(default=True, description="生发是否启用 inpaint 遮罩（同接口2，测试对比用）"),
-    prompt: str = Form(default="补充遮罩区域的头发", description="ComfyUI 提示词（同接口2），会替换工作流节点60的文本"),
+    use_mask: bool = Form(default=True, description="生发是否启用 inpaint 遮罩（同接口2，仅 male 路径生效）"),
+    prompt: str = Form(default="填充遮罩区域的头发", description="ComfyUI 提示词（同接口2，仅 male 路径生效），会替换工作流节点60的文本"),
+    generate_grow_image: bool = Form(default=True, description="是否生成生发效果图（最耗时）。默认 true 出图；false 时跳过生发，各发型 grown_image 恒为 null，仅返回三档发际线叠图与中心点"),
+    flux_model: Optional[str] = Form(default=None, description="Flux 模型文件名（同接口2，切换模型用）。None=工作流默认"),
+    redraw_max_side: Optional[int] = Form(default=None, description="重绘压图长边像素（同接口2，仅 female 路径生效）。None=默认896；0=不缩图（原图直送）；其他如 768/640/1024"),
 ):
     if gender not in ("male", "female"):
         return err(1004, "gender 必填且只能为 male / female")
@@ -1058,7 +1130,10 @@ async def hairline_generate(
         from hairline.service import generate_hairline_pngs
 
         res = await run_in_threadpool(
-            generate_hairline_pngs, image, gender, hair_styles, use_mask, prompt)
+            generate_hairline_pngs, image, gender, hair_styles, use_mask, prompt,
+            generate_grow_image=generate_grow_image,
+            redraw_max_side=redraw_max_side, unet_name=flux_model,
+            v2_defaults=_V2_FINAL_DEFAULTS)
         if res is None:
             return err(1001, "无法识别人像")
 
@@ -1067,18 +1142,32 @@ async def hairline_generate(
             ov = it["overlays"]
             hairline_images.append({
                 "hairline_type": it["hairline_type"],
-                "image_middle_base64": _jpg_b64(ov["middle"]),          # 发际线叠图 middle 档 JPG
-                "image_high_base64": _jpg_b64(ov["high"]),              # 发际线叠图 high 档 JPG
-                "image_low_base64": _jpg_b64(ov["low"]),                # 发际线叠图 low 档 JPG
+                "image_middle_base64": _rgba_png_b64(ov["middle"]),     # 发际线曲线透明 PNG middle 档
+                "image_high_base64": _rgba_png_b64(ov["high"]),         # 发际线曲线透明 PNG high 档
+                "image_low_base64": _rgba_png_b64(ov["low"]),           # 发际线曲线透明 PNG low 档
                 "grown_image_base64": (_png_to_jpg_b64(it["grown_png"]) # 生发图 JPG（失败为 null）
                                        if it.get("grown_png") else None),
                 "order": it["order"],
             })
-        c = res["best_center"]
+        c = res.get("best_centers") or {}
+        def _pt(p):
+            return ({"x": p[0], "y": p[1]} if p else None)
         data = {
             "hairline_images": hairline_images,
-            "best_hairline_center_point": ({"x": c[0], "y": c[1]} if c else None),
+            "best_hairline_center_point": _pt(c.get("middle")),
+            "high_hairline_center_point": _pt(c.get("high")),
+            "low_hairline_center_point": _pt(c.get("low")),
         }
+
+        # face_measure：复用接口1测量数值（四庭/七眼 eye1~eye7/landmarks/姿态），不含标注图。
+        # 独立流程，容错：测量失败（无人脸/非正面/分割失败）→ null，不影响发际线主结果。
+        try:
+            fm = _run_face_measure_data(image, variant="v1")
+            data["face_measure"] = fm[0] if fm is not None else None
+        except Exception as fm_e:  # noqa: BLE001
+            logger.warning("接口5 face_measure 失败：%s", fm_e)
+            data["face_measure"] = None
+
         return ok(data)
     except Exception as ex:  # noqa: BLE001
         logger.exception("接口5 处理异常")
@@ -1297,19 +1386,23 @@ async def hairline_grow(
     hairgrow_strength: float = Form(default=0.75, description="区域生发强度（仅 hairgrow 后端），默认 0.75"),
     is_hr: bool = Form(default=False, description="高清模式（换发型输出 1152×1536，否则 576×768）"),
     seg_model: str = Form(default="segformer", description="头发分割模型：bisenet | segformer（默认 segformer）"),
-    mask_type: str = Form(default="eroded", description="遮罩类型：eroded(内缩) | closed(闭合区域) | pushed(发际线外推)（默认 eroded）"),
-    erode_cm: float = Form(default=1.2, description="遮罩外缘朝中心151内缩距离（厘米，同接口9），默认 1.2"),
-    hairline_push_cm: float = Form(default=1.0, description="发际线外推距离（厘米，往头发方向推进；仅 mask_type=pushed 生效），默认 1.0"),
-    hairline_edge: str = Form(default="column", description="发际线提取方式：column(逐列最低点) | contour(形态学轮廓)（仅 pushed 生效），默认 column"),
-    swap_mode: str = Form(default="ext_mask", description="换发型取图模式：ext_mask(改造换发型用接口9遮罩) | as_is(不改换发型，贴回再裁)（默认 ext_mask）"),
-    blend_method: str = Form(default="feather", description="接缝融合：feather(高斯羽化) | alpha_gradient(距离渐变) | seamless(泊松无缝) | multiband(多频段金字塔)（默认 feather）"),
-    feather_px: int = Form(default=15, description="羽化/渐变过渡宽度（像素），默认 15"),
+    erode_cm: float = Form(default=0.6, description="baseline 参考内缩距离（厘米），默认 0.6"),
+    hairline_push_cm: float = Form(default=1.0, description="发际线外推距离（厘米，往头发方向推进），默认 1.0"),
+    hairline_edge: str = Form(default="column", description="发际线提取方式：column | contour，默认 column"),
+    swap_mode: str = Form(default="ext_mask", description="换发型取图模式：ext_mask | as_is（默认 ext_mask）"),
     edge_erode_px: int = Form(default=3, description="贴图前遮罩内缩像素（防边缘露皮/光晕），默认 3"),
     denoising_strength: float = Form(default=0.6, description="换发型 webui 重绘强度（越大生发越激进），默认 0.6"),
-    color_match: bool = Form(default=False, description="融合前对生成图做 Reinhard 颜色校正（消除整体色差，seamless 下自动跳过），默认 false"),
-    mb_levels: int = Form(default=5, description="multiband 多频段金字塔层数（2~6，越大低频色差抹得越宽，仅 multiband 生效），默认 5"),
+    mb_levels: int = Form(default=5, description="多频段金字塔层数（2~6，越大低频色差抹得越宽），默认 5"),
+    blend_method: str = Form(default="multiband", description="接缝融合方法：multiband(多频段金字塔,默认) | seamless(泊松无缝克隆) | two_stage(泊松→多频段两段式,大色差场景) | feather(高斯羽化) | alpha_gradient(距离变换内渐变)"),
+    color_match: bool = Form(default=True, description="融合前 Reinhard 颜色迁移消除整体色差（对 multiband/feather/alpha_gradient 有效；seamless/two_stage 自带调色故跳过），默认 True"),
+    color_match_strength: float = Form(default=1.0, description="颜色迁移强度（0~1，1=全迁移，<1 只迁移部分防过度改色），默认 1.0"),
+    mb_feather_px: int = Form(default=1, description="多频段最细层掩码轻羽化像素（0=不羽化，消除发丝边缘锯齿），默认 1"),
+    transition_band_px: int = Form(default=-1, description="keep-region 过渡带边距（-1=自动按层数 2**n，>=0 用绝对像素与层数解耦），默认 -1"),
+    inpainting_fill: int = Form(default=1, description="change_hair服务端重绘填充：0=保留原图(治染绿) | 1=填充噪声(默认/原始) | 2=纯色 | 3=潜变量噪声。默认 1"),
+    mask_blur: int = Form(default=11, description="change_hair服务端遮罩边缘模糊像素（原始11，越大颜色越易从边缘渗透），默认 11"),
+    mask_dilate_scale: float = Form(default=1.0, description="change_hair服务端遮罩膨胀缩放（1.0=原始核尺寸，<1收缩防越界），默认 1.0"),
 ):
-    """接口11：发际线生发 + 分步可视化"""
+    """接口11：发际线生发 + 分步可视化（**不含重绘**，重绘见接口12）。遮罩固定 pushed，融合默认 multiband（可选 seamless/two_stage/feather）。"""
     raw, e = await resolve_image_bytes(image_file, image_url, image_base64)
     if e is not None:
         return e
@@ -1323,14 +1416,24 @@ async def hairline_grow(
         from face_analysis.hairline_grow import generate_hairline_grow, NoFaceError, SwapError
         from uuid import uuid4 as _uuid4
         rid = _uuid4().hex[:8]
-        logger.info("[%s] 接口11 收到请求: mask_type=%s hairline_push_cm=%s hairline_edge=%s",
-                    rid, mask_type, hairline_push_cm, hairline_edge)
+        logger.info("[%s] 接口11 收到请求: hairline_push_cm=%s hairline_edge=%s mb_levels=%s "
+                    "blend=%s color_match=%s cm_strength=%s mb_feather_px=%s transition_band_px=%s "
+                    "inpainting_fill=%s mask_blur=%s mask_dilate_scale=%s",
+                    rid, hairline_push_cm, hairline_edge, mb_levels, blend_method,
+                    color_match, color_match_strength, mb_feather_px, transition_band_px,
+                    inpainting_fill, mask_blur, mask_dilate_scale)
         try:
             data = await run_in_threadpool(
-                generate_hairline_grow, image, hairline_id, is_hr, seg_model,
-                mask_type, erode_cm, swap_mode, blend_method, feather_px, edge_erode_px,
-                denoising_strength, gen_backend, hairgrow_strength, color_match, mb_levels,
-                hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge, rid=rid)
+                generate_hairline_grow, image, hairline_id,
+                is_hr=is_hr, seg_model=seg_model, erode_cm=erode_cm, swap_mode=swap_mode,
+                edge_erode_px=edge_erode_px, denoising_strength=denoising_strength,
+                gen_backend=gen_backend, hairgrow_strength=hairgrow_strength,
+                mb_levels=mb_levels, hairline_push_cm=hairline_push_cm,
+                hairline_edge=hairline_edge, blend_method=blend_method,
+                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)
         except NoFaceError:
             return err(1001, "无法识别人像")
         except SwapError as se:
@@ -1343,22 +1446,33 @@ async def hairline_grow(
 
 
 # ---------------------------------------------------------------------------
-# 接口 12：发际线生发（接口11 固定参数精简版）
+# 接口 12：发际线带重绘（调用接口11 的 ④final 作输入 + ⑤-①重绘带作遮罩，Flux-2 重绘）
 # ---------------------------------------------------------------------------
 
 @app.post(
     "/api/v1/hairline/grow_v2",
-    summary="接口12 发际线生发（固定金字塔融合，仅返回最终图）",
+    summary="接口12 发际线带重绘（接口11 final + 发际线重绘带 → Flux-2 保色重绘）",
     tags=["生发"],
     description=f"""
-接口11 的固定参数精简版，适合生产直调。与接口11 共用同一管线，区别仅在于：
+接口12 是接口11 的**下游重绘阶段**。内部先跑接口11 核心管线拿到 **④ 接缝融合最终图（final）**，
+再取 **⑤-① 发际线重绘带**（发际线沿外推方向 `band_lo_mult×push` ~ `band_hi_mult×push`
+之间、经 ①-a baseline 截断只留上部的带状区域，默认 0.5×~1.5×push）
+作为遮罩，用 **Flux-2（ComfyUI）** 做 reference-latent 保色重绘（不易染绿），重绘结果再与 final 融合。
 
-- **固定** `blend_method=multiband`（多频段金字塔融合）、`mb_levels=5`、`erode_cm=0.6`
-  （外缘朝中心151 内缩 0.6cm）。这三项不可调，故本接口不暴露。
-- **返回值精简**：只返回 `final_base64`（最终合成图），不再附带接口11 的分步可视化。
+与接口11 的关系：接口11 只负责生成 final（不再含重绘）；接口12 负责在 final 上做发际线带重绘。
+接口11 的可调参数（seg_model/gen_backend/hairline_push_cm/blend_method/color_match 等）
+在本接口同样暴露，用于内部生成 final 与重绘带；另有 `comfyui_prompt` 控制 Flux-2 提示词。
 
-其余参数（hairline_id、seg_model、gen_backend、is_hr、denoising_strength、color_match、
-edge_erode_px 等）仍保留为可选 Form，调用方可按需覆盖，未传则用接口11 同款默认值。
+⚠️ 依赖 ComfyUI（默认 :8188）在跑，否则重绘失败会在 `data.redraw.c_error` 报告。
+
+**同时产出两版结果供对比**：
+- `redraw_full`：ComfyUI 整帧输出（全脸美颜 + 全脸重绘），与手动跑 ComfyUI 一致。
+- `redraw_band`：加发只在发际线带、美颜保留全脸（band 内用 ComfyUI 重绘，band 外 = final 结构
+  + 按 `beauty_alpha` 融入全脸美颜）。
+
+返回 `data.steps`：`input`（原图）/ `final`（接口11 的 ④，重绘输入基底）/
+`redraw_band_overlay`（⑤-① 重绘带可视化）/ `redraw_full`（A 整帧）/ `redraw_band`（B 局部加发+全脸美颜）/
+`redraw_c`（兼容旧字段，=redraw_full）。
 
 {_image_fields_desc}
 """,
@@ -1372,16 +1486,27 @@ async def hairline_grow_v2(
     hairgrow_strength: float = Form(default=0.75, description="区域生发强度（仅 hairgrow 后端），默认 0.75"),
     is_hr: bool = Form(default=False, description="高清模式（换发型输出 1152×1536，否则 576×768）"),
     seg_model: str = Form(default="segformer", description="头发分割模型：bisenet | segformer（默认 segformer）"),
-    mask_type: str = Form(default="eroded", description="遮罩类型：eroded(内缩) | closed(闭合区域) | pushed(发际线外推)（默认 eroded）"),
-    swap_mode: str = Form(default="ext_mask", description="换发型取图模式：ext_mask(改造换发型用接口9遮罩) | as_is(不改换发型，贴回再裁)（默认 ext_mask）"),
-    feather_px: int = Form(default=15, description="羽化/渐变过渡宽度（像素，本接口固定 multiband 故不生效，仅留作兼容）"),
+    erode_cm: float = Form(default=0.6, description="baseline 参考内缩距离（厘米），默认 0.6"),
+    swap_mode: str = Form(default="ext_mask", description="换发型取图模式：ext_mask | as_is（默认 ext_mask）"),
     edge_erode_px: int = Form(default=3, description="贴图前遮罩内缩像素（防边缘露皮/光晕），默认 3"),
     denoising_strength: float = Form(default=0.6, description="换发型 webui 重绘强度（越大生发越激进），默认 0.6"),
-    color_match: bool = Form(default=False, description="融合前对生成图做 Reinhard 颜色校正（消除整体色差），默认 false"),
-    hairline_push_cm: float = Form(default=1.0, description="发际线外推距离（厘米，仅 mask_type=pushed 生效），默认 1.0"),
-    hairline_edge: str = Form(default="column", description="发际线提取方式：column | contour（仅 pushed 生效），默认 column"),
+    mb_levels: int = Form(default=5, description="多频段金字塔层数（2~6），默认 5"),
+    hairline_push_cm: float = Form(default=1.0, description="发际线外推距离（厘米），默认 1.0"),
+    hairline_edge: str = Form(default="column", description="发际线提取方式：column | contour，默认 column"),
+    blend_method: str = Form(default="multiband", description="接缝融合方法：multiband | seamless | two_stage | feather | alpha_gradient"),
+    color_match: bool = Form(default=True, description="融合前 Reinhard 颜色迁移消除整体色差，默认 True"),
+    color_match_strength: float = Form(default=1.0, description="颜色迁移强度（0~1），默认 1.0"),
+    mb_feather_px: int = Form(default=1, description="多频段最细层掩码轻羽化像素，默认 1"),
+    transition_band_px: int = Form(default=-1, description="keep-region 过渡带边距（-1=自动），默认 -1"),
+    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」"),
+    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"),
 ):
-    """接口12：发际线生发（固定 multiband/mb_levels=5/erode_cm=0.6，仅返回最终图）。"""
+    """接口12：发际线带重绘。同时产出 redraw_full(整帧美颜) 与 redraw_band(局部加发+全脸美颜) 两版对比。"""
     raw, e = await resolve_image_bytes(image_file, image_url, image_base64)
     if e is not None:
         return e
@@ -1392,42 +1517,190 @@ async def hairline_grow_v2(
 
     try:
         from fastapi.concurrency import run_in_threadpool
-        from face_analysis.hairline_grow import generate_hairline_grow, NoFaceError, SwapError
+        from face_analysis.hairline_grow import generate_hairline_redraw, NoFaceError, SwapError
+        from uuid import uuid4 as _uuid4
+        rid = _uuid4().hex[:8]
+        logger.info("[%s] 接口12 收到请求: hairline_id=%s hairline_push_cm=%s blend=%s comfyui_prompt=%r",
+                    rid, hairline_id, hairline_push_cm, blend_method, comfyui_prompt)
         try:
-            # 固定三项：blend_method=multiband、mb_levels=5、erode_cm=0.6
             data = await run_in_threadpool(
-                generate_hairline_grow, image, hairline_id, is_hr, seg_model,
-                mask_type, 0.6, swap_mode, "multiband", feather_px, edge_erode_px,
-                denoising_strength, gen_backend, hairgrow_strength, color_match, 5,
-                hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge)
+                generate_hairline_redraw, image, hairline_id,
+                is_hr=is_hr, seg_model=seg_model, erode_cm=erode_cm, swap_mode=swap_mode,
+                edge_erode_px=edge_erode_px, denoising_strength=denoising_strength,
+                gen_backend=gen_backend, hairgrow_strength=hairgrow_strength,
+                mb_levels=mb_levels, hairline_push_cm=hairline_push_cm,
+                hairline_edge=hairline_edge, blend_method=blend_method,
+                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, comfyui_prompt=comfyui_prompt,
+                beauty_alpha=beauty_alpha, band_lo_mult=band_lo_mult,
+                band_hi_mult=band_hi_mult, rid=rid)
         except NoFaceError:
             return err(1001, "无法识别人像")
         except SwapError as se:
             return err(1007, f"换发型失败：{se}")
-        # 精简返回：只取最终合成图，丢掉接口11 的全部分步可视化
-        return ok({
-            "hairline_id": data["hairline_id"],
-            "image_size": data["image_size"],
-            "final_base64": data["steps"]["final_base64"],
-        })
+        logger.info("[%s] 接口12 成功返回", rid)
+        return ok(data)
     except Exception as ex:  # noqa: BLE001
         logger.exception("接口12 处理异常")
         return err(1007, f"处理失败：{ex}")
 
 
+# ---------------------------------------------------------------------------
+# 接口 12 final：精简版发际线带重绘（仅需图片 + 发型 ID，其余参数全用默认值）
+# ---------------------------------------------------------------------------
+
+# 接口12 final 固化的默认参数（= test_interface12.html 当前默认值，color_match 关闭）
+_V2_FINAL_DEFAULTS = dict(
+    gen_backend="swaphair", hairgrow_strength=0.75, is_hr=False, seg_model="segformer",
+    erode_cm=0.6, swap_mode="ext_mask", edge_erode_px=3, denoising_strength=0.6,
+    mb_levels=5, hairline_push_cm=0.8, hairline_edge="column", blend_method="two_stage",
+    color_match=False, color_match_strength=0.4, mb_feather_px=1, transition_band_px=-1,
+    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,
+)
+
+
+async def _run_v2_final(image_file, image_url, image_base64, hairline_id, tag):
+    """接口12 final / final v2 共用：仅需图片 + hairline_id，其余用固化默认值。
+    两者后端计算完全一致（同一次 ComfyUI 输出同时含 A 整帧与 B 局部+美颜），
+    差异仅在配套测试页展示哪一版。"""
+    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:
+        from fastapi.concurrency import run_in_threadpool
+        from face_analysis.hairline_grow import generate_hairline_redraw, NoFaceError, SwapError
+        from uuid import uuid4 as _uuid4
+        rid = _uuid4().hex[:8]
+        logger.info("[%s] %s 收到请求: hairline_id=%s（其余用默认值）", rid, tag, hairline_id)
+        try:
+            data = await run_in_threadpool(
+                generate_hairline_redraw, image, hairline_id,
+                rid=rid, **_V2_FINAL_DEFAULTS)
+        except NoFaceError:
+            return err(1001, "无法识别人像")
+        except SwapError as se:
+            return err(1007, f"换发型失败：{se}")
+        logger.info("[%s] %s 成功返回", rid, tag)
+        return ok(data)
+    except Exception as ex:  # noqa: BLE001
+        logger.exception("%s 处理异常", tag)
+        return err(1007, f"处理失败：{ex}")
+
+
+@app.post(
+    "/api/v1/hairline/grow_v2_final",
+    summary="接口12 final 精简重绘（整帧重绘；仅图片 + 发型 ID）",
+    tags=["生发"],
+    description=f"""
+接口12 的**精简/生产版**：只需上传图片 + 选择 `hairline_id`，其余所有参数固化为当前调优默认值
+（hairline_push_cm=0.8 / blend_method=two_stage / **color_match=关闭** / color_match_strength=0.4 /
+beauty_alpha=0.6 / band_lo_mult=0.5 / band_hi_mult=1.5 等）。
+
+**最终重绘取整帧重绘**（`data.steps.redraw_full`，全脸美颜 + 全脸重绘）。
+返回结构与接口12 一致（`data.steps` 同时含 `redraw_full`（A 整帧）/ `redraw_band`（B 局部+美颜））。
+
+⚠️ 依赖 ComfyUI（默认 :8188）在跑。
+
+{_image_fields_desc}
+""",
+)
+async def hairline_grow_v2_final(
+    image_file: Optional[UploadFile] = File(default=None, description="上传图片文件（JPG/PNG）"),
+    image_url: Optional[str] = Form(default=None, description="图片 URL"),
+    image_base64: Optional[str] = Form(default=None, description="图片 base64（需带 data:image/...;base64, 前缀）"),
+    hairline_id: str = Form(..., description="发际线类型 ID（= change_hair hair_id，如 chang_bolang）"),
+):
+    """接口12 final：整帧重绘版，仅需图片 + hairline_id。"""
+    return await _run_v2_final(image_file, image_url, image_base64, hairline_id, "接口12final")
+
+
+@app.post(
+    "/api/v1/hairline/grow_v2_final_v2",
+    summary="接口12 final v2 精简重绘（B 局部加发+全脸美颜；仅图片 + 发型 ID）",
+    tags=["生发"],
+    description=f"""
+接口12 final 的**局部加发版**：参数与 `grow_v2_final` 完全相同（color_match 关闭等），
+唯一区别是**最终重绘取 B 局部加发+全脸美颜**（`data.steps.redraw_band`：
+加发只在发际线带内、band 外保留 final 结构并按 beauty_alpha 融入全脸美颜）。
+
+返回结构与接口12 一致（`data.steps` 同时含 `redraw_full`（A 整帧）/ `redraw_band`（B 局部+美颜））。
+
+⚠️ 依赖 ComfyUI（默认 :8188）在跑。
+
+{_image_fields_desc}
+""",
+)
+async def hairline_grow_v2_final_v2(
+    image_file: Optional[UploadFile] = File(default=None, description="上传图片文件（JPG/PNG）"),
+    image_url: Optional[str] = Form(default=None, description="图片 URL"),
+    image_base64: Optional[str] = Form(default=None, description="图片 base64（需带 data:image/...;base64, 前缀）"),
+    hairline_id: str = Form(..., description="发际线类型 ID（= change_hair hair_id，如 chang_bolang）"),
+):
+    """接口12 final v2：B 局部加发+全脸美颜版，仅需图片 + hairline_id。"""
+    return await _run_v2_final(image_file, image_url, image_base64, hairline_id, "接口12finalv2")
+
+
+# ---------------------------------------------------------------------------
+# 重绘端点（替代 local_test /api/generate）
+# ---------------------------------------------------------------------------
+
+@app.post(
+    "/api/v1/redraw",
+    summary="ComfyUI 重绘",
+    tags=["重绘"],
+    description="""
+传入人物图片 + 遮罩图片，直接调 ComfyUI（0716add-hair 工作流）执行局部重绘。
+替代原 local_test :8899 的 /api/generate 接口。
+
+**遮罩图片格式**：支持红色遮罩(R=255)、白色遮罩(R=G=B=255)、Alpha遮罩(A=255)，服务取所有通道最大值。
+**遮罩区域**表示需要重绘的部分，非遮罩区域保持原图不变。
+""",
+)
+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",
+                       description="ComfyUI 提示词"),
+):
+    image_bytes = await image_file.read()
+    mask_bytes = await mask_file.read()
+    from fastapi.concurrency import run_in_threadpool
+    from hairline.redraw import run_redraw
+    try:
+        png_bytes = await run_in_threadpool(
+            run_redraw, image_bytes, mask_bytes, prompt)
+    except Exception as e:  # noqa: BLE001
+        logger.warning("重绘失败: %s", e)
+        return err(500, f"重绘失败: {e}")
+    b64 = base64.b64encode(png_bytes).decode()
+    return ok({"image_base64": f"data:image/png;base64,{b64}"})
+
+
 # ---------------------------------------------------------------------------
 # 调试：下载后端日志（接口11 遮罩计算全过程）
 # ---------------------------------------------------------------------------
 
 @app.get("/api/v1/debug/hairline_log", include_in_schema=False)
 async def download_hairline_log(rid: Optional[str] = None, tail: int = 500):
-    """返回 /home/xsl/hair/log/hairline_grow.log 的内容。
+    """返回 <仓库根>/log/hairline_grow.log 的内容。
 
     rid 非空时只返回该 request id 相关的行；tail 限制返回最后 N 行（默认 500）。
     供调试页"下载日志"按钮调用。
     """
     from fastapi.responses import PlainTextResponse
-    log_path = "/home/xsl/hair/log/hairline_grow.log"
+    log_path = os.getenv(
+        "HAIR_LOG_DIR",
+        os.path.join(os.path.dirname(os.path.abspath(__file__)), "log"),
+    )
+    log_path = os.path.join(log_path, "hairline_grow.log")
     try:
         with open(log_path, encoding="utf-8") as fh:
             lines = fh.readlines()

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()

comfyui.service

@@ -0,0 +1,15 @@
+[Unit]
+Description=ComfyUI (127.0.0.1:8188)
+After=network-online.target
+Wants=network-online.target
+
+[Service]
+Type=simple
+User=ubuntu
+WorkingDirectory=/home/ubuntu/ComfyUI
+ExecStart=/home/ubuntu/ComfyUI/venv/bin/python main.py --listen 127.0.0.1 --port 8188 --cache-classic --fast
+Restart=on-failure
+RestartSec=5
+
+[Install]
+WantedBy=multi-user.target

docs/iface11_hair_test_run/response_meta.json

@@ -0,0 +1,65 @@
+{
+  "code": 0,
+  "message": "success",
+  "request_id": "mock-request-id",
+  "data": {
+    "hairline_id": "chang_zhixian",
+    "gen_backend": "swaphair",
+    "hairgrow_strength": 0.75,
+    "is_hr": false,
+    "seg_model": "segformer",
+    "mask_type": "pushed",
+    "erode_cm": 0.6,
+    "swap_mode": "ext_mask",
+    "blend_method": "multiband",
+    "edge_erode_px": 3,
+    "mb_levels": 5,
+    "hairline_push_cm": 1.0,
+    "hairline_edge": "column",
+    "denoising_strength": 0.6,
+    "color_match": true,
+    "color_match_strength": 1.0,
+    "mb_feather_px": 1,
+    "transition_band_px": -1,
+    "inpainting_fill": 1,
+    "mask_blur": 11,
+    "mask_dilate_scale": 1.0,
+    "px_per_cm": 47.5311,
+    "erode_px": 29,
+    "hair_pixels": 186798,
+    "closed_pixels": 191712,
+    "mask_pixels": 140299,
+    "image_size": {
+      "width": 1257,
+      "height": 1495
+    },
+    "timings_ms": {
+      "mask": 1462,
+      "swap": 5596,
+      "blend": 220
+    },
+    "redraw": {
+      "enabled": false
+    },
+    "_rid": "bc7205a4",
+    "steps": {
+      "input_base64": "<omitted 427407 chars>",
+      "baseline_overlay_base64": "<omitted 435895 chars>",
+      "upper_overlay_base64": "<omitted 384479 chars>",
+      "hair_seg_overlay_base64": "<omitted 428039 chars>",
+      "top_fill_overlay_base64": "",
+      "closed_overlay_base64": "",
+      "hairline_overlay_base64": "<omitted 445467 chars>",
+      "pushed_overlay_base64": "<omitted 452803 chars>",
+      "mask_overlay_base64": "<omitted 420375 chars>",
+      "mask_base64": "<omitted 8026 chars>",
+      "swap_raw_base64": "<omitted 340791 chars>",
+      "hard_paste_base64": "<omitted 420679 chars>",
+      "alpha_base64": "<omitted 7762 chars>",
+      "final_base64": "<omitted 415811 chars>",
+      "redraw_band_overlay_base64": "",
+      "redraw_a_base64": "",
+      "redraw_c_base64": ""
+    }
+  }
+}

docs/iface3_implementation.md

@@ -0,0 +1,177 @@
+# 接口3 B端生发 — 实现文档
+
+> 文档日期：2026-07-18
+
+---
+
+## 一、接口概述
+
+**接口3** 是 B端（医生/操作端）生发接口。医生在用户照片上手动用马克笔画出发际线后，只需上传这一张划线图，系统自动检测划线 → 生成遮罩 → 送 ComfyUI 生发，返回「植发3个月」效果图。
+
+**与接口2 的核心区别**：
+
+| 特性 | 接口2（C端生发） | 接口3（B端生发） |
+|------|----------------|----------------|
+| 输入 | 原始照片 | 划线图（含手绘线） |
+| 发际线来源 | 系统按发型模板自动生成 | 医生手绘标注 |
+| 发型类型 | ellipse/flower/heart/straight/wave | custom（自定义） |
+| 中间步骤 | extract_context + swapHair + ComfyUI重绘 | 划线检测 + 遮罩 + ComfyUI生发 |
+| 是否调 change_hair | 是（女性流程） | 否 |
+| ComfyUI 工作流 | 0716add-hair-api.json（重绘） | add_hair.json（生发） |
+| 典型耗时 | ~11s | ~6-8s |
+
+---
+
+## 二、接口定义
+
+### 路由
+
+```
+POST /api/v1/hair/grow-b
+```
+
+### 入参
+
+| 参数 | 类型 | 必填 | 说明 |
+|------|------|------|------|
+| `marked_image_file` | UploadFile | 三选一 | 划线图片文件（JPG/PNG） |
+| `marked_image_url` | str | 三选一 | 划线图片 URL |
+| `marked_image_base64` | str | 三选一 | 划线图片 base64 |
+| `use_mask` | bool | 否（默认True） | 是否自动检测划线并建遮罩。False时跳过检测，直接送划线图 |
+| `prompt` | str | 否 | ComfyUI 提示词，默认"补充遮罩区域的头发，加一点美颜" |
+
+### 返回
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "hair_growth_image_base64": "iVBORw0KGgo...（生发图 JPG base64）",
+    "hairline_type": "custom"
+  }
+}
+```
+
+错误码：
+- `1001`: 无法识别人像 / 未检测到发际线划线
+- `1007`: 处理失败
+- `1008`: 图片格式不支持
+
+---
+
+## 三、完整调用链
+
+```
+POST /api/v1/hair/grow-b
+│
+├─ app.py hair_grow_b()                          [app.py:929]
+│   ├─ resolve_image_bytes() → marked_raw       解析图片（file/url/base64三选一）
+│   ├─ cv2.imdecode → marked_bgr                解码为 BGR
+│   └─ run_in_threadpool(generate_grow_b, ...)
+│
+├─ service.py generate_grow_b(marked_bgr, use_mask, prompt)  [service.py:381]
+│   │
+│   ├─ 步骤1：人脸检测 + 头发分割（仅 use_mask=True 时）
+│   │   ├─ get_landmarker().detect(rgb)          MediaPipe 478点人脸检测
+│   │   │   → landmarks（无人脸返回 no_face）
+│   │   ├─ get_parser().parse(rgb)               SegFormer 面部分割（CPU ~0.9s）
+│   │   │   → parse_map（int label map）
+│   │   │
+│   ├─ 步骤2：手绘发际线检测（仅 use_mask=True 时）
+│   │   ├─ detect_marker_hairline(marked_bgr, landmarks, parse_map)
+│   │   │   │                                   [marker_detect.py:41]
+│   │   │   ├─ forehead_upper_region(landmarks)  额头上部 ROI
+│   │   │   ├─ head_silhouette(parse_map)         头部轮廓 ROI
+│   │   │   ├─ _blackhat(gray)                    黑帽变换（响应比邻域暗的细结构）
+│   │   │   ├─ _snap_anchor(bh, 左鬓角21)         左锚点吸附
+│   │   │   ├─ _snap_anchor(bh, 右鬓角251)        右锚点吸附
+│   │   │   ├─ route_through_array(cost, 左, 右)  Dijkstra最小代价路径
+│   │   │   └→ path (N,2) row,col（拒识返回 None → no_line）
+│   │   │
+│   │   ├─ path_to_curve_mask(path)              路径→曲线mask（uint8 0/255）
+│   │   └─ mask_from_curve(curve_mask, landmarks, parse_map)
+│   │       │                                   [mask.py]
+│   │       ├─ _above_curve_region(curve_mask)    曲线以上区域
+│   │       ├─ cv2.morphologyEx(闭运算)           填洞
+│   │       ├─ 最大连通域
+│   │       └─ 高斯羽化                            → mask (uint8 0-255)
+│   │
+│   ├─ 步骤3：合成 RGBA PNG
+│   │   ├─ compose_comfy_rgba(marked_bgr, mask)  RGB=原图，alpha=255×(1-mask)
+│   │   └─ PNG 编码                               → rgba_png_bytes
+│   │
+│   └─ 步骤4：ComfyUI 生发
+│       └─ comfyui.run(rgba_png_bytes, prompt)   [comfyui.py:87]
+│           ├─ 上传图片到 ComfyUI /upload/image
+│           ├─ 加载工作流 add_hair.json
+│           ├─ 替换节点26输入图 + 节点6随机seed + 节点60提示词
+│           ├─ POST /prompt 提交工作流
+│           ├─ 轮询 /history/{prompt_id}（间隔0.2s）
+│           └─ GET /view 取回输出 PNG              → grown_png
+│
+└─ 返回 {"grown_png": bytes, "status": "ok"}
+```
+
+---
+
+## 四、用到的模型和外部服务
+
+| 模型/服务 | 用途 | 位置 | 设备 |
+|----------|------|------|------|
+| **FaceLandmarker** (MediaPipe) | 478点人脸检测 | hairline/face_landmarks.py | CPU |
+| **FaceParser** (SegFormer) | 面部分割（hair/skin/...） | hairline/face_parsing.py | CPU (5090不兼容cu121) |
+| **ComfyUI** (Flux-2) | 生发图生成 | hairline/comfyui.py → :8188 | GPU |
+
+**注意**：接口3 **不调用** change_hair 服务（:8801），不需要 swapHair。这是它与接口2女性流程的关键区别。
+
+---
+
+## 五、核心算法：手绘发际线检测
+
+### 5.1 为什么不用简单阈值？
+
+手绘马克笔线条的灰度值与皮肤阴影、抬头纹等重叠，全局阈值无法区分。采用**黑帽变换 + Dijkstra最小路径**方案。
+
+### 5.2 黑帽变换（Black Hat）
+
+```python
+kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (k, k))
+bh = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, kernel)
+```
+
+黑帽 = 闭运算 − 原图，响应"比局部邻域暗的细结构"（即马克笔线条），对抬头纹/眉毛/发丝鲁棒。
+
+### 5.3 Dijkstra 最小代价路径
+
+1. **ROI 限定**：额头上部 ∩ 头部轮廓（排除背景）
+2. **锚点**：左鬓角(21) / 右鬓角(251) MediaPipe 关键点
+3. **代价图**：`cost = (bh.max() - bh) + 1.0`，ROI外设 1e6
+4. **路径**：`route_through_array(cost, 左锚, 右锚)` — skimage 的 Dijkstra 实现
+
+### 5.4 拒识机制
+
+路径平均黑帽响应 < 8.0 → 判定"未画线"，返回 `no_line`。
+
+---
+
+## 六、与接口1、接口2 的对比
+
+| 维度 | 接口1 | 接口2 | 接口3 |
+|------|-------|-------|-------|
+| 功能 | 四庭七眼测量 | C端生发（5种发际线） | B端生发（手绘线） |
+| 路由 | /api/v1/face/measure | /api/v1/hair/grow | /api/v1/hair/grow-b |
+| 输入 | 正面照 | 正面照 | 划线图 |
+| MediaPipe | ✅ | ✅ | ✅ |
+| SegFormer | ✅ | ✅ | ✅ |
+| change_hair | ❌ | ✅（女性） | ❌ |
+| ComfyUI | ❌ | ✅（Flux-2重绘） | ✅（Flux-2生发） |
+| 典型耗时 | ~2s | ~11s | ~6-8s |
+| ComfyUI工作流 | — | 0716add-hair-api.json | add_hair.json |
+
+---
+
+## 七、测试
+
+- **测试页面**：[static/test_interface3.html](file:///home/ubuntu/hair/static/test_interface3.html)
+- **测试图片**：[image/girl_img/girl13.jpg](file:///home/ubuntu/hair/image/girl_img/girl13.jpg)（需手动在图上画发际线后作为划线图上传）

docs/发际线生发遮罩算法_pushed模式.md

@@ -1,11 +1,14 @@
 # 发际线生发遮罩算法（pushed 模式）
 
-> 对应接口11 `/api/v1/hairline/grow`、接口12 `/api/v1/hairline/grow_v2`，`mask_type=pushed`。
-> 代码：`face_analysis/hairline_grow.py`（`_extract_hairline` / `_pushed_mask` / `compute_mask`）。
+> 对应接口11 `/api/v1/hairline/grow`、接口12 `/api/v1/hairline/grow_v2`。
+> 遮罩算法固定为 pushed；融合算法默认 multiband（多频段金字塔），接口11 可切换 seamless/two_stage/feather。
+> 代码：`face_analysis/hairline_grow.py`（`_extract_hairline` / `_pushed_mask` / `compute_mask` / `_composite`）。
 
 ## 概述
 
-pushed 模式是发际线生发的默认遮罩算法（接口11/12 的 `mask_type` 三选一：`eroded` / `closed` / `pushed`，当前只用 pushed）。它从头发分割结果中提取「头发/皮肤交界线」（发际线），以眉心为圆心逐点径向外推一段距离，与 baseline 组成闭合区域作为最终遮罩。这样遮罩顶部会覆盖现有头发下沿一小段，贴回生发结果时顶部与真头发重叠、过渡自然。
+pushed 是发际线生发的**唯一**遮罩算法。融合算法默认 multiband（多频段金字塔），接口11 暴露 `blend_method` 可切换为 seamless(泊松)/two_stage(泊松→多频段两段式)/feather(羽化)，便于对比调优。它从头发分割结果中提取「头发/皮肤交界线」（发际线），以眉心为圆心逐点径向外推一段距离，与 baseline 组成闭合区域作为最终遮罩。这样遮罩顶部会覆盖现有头发下沿一小段，贴回生发结果时顶部与真头发重叠、过渡自然。
+
+> 接口12 `/api/v1/hairline/grow_v2` 只需传 `image` + `hairline_id`，遮罩和融合全部固定，无需任何算法选择参数。
 
 ## 算法流程（5 步）
 
@@ -54,15 +57,52 @@ segformer（默认）或 bisenet 得到的头发二值掩码。
 
 ## 关键参数
 
+遮罩算法（pushed）固定。融合算法接口11 通过 `blend_method` 可切换（默认 multiband），其余融合参数均可调：
+
 | 参数 | 默认 | 说明 |
 |---|---|---|
-| `mask_type` | `eroded`（接口默认）/ `pushed`（当前推荐） | pushed 走上述流程；eroded/closed 走旧的 top_fill→closed/eroded 流程 |
 | `hairline_push_cm` | 1.0 | 内轮廓径向外推距离（厘米），= push_px / px_per_cm。`px_per_cm` 由虹膜直径标定 |
-| `hairline_edge` | `column` | 兼容保留的入参；新版内轮廓提取（轮廓+内侧判定）不再按它分支，取值不影响结果 |
+| `hairline_edge` | `column` | 兼容保留的入参；内轮廓提取（轮廓+内侧判定）不再按它分支，取值不影响结果 |
+| `mb_levels` | 5 | 多频段金字塔层数（2~6，越大低频色差抹得越宽）|
+| `blend_method` | `multiband` | 接缝融合：multiband(多频段金字塔) / seamless(泊松) / two_stage(泊松→多频段,大色差) / feather(羽化) / alpha_gradient。接口12 固定 multiband |
+| `color_match` | `true` | 融合前 Reinhard 颜色迁移消除整体色差（multiband/feather/alpha_gradient 生效；seamless/two_stage 自带调色故跳过）|
+| `color_match_strength` | 1.0 | 颜色迁移强度（0~1，<1 只迁移部分，防 Reinhard 过度改色）|
+| `mb_feather_px` | 1 | 多频段最细层掩码轻羽化像素（0=不羽化），消除发丝边缘锯齿 |
+| `transition_band_px` | -1 | keep-region 过渡带边距（-1=自动按层数 `2**n`；>=0 用绝对像素与层数解耦）|
+| `edge_erode_px` | 3 | 贴图前遮罩内缩像素（防边缘露皮/光晕）|
+| `erode_cm` | 0.6（接口12 固定）| baseline 参考内缩距离，对 pushed 影响很小 |
+| `redraw` | `false` | 发际线带重绘开关：开启后用 final（④融合图）在「外推线↔发际线」带重绘，swapHair/Flux-2 两路对比，结果单独展示（不替换 final）|
+| `inpainting_fill` | 1 | change_hair 重绘填充：0=保留原图(治染绿) / 1=填充噪声(默认) / 2=纯色 / 3=潜变量噪声 |
+| `mask_blur` | 11 | change_hair 遮罩边缘模糊像素（越大颜色越易从边缘渗透）|
+| `mask_dilate_scale` | 1.0 | change_hair 遮罩膨胀核缩放（1.0=原始，<1 收缩防越界）|
+| `comfyui_prompt` | `null` | redraw Flux-2 路提示词，null 用默认「补充遮罩区域的头发，加一点美颜」|
 
-## 与旧模式（eroded/closed）的区别
+> 接口12 `/api/v1/hairline/grow_v2` 只需传 `image` + `hairline_id`，遮罩和融合全部用默认值（multiband + color_match=true），不暴露算法选择参数。
 
-| | eroded/closed | pushed（当前） |
+### 融合方法选择建议
+
+- **multiband**（默认）：常规首选。低频抹色差、高频保发丝。需配合 `color_match=true` 消除整体色差。
+- **two_stage**：生成图与原图色差大时用。先泊松克隆统一色调，再多频段贴细节，兼顾调色与保发丝。比纯 seamless 更不易溢色。
+- **seamless**：纯泊松梯度域调和，色调统一干净，但可能整体改色/边缘溢色。
+- **feather / alpha_gradient**：单层 alpha 过渡，最轻量，但过渡带内色差不会被抹平，仅适合色差极小的场景。
+
+## 发际线带重绘（redraw，接口11 可选）
+
+`redraw=true` 时，在主流程（④接缝融合 final）之后额外跑一条重绘分支，结果单独展示（`steps.redraw_a` / `redraw_c`），**不替换** final。
+
+**重绘区域** = ①-g 外推发际线（`outer_pts`）与 ①-f 发际线（`inner_pts`）两条折线端点相连组成的带状闭合区域（宽度 ≈ `hairline_push_cm`，只覆盖发际线交界处）。
+
+**两路后端对比**（输入图 + 融合基底都用 final）：
+- **swapHair 路**（`redraw_a`）：final + 带遮罩调 change_hair → final 走 multiband 融合
+- **Flux-2 路**（`redraw_c`）：final + 带遮罩调 ComfyUI（`hair_repaint.json` 工作流）→ final 走 multiband 融合。Flux-2 经 reference latent + ColorMatch 双重保色，**不易染绿**
+
+> `inpainting_fill` / `mask_blur` / `mask_dilate_scale` 透传 change_hair 服务端（仅影响 swapHair 路）。`comfyui_prompt` 仅影响 Flux-2 路。
+> 两路独立容错：任一路失败只跳过该路，不影响另一路和主 final。
+> ⚠️ Flux-2 路需 ComfyUI（8188）在跑；swapHair 路需 change_hair（8801）在跑。
+
+## 与旧模式（eroded/closed，已移除）的区别
+
+| | eroded/closed（已移除） | pushed（当前唯一） |
 |---|---|---|
 | 遮罩来源 | top_fill（头发向下填充含额头）外缘内缩 | 头发内轮廓线 径向外推成带 |
 | 截断方式 | 用 baseline 组上半区 upper | 内侧判定 + 下颌 y 截断（不再用 baseline） |

docs/接口11_hair_test运行记录.md

@@ -0,0 +1,182 @@
+# 接口11 运行记录 — `image/hair_test.jpg`
+
+> 实测时间：2026-07-15  
+> 调用：`POST http://127.0.0.1:8187/api/v1/hairline/grow`  
+> 鉴权：`X-Internal-Token: dev-shared-secret-2026`  
+> 输入图：`image/hair_test.jpg`（1257×1495）  
+> `hairline_id`：`chang_zhixian`（直线）；其余全部走接口默认值  
+> 业务结果：`code=0`，`rid=bc7205a4`  
+> 产物目录：`docs/iface11_hair_test_run/`
+
+---
+
+## 1. 本次调用用到的全部默认参数
+
+未在 Form 里显式传的参数均取 `app.py` / `generate_hairline_grow` 默认值；下表即本次实际生效值。
+
+| 参数 | 本次值 | 说明 |
+|------|--------|------|
+| `hairline_id` | `chang_zhixian` | **必填**。发际线类型 = change_hair 的 `hair_id`（直线） |
+| `gen_backend` | `swaphair` | 生成后端：换发型 LoRA |
+| `hairgrow_strength` | `0.75` | 仅 `hairgrow` 后端用；本次未走该路径 |
+| `is_hr` | `false` | 高清关闭（576×768 档，非 1152×1536） |
+| `seg_model` | `segformer` | 头发分割模型 |
+| `erode_cm` | `0.6` | baseline 参考内缩（cm）；pushed 下影响很小 |
+| `hairline_push_cm` | `1.0` | 发际线内轮廓径向外推距离（cm） |
+| `hairline_edge` | `column` | 兼容入参；当前内轮廓提取不再按它分支 |
+| `swap_mode` | `ext_mask` | 把 pushed 遮罩作为 `ext_mask` 传给 swapHair |
+| `edge_erode_px` | `3` | 贴图前遮罩内缩像素 |
+| `denoising_strength` | `0.6` | 换发型 webui 重绘强度 |
+| `mb_levels` | `5` | 多频段金字塔层数 |
+| `blend_method` | `multiband` | 接缝融合：多频段金字塔 |
+| `color_match` | `true` | 融合前 Reinhard 颜色迁移 |
+| `color_match_strength` | `1.0` | 颜色迁移强度（全迁移） |
+| `mb_feather_px` | `1` | 多频段最细层掩码轻羽化 |
+| `transition_band_px` | `-1` | keep-region 过渡带：自动按层数 `2**n` |
+| `redraw` | `false` | 发际线带重绘关闭 |
+| `inpainting_fill` | `1` | change_hair 填充噪声 |
+| `mask_blur` | `11` | change_hair 遮罩边缘模糊像素 |
+| `mask_dilate_scale` | `1.0` | change_hair 遮罩膨胀缩放 |
+| `comfyui_prompt` | `null` | 仅 `redraw`+Flux-2 路用；本次未用 |
+| `mask_type` | `pushed`（固定） | 代码写死，不可选 |
+
+图片入参：仅传了 `image_file`（三选一中的文件上传）。
+
+---
+
+## 2. 返回元数据（无 base64）
+
+| 字段 | 值 |
+|------|-----|
+| `px_per_cm` | 47.5311（虹膜直径标定） |
+| `erode_px` | 29（≈ 0.6cm × px_per_cm） |
+| `hair_pixels` | 186798 |
+| `closed_pixels` | 191712 |
+| `mask_pixels` | 140299 |
+| `image_size` | 1257 × 1495 |
+| `timings_ms.mask` | 1462 |
+| `timings_ms.swap` | 5596 |
+| `timings_ms.blend` | 220 |
+| `redraw.enabled` | false |
+| 总耗时（curl） | ≈ 7.4 s |
+
+完整精简 JSON：`docs/iface11_hair_test_run/response_meta.json`  
+完整原始响应（含 base64）：`docs/iface11_hair_test_run/response.json`
+
+---
+
+## 3. 管线分步说明与产物
+
+管线：① pushed 遮罩 → ② swapHair 生成 → ③ 硬贴回 → ④ multiband 融合。  
+各步图保存在 `docs/iface11_hair_test_run/steps/`。
+
+### ①-a 发际线分割线（baseline）
+
+- **做什么**：MediaPipe 关键点连成眉骨折线（中心为 151 眉心），并向左右边缘水平延长。
+- **图**：[`steps/baseline_overlay.jpg`](iface11_hair_test_run/steps/baseline_overlay.jpg)
+- **含义**：黄线 = baseline；151 中心点为后续径向外推圆心。
+
+### ①-b 分割线上半区（upper）
+
+- **做什么**：baseline 折线以上的多边形区域，作为后续裁剪范围。
+- **图**：[`steps/upper_overlay.jpg`](iface11_hair_test_run/steps/upper_overlay.jpg)
+- **含义**：青 = 上半区。
+
+### ①-c 头发分割（hair_seg）
+
+- **做什么**：SegFormer 得到头发二值掩码。
+- **图**：[`steps/hair_seg_overlay.jpg`](iface11_hair_test_run/steps/hair_seg_overlay.jpg)
+- **含义**：绿 = 原始头发像素（本次 `hair_pixels=186798`）。
+
+### ①-d / ①-e（旧 eroded/closed 中间步）
+
+- pushed 模式**不走**这两步；返回字段为空字符串。
+- `top_fill_overlay` / `closed_overlay`：本次无图。
+
+### ①-f 头发内轮廓线（hairline）
+
+- **做什么**：取头发朝脸一侧的内轮廓（额头弧 + 两侧到下颌），有序折线。
+- **图**：[`steps/hairline_overlay.jpg`](iface11_hair_test_run/steps/hairline_overlay.jpg)
+- **含义**：绿 = 内轮廓；黄 = baseline。
+
+### ①-g 外推发际线（pushed）
+
+- **做什么**：以眉心 151 为圆心，内轮廓逐点向外推 `hairline_push_cm=1.0`（≈ 47.5 px），与 baseline 组闭合区域。
+- **图**：[`steps/pushed_overlay.jpg`](iface11_hair_test_run/steps/pushed_overlay.jpg)
+- **含义**：青 = 外推线；红 = 外推遮罩区域。
+
+### ① 最终遮罩
+
+- **叠加图**：[`steps/mask_overlay.jpg`](iface11_hair_test_run/steps/mask_overlay.jpg) — 红 = 遮罩区（贴回/生成区）
+- **纯遮罩**：[`steps/mask.png`](iface11_hair_test_run/steps/mask.png) — 白 = 生成/贴回区  
+- 本次 `mask_pixels=140299`；贴图前再内缩 `edge_erode_px=3`。
+
+### ② 生成全帧（swap_raw）
+
+- **做什么**：`gen_backend=swaphair` + `swap_mode=ext_mask`，把遮罩交给 change_hair（`:8801`），LoRA=`chang_zhixian`，`denoising_strength=0.6`。
+- **图**：[`steps/swap_raw.jpg`](iface11_hair_test_run/steps/swap_raw.jpg)
+- **含义**：生成结果已与原图同分辨率对齐；耗时约 5.6 s。
+
+### ③ 严格按遮罩贴回（hard_paste）
+
+- **做什么**：遮罩内用生成图，遮罩外保持原图，无融合。
+- **图**：[`steps/hard_paste.jpg`](iface11_hair_test_run/steps/hard_paste.jpg)
+- **含义**：用于对比接缝融合前后差异。
+
+### ④ 融合权重 alpha + 最终结果
+
+- **做法**：`blend_method=multiband`，`mb_levels=5`，`color_match=true`（强度 1.0），`mb_feather_px=1`。
+- **alpha**：[`steps/alpha.png`](iface11_hair_test_run/steps/alpha.png) — 白 = 更多采用生成图
+- **最终输出**：[`steps/final.jpg`](iface11_hair_test_run/steps/final.jpg)（副本：[`final.jpg`](iface11_hair_test_run/final.jpg)）
+- **输入对照**：[`steps/input.jpg`](iface11_hair_test_run/steps/input.jpg)
+
+### ⑤ 发际线带重绘（本次关闭）
+
+`redraw=false`，故 `redraw_band_overlay` / `redraw_a` / `redraw_c` 均为空。
+
+---
+
+## 4. 最终输出
+
+**主结果文件**：[`docs/iface11_hair_test_run/final.jpg`](iface11_hair_test_run/final.jpg)
+
+含义：同一人、同一发型观感下，按直线发际线类型（`chang_zhixian`）压低发际线后的合成图；遮罩外像素保持原图不动。
+
+---
+
+## 5. 复现命令
+
+```bash
+curl -sS -X POST "http://127.0.0.1:8187/api/v1/hairline/grow" \
+  -H "X-Internal-Token: dev-shared-secret-2026" \
+  -F "image_file=@image/hair_test.jpg" \
+  -F "hairline_id=chang_zhixian" \
+  -o docs/iface11_hair_test_run/response.json
+```
+
+（其余参数全部省略即可走默认值。）
+
+---
+
+## 6. 产物清单
+
+```
+docs/接口11_hair_test运行记录.md          ← 本文档
+docs/iface11_hair_test_run/
+  final.jpg                               ← 最终结果
+  response.json                           ← 完整 API 响应（含 base64）
+  response_meta.json                      ← 去掉大图的元数据
+  steps/
+    input.jpg
+    baseline_overlay.jpg
+    upper_overlay.jpg
+    hair_seg_overlay.jpg
+    hairline_overlay.jpg
+    pushed_overlay.jpg
+    mask_overlay.jpg
+    mask.png
+    swap_raw.jpg
+    hard_paste.jpg
+    alpha.png
+    final.jpg
+```

docs/接口文档.md

@@ -20,7 +20,6 @@
 | 3 B 端生发 | POST | `/api/v1/hair/grow-b` |
 | 4 用户特征 | POST | `/api/v1/face/features` |
 | 5 发际线 PNG 生成 | POST | `/api/v1/hairline/generate` |
-| 7 C 端生发 v2 | POST | `/api/v1/hair/grow-v2` |
 
 ---
 
@@ -87,6 +86,7 @@
 | 1006 | 文件超出大小限制 | 单文件超过 1 MB |
 | 1007 | 图片参数错误 | file / url / base64 未传，或同时传了多个（三者严格互斥） |
 | 1008 | 图片格式不支持 | 非 JPG / PNG |
+| 1009 | 未授权 | 缺少或错误的 `X-Internal-Token`（`/api/*` 路径鉴权） |
 
 ---
 
@@ -109,6 +109,8 @@
 | four_courts | object | 四庭数据，见下表 |
 | seven_eyes | object | 七眼数据，见下表 |
 | landmarks | object | 关键分界点坐标（头顶 / 发际线 / 眉心 / 鼻翼下缘 / 下巴尖），原图像素坐标 |
+| left_position | object | MediaPipe 21 号关键点坐标（左脸定位点），原图像素：`{ "x": int, "y": int }` |
+| right_position | object | MediaPipe 251 号关键点坐标（右脸定位点，与 21 号镜像），原图像素：`{ "x": int, "y": int }` |
 
 `four_courts`（四庭，自上而下）：
 
@@ -128,6 +130,15 @@
 | face_width_cm | number | 脸宽（cm） |
 | inter_eye_distance_cm | number | 两眼间距（cm） |
 | ratios | object | 七眼各段占脸宽的比例 |
+| eye1 | number \| null | 从左到右第 1 段宽度（cm）：人头最左 → 左脸颊（左耳外侧段）。该侧耳朵不可见时为 null |
+| eye2 | number | 从左到右第 2 段宽度（cm）：左脸颊 → 左眼外角 |
+| eye3 | number | 从左到右第 3 段宽度（cm）：左眼外角 → 左眼内角（左眼宽度） |
+| eye4 | number | 从左到右第 4 段宽度（cm）：左眼内角 → 右眼内角（两眼间距） |
+| eye5 | number | 从左到右第 5 段宽度（cm）：右眼内角 → 右眼外角（右眼宽度） |
+| eye6 | number | 从左到右第 6 段宽度（cm）：右眼外角 → 右脸颊 |
+| eye7 | number \| null | 从左到右第 7 段宽度（cm）：右脸颊 → 人头最右（右耳外侧段）。该侧耳朵不可见时为 null |
+
+> `eye1`~`eye7` 为从左到右共 7 段宽度，与标注图竖线一一对应。最左/最右端线取自耳朵分割外缘；某侧耳朵被头发或侧脸遮挡（不可见）时该侧端线省略，对应 `eye1` 或 `eye7` 为 `null`（键始终保留），实际有效段为 5 或 6 段。`eye3`/`eye5` 为左右眼宽、`eye4` 为两眼间距，与 `eye_width_cm` / `inter_eye_distance_cm` 语义一致。
 
 ### 标注图片（UI）规范
 
@@ -161,7 +172,9 @@
       "eye_width_cm": 3.44,
       "face_width_cm": 24.08,
       "inter_eye_distance_cm": 3.44,
-      "ratios": { "eye_width": 0.143, "inter_eye_distance": 0.143 }
+      "ratios": { "eye_width": 0.143, "inter_eye_distance": 0.143 },
+      "eye1": 3.44, "eye2": 3.44, "eye3": 3.44, "eye4": 3.44,
+      "eye5": 3.44, "eye6": 3.44, "eye7": 3.44
     },
     "landmarks": {
       "hair_top": { "x": 540, "y": 120 },
@@ -198,8 +211,12 @@
 | annotated_image_url | string | 标注图层 PNG URL（透明底，仅标注线/文字，不含人物） |
 | face_total_height_cm | number | 面部总高度（cm）= 上庭 + 中庭 + 下庭（**不含顶庭**） |
 | four_courts | object | 三庭数据（上/中/下庭，各含 cm 与 ratio；**无顶庭**） |
-| seven_eyes | object | 七眼数据（眼宽/脸宽/两眼间距，各含 cm 与 ratio） |
+| seven_eyes | object | 七眼数据（眼宽/脸宽/两眼间距 cm + 占比 ratios + **eye2~eye6** 共 5 段宽度） |
 | landmarks | object | 四个关键点像素坐标（发际线/眉心/鼻翼下缘/下巴尖） |
+| left_position | object | MediaPipe 21 号关键点坐标（左脸定位点），原图像素：`{ "x": int, "y": int }` |
+| right_position | object | MediaPipe 251 号关键点坐标（右脸定位点，与 21 号镜像），原图像素：`{ "x": int, "y": int }` |
+
+> 接口6 是**三庭五眼**：`four_courts`/`landmarks` 不含顶庭与头顶点（无 `top_court_cm`/`hair_top`）；`seven_eyes` 只含 **eye2~eye6**（左脸颊/左眼/两眼间距/右眼/右脸颊，5 段），**无 eye1/eye7**（耳外段需头发轮廓端线，仅接口1 有）。
 
 ### 响应示例
 
@@ -217,7 +234,8 @@
     },
     "seven_eyes": {
       "eye_width_cm": 3.44, "face_width_cm": 24.08, "inter_eye_distance_cm": 3.44,
-      "ratios": { "eye_width": 0.143, "inter_eye_distance": 0.143 }
+      "ratios": { "eye_width": 0.143, "inter_eye_distance": 0.143 },
+      "eye2": 3.0, "eye3": 3.44, "eye4": 3.44, "eye5": 3.44, "eye6": 3.0
     },
     "landmarks": {
       "hairline": { "x": 540, "y": 430 },
@@ -233,10 +251,10 @@
 
 ## 接口 2：C 端生发接口
 
-**说明**：输入用户正面照 + 性别 + 发型序号（可多选），按指定发际线类型渲染预览图 + 生发图。
+**说明**：输入用户正面照 + 性别 + 发型序号（可多选），按指定发际线类型渲染发际线曲线透明 PNG + 生发图。
 
-> **每个方案返回两张图**：`image_url`=「原照片 + 发际线曲线叠加的**预览图**」；`grown_image_url`=
-> 经 ComfyUI/Flux 的「植发 3 个月**生发后图片**」。两者均已实现，实现简述见 [`实现说明.md`](实现说明.md)。
+> **每个方案返回两张图**：`image_url`=「发际线曲线**透明 PNG**（仅白色曲线，透明底，需叠加原图显示）」；`grown_image_url`=
+> 经 ComfyUI/Flux 的「植发 3 个月**生发后图片**」（完整人像照片）。两者均已实现，实现简述见 [`实现说明.md`](实现说明.md)。
 
 **请求**：`POST /api/v1/hair/grow`
 
@@ -250,6 +268,7 @@
 | hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`），最多不超过该性别的预设数。female：1=ellipse, 2=flower, 3=heart, 4=straight, 5=wave；male：1=ellipse, 2=inverse_arc, 3=m, 4=straight。越界/非法返回 `1007` |
 | beauty_enabled | bool | 否 | 生发图是否带美颜效果，默认 false（当前阶段不生效） |
 | use_mask | bool | 否 | 是否启用 inpaint 遮罩，默认 `true`。`false` 时用干净原图生成（空遮罩、不烧模板黑线），供测试对比 |
+| prompt | string | 否 | ComfyUI 提示词，默认「补充遮罩区域的头发，加一点美颜」，会替换工作流节点 60 的文本 |
 
 ### 输出（data）
 
@@ -257,14 +276,16 @@
 
 | 字段 | 类型 | 说明 |
 |------|------|------|
-| image_url | string | 方案**预览图** URL（发际线曲线叠加图） |
-| grown_image_url | string | **生发后图片** URL（ComfyUI/Flux「植发 3 个月」效果图） |
+| image_url | string | 发际线曲线**透明 PNG** URL（仅白色发际线曲线，透明底，**不含人物**，需前端叠加原图显示） |
+| grown_image_url | string | **生发后图片** URL（ComfyUI/Flux「植发 3 个月」效果图，完整人像照片） |
 | hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`（female），`ellipse`/`m`/`straight`/`inverse_arc`（male） |
 | order | int | 排序序号（当前阶段固定 `1..N`，按贴图顺序，暂不计算合适度） |
 
 > ⚠️ 生发图由本机 ComfyUI（Flux-2，端口 8182）生成，**一次请求生成指定发型的 1 张、同步返回**。
 > worker 侧返回 `image_base64` / `grown_image_base64`，
 > 网关落盘后改写为上表的 `image_url` / `grown_image_url`。
+>
+> 💡 `image_url` 为透明底 PNG，前端需用绝对定位叠加到原图上显示（参考[测试页](https://hair.xiangsilian.com/static/test_interface2.html)的 `.img-stack` 叠加结构）。
 
 ### 响应示例
 
@@ -302,6 +323,7 @@
 |------|------|------|------|
 | marked_image_* | file / string | 是 | 已用马克笔标注发际线的图片，三选一 |
 | use_mask | bool | 否 | 是否画发际线，默认 `true`。`false` 时跳过划线检测、直接送划线图，模型仅凭手绘黑线生发，供测试对比 |
+| prompt | string | 否 | ComfyUI 提示词，默认「补充遮罩区域的头发，加一点美颜」，会替换工作流节点 60 的文本 |
 
 ### 输出（data）
 
@@ -386,7 +408,8 @@
 | gender | string | **是** | 性别：`male` / `female`。决定发型集合（female 5 / male 4）。缺失/非法返回 `1004` |
 | hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`），决定返回哪些发际线类型。female：1=ellipse, 2=flower, 3=heart, 4=straight, 5=wave；male：1=ellipse, 2=inverse_arc, 3=m, 4=straight。缺失/越界/非法返回 `1007` |
 | use_mask | bool | 否 | 生发是否启用 inpaint 遮罩，默认 `true`。`false` 时用干净原图生成（空遮罩、不烧模板黑线），供测试对比 |
-| prompt | string | 否 | ComfyUI 提示词，默认「补充遮罩区域的头发」，会替换工作流节点 60 的文本 |
+| prompt | string | 否 | ComfyUI 提示词，默认「补充遮罩区域的头发，加一点美颜」，会替换工作流节点 60 的文本 |
+| generate_grow_image | bool | 否 | 是否生成生发效果图（ComfyUI 生发，全流程最耗时），默认 `true`。传 `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，可大幅降低耗时 |
 
 > ⚠️ 三档叠图分别用 `hairline_texture` / `hairline_texture_high` / `hairline_texture_low` 三套同名贴图；**生发黑模板固定取自 `hairline_texture_black/`（middle 档）**，即生发目标固定压到 middle 档，每个发型仅 1 张生发图。
 
@@ -395,20 +418,40 @@
 | 字段 | 类型 | 说明 |
 |------|------|------|
 | hairline_images | object[] | **选中发型**列表，**数量 = 所选发型数**，元素见下表 |
-| best_hairline_center_point | object | **首个选中发型**的 middle 档发际线曲线「面部中间点」坐标，原图像素：`{ "x": number, "y": number }` |
+| best_hairline_center_point | object \| null | **首个选中发型**的 **middle 档**发际线曲线「面部中间点」坐标，原图像素：`{ "x": number, "y": number }` |
+| high_hairline_center_point | object \| null | 同上，**high 档**发际线中点（发际线偏高） |
+| low_hairline_center_point | object \| null | 同上，**low 档**发际线中点（发际线偏低） |
+| face_measure | object \| null | **复用接口1**的四庭七眼测量**数值**（不含标注图）。独立流程，测量失败（无人脸/非正面/分割失败）时为 `null`，不影响发际线主结果。字段结构见下表 |
 
 `hairline_images` 元素：
 
 | 字段 | 类型 | 说明 |
 |------|------|------|
 | hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`（female），`ellipse`/`m`/`straight`/`inverse_arc`（male） |
-| image_middle_url | string | middle 档发际线叠加图 URL |
-| image_high_url | string | high 档发际线叠加图 URL |
-| image_low_url | string | low 档发际线叠加图 URL |
-| grown_image_url | string \| null | **生发后图片** URL（ComfyUI「植发」效果图，生发失败时为 `null`） |
+| image_middle_url | string | middle 档发际线曲线**透明 PNG** URL（仅曲线，透明底，**不含人物**，需叠加原图显示） |
+| image_high_url | string | high 档发际线曲线**透明 PNG** URL（同上，high 档曲线） |
+| image_low_url | string | low 档发际线曲线**透明 PNG** URL（同上，low 档曲线） |
+| grown_image_url | string \| null | **生发后图片** URL（ComfyUI「植发」效果图，完整人像照片，生发失败或 `generate_grow_image=false` 时为 `null`） |
 | order | int | 发型序号（= 传入的 hair_style 值） |
 
 > worker 侧返回 `image_middle_base64` / `image_high_base64` / `image_low_base64` / `grown_image_base64`，网关落盘后改写为上表对应的 `*_url`。
+>
+> 💡 三档 `image_*_url` 为透明底 PNG，前端需用绝对定位叠加到原图上显示（参考[测试页](https://hair.xiangsilian.com/static/test_interface5.html)的 `.img-stack` 叠加结构）。`grown_image_url` 是完整人像照片，直接显示即可。
+
+`face_measure` 元素（与[接口1](#接口-1四庭七眼测量标注接口)的 `data` 同构，**不含** `annotated_image_*` 标注图字段）：
+
+| 字段 | 类型 | 说明 |
+|------|------|------|
+| face_total_height_cm | number | 全脸总高度（cm）= 四庭之和 |
+| four_courts | object | 四庭数据（顶/上/中/下庭 cm + 占比 ratios），结构同接口1 |
+| seven_eyes | object | 七眼数据（眼宽/脸宽/两眼间距 cm + 占比 ratios + eye1~eye7 从左到右 7 段宽度），结构同接口1 |
+| landmarks | object | 5 个纵向关键点像素坐标（hair_top/hairline/brow_center/nose_bottom/chin_tip），结构同接口1 |
+| hairline_source | string | 发际线来源：`segmentation`（真实分割）/ `estimated`（比例估算） |
+| head_pose | object | 头部姿态角度（yaw/pitch/roll，单位：度） |
+| left_position | object | MediaPipe 21 号关键点坐标（左脸定位点），原图像素：`{ "x": int, "y": int }` |
+| right_position | object | MediaPipe 251 号关键点坐标（右脸定位点，与 21 号镜像），原图像素：`{ "x": int, "y": int }` |
+
+> `eye1`~`eye7` 为从左到右共 7 段宽度，eye1=左耳外段、eye7=右耳外段，某侧耳朵不可见时对应段为 `null`。详见接口1说明。
 
 ### 响应示例（当前 Mock 返回值）
 
@@ -436,7 +479,34 @@
         "order": 3
       }
     ],
-    "best_hairline_center_point": { "x": 540, "y": 430 }
+    "best_hairline_center_point": { "x": 540, "y": 430 },
+    "high_hairline_center_point": { "x": 540, "y": 380 },
+    "low_hairline_center_point": { "x": 540, "y": 480 },
+    "face_measure": {
+      "face_total_height_cm": 26.76,
+      "four_courts": {
+        "top_court_cm": 5.77, "upper_court_cm": 5.93,
+        "middle_court_cm": 7.62, "lower_court_cm": 7.44,
+        "ratios": { "top_court": 0.216, "upper_court": 0.222,
+                    "middle_court": 0.285, "lower_court": 0.278 }
+      },
+      "seven_eyes": {
+        "eye_width_cm": 2.76, "face_width_cm": 15.08,
+        "inter_eye_distance_cm": 3.9,
+        "ratios": { "eye_width": 0.183, "inter_eye_distance": 0.259 },
+        "eye1": null, "eye2": 3.0, "eye3": 2.76, "eye4": 3.9,
+        "eye5": 2.76, "eye6": 3.0, "eye7": null
+      },
+      "landmarks": {
+        "hair_top": { "x": 504, "y": 103 },
+        "hairline": { "x": 504, "y": 228 },
+        "brow_center": { "x": 504, "y": 357 },
+        "nose_bottom": { "x": 505, "y": 522 },
+        "chin_tip": { "x": 506, "y": 683 }
+      },
+      "hairline_source": "segmentation",
+      "head_pose": { "yaw": -1.39, "pitch": 2.49, "roll": -0.06 }
+    }
   }
 }
 ```
@@ -445,59 +515,6 @@
 
 ---
 
-## 接口 7：C 端生发 v2 接口
-
-**说明**：功能与[接口 2](#接口-2c-端生发接口)完全一致，仅 ComfyUI 工作流不同——使用 `add_hair2.json` 替代 `add_hair.json`。
-
-**请求**：`POST /api/v1/hair/grow-v2`
-
-### 输入
-
-与接口 2 完全相同。图片参数见「通用约定 → 图片传参字段」。专属参数：
-
-| 参数 | 类型 | 必填 | 说明 |
-|------|------|------|------|
-| gender | string | **是** | 性别：`male` / `female`。决定使用的发际线贴图集合 |
-| hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`）。female：1=ellipse, 2=flower, 3=heart, 4=straight, 5=wave；male：1=ellipse, 2=inverse_arc, 3=m, 4=straight。越界/非法返回 `1007` |
-| beauty_enabled | bool | 否 | 生发图是否带美颜效果，默认 false（当前阶段不生效） |
-| use_mask | bool | 否 | 是否启用 inpaint 遮罩，默认 `true`。`false` 时用干净原图生成（空遮罩、不烧模板黑线） |
-
-### 输出（data）
-
-与接口 2 完全相同。`results`：发际线方案数组，**数量 = 所选发型数**。每个元素：
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| image_url | string | 方案**预览图** URL（发际线曲线叠加图） |
-| grown_image_url | string | **生发后图片** URL（ComfyUI/Flux「植发 3 个月」效果图） |
-| hairline_type | string | 发际线类型 key |
-| order | int | 排序序号 |
-
-> ⚠️ 与接口 2 的区别：本接口使用 `add_hair2.json` 工作流（Flux-2 Klein 9b），输入/遮罩节点同为 26，
-> SaveImage 输出节点为 75。
-
-### 响应示例
-
-```json
-{
-  "code": 0,
-  "message": "success",
-  "request_id": "mock-request-id",
-  "data": {
-    "results": [
-      {
-        "image_url": "https://hair.xiangsilian.com/static/sample.jpg",
-        "grown_image_url": "https://hair.xiangsilian.com/static/sample.jpg",
-        "hairline_type": "ellipse",
-        "order": 1
-      }
-    ]
-  }
-}
-```
-
----
-
 ## 汇总：输入输出一览
 
 | 接口 | 输入 | 主要输出 |
@@ -508,7 +525,6 @@
 | 3 B 端生发 | 划线图片 | 最合适发际线图片 + 生发后图片 |
 | 4 用户特征 | 用户照片 | 6 个用户特征字段（脸形/眉形/年龄/动静/性别/基因风格） |
 | 5 发际线 PNG | 用户照片 + gender + hair_style（多选） | 每个选中发型 middle/high/low 三档发际线叠图 + 生发图 + 最合适发际线面部中间点坐标 |
-| 7 C 端生发 v2 | 用户照片 + gender + hair_style | 同接口2，使用 add_hair2.json 工作流 |
 
 ---
 

face_analysis/annotation.py

@@ -6,9 +6,9 @@
 - 纵向竖线 8 条：人头最左 + 左脸颊/左眼外/内角/右眼内/外角/右脸颊 + 人头最右，
   把头宽切 7 段（七眼），段宽数值上下交替（上 3 / 下 4），带虚线双箭头。
   人头最左/最右取自耳朵分割外缘，看不到耳朵则省略该侧（最少 6 点 5 段）。
-- 四庭：图片左侧，「名」上「数值」下两行换行（不带 cm），带竖向虚线双箭头。
+- 四庭：图片左侧，「名」「数值(带 cm)」「百分比」三行换行，带竖向虚线双箭头。
 - 五条横线右侧标名：头顶/发际线/眉心/鼻翼下缘/下巴尖。
-- 单位 cm 统一标在底部「单位cm」。
+- 每段数值直接带 cm 后缀，下方另起一行标百分比（不再单独标底部「单位cm」）。
 中文字体用打包的思源黑体绝对路径加载，缺字体直接抛错（不静默降级成方块）。
 """
 import os
@@ -189,9 +189,8 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
       取自耳朵分割掩膜的外缘（方案 B，BiSeNet 类 7/8）；耳朵不可见（被头发/侧脸
       遮挡 → 掩膜空）或无掩膜时省略该侧端线，只画对应脸颊线。
     - 横向 5 条分界线：头顶/发际线/眉心/鼻翼下缘/下巴尖，右侧标名。
-    - 四庭（顶/上/中/下庭）在左侧：名 + 数值两行换行（无 cm），竖向虚线双箭头。
-    - 七眼段宽数值上下交替（上 3 / 下 4，无 cm），横向虚线双箭头。
-    - 底部统一标「单位cm」。
+    - 四庭（顶/上/中/下庭）在左侧：名 + 数值(带 cm) + 百分比三行换行，竖向虚线双箭头。
+    - 七眼段宽上下交替（上 3 / 下 4）：数值(带 cm) 上、百分比（占头宽比）下，横向虚线双箭头。
 
     variant="v6"（接口6）：去掉头顶横线与顶庭（只画发际线/眉心/鼻翼下缘/下巴尖 4 条
     横线 + 上/中/下庭），竖线纵向范围改为发际线→下巴尖，且不画人头最左/最右端线
@@ -203,7 +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(11, round(s * 0.026))     # 字体更小
+    font_size = max(9, round(s * 0.020))      # 字号上调一档
     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）
@@ -213,7 +212,11 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
 
     buf = np.zeros((h, w, 4), dtype=np.uint8)
 
-    if variant == "v6":
+    # 发际线弃用（hairline_discarded）：保留头顶横线，去掉发际线横线，
+    # 也不标顶/上庭（缺发际线作边界，算不出）。横线 = 头顶/眉心/鼻翼下缘/下巴尖。
+    if getattr(measure_result, "hairline_discarded", False):
+        order = ["hair_top", "brow_center", "nose_bottom", "chin_tip"]
+    elif variant == "v6":
         order = ["hairline", "brow_center", "nose_bottom", "chin_tip"]
     else:
         order = ["hair_top", "hairline", "brow_center", "nose_bottom", "chin_tip"]
@@ -240,7 +243,7 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
     face_cx = (fx0 + fx1) / 2
     over = max(6, round(s * 0.030))           # 线超出包围盒的长度（参考图风格）
     face_half = (fx1 - fx0) / 2 + over        # 横线超出最外侧竖线一点
-    # v6 竖线纵向范围 = 发际线→下巴尖（不超出）；v1 = 头顶→下巴尖并两端超出一点
+    # 竖线纵向范围：v6 = 发际线→下巴尖（不超出）；v1（含发际线弃用）= 头顶→下巴尖并两端超出一点
     v_top = fy0 if variant == "v6" else fy0 - over
     v_bot = fy1 if variant == "v6" else fy1 + over
 
@@ -256,70 +259,85 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
     draw = ImageDraw.Draw(canvas)
     font = _load_font(font_size)
 
-    # --- 3a. 横线右侧：线名（头顶/发际线/眉心/鼻翼下缘/下巴尖），文字在线上方 ---
-    name_x = fx1 + pad
-    name_gap = max(2, round(pad * 1.6))       # 文字底部到线的间距（再上移）
+    # --- 3a. 横线右侧：线名（头顶/发际线/眉心/鼻翼下缘/下巴尖），文字纵向居中对齐到线 ---
+    name_x = fx1 + over + pad                  # 移到横线右端外侧一点（往右）
     for i, name in enumerate(order):
         text = _LINE_NAMES[name]
-        tw, th = _text_size(draw, text, font)
+        tw, _ = _text_size(draw, text, font)
         x = min(name_x, w - 2 - tw)           # 右侧越界时回收
-        draw.text((x, max(2, ys[i] - th - name_gap)), text, fill=LINE_COLOR, font=font)
+        # anchor="lm"：x 为左、y 为竖直中点 → 文字中线正好压在横线上（与线对齐）
+        draw.text((x, ys[i]), text, fill=LINE_COLOR, font=font, anchor="lm")
 
     # --- 3b. 左侧四庭：名 + 数值两行（无 cm）+ 竖向虚线双箭头 ---
-    if variant == "v6":
+    # court_start：庭段在 order 里的起始索引。发际线弃用时 order 首位是头顶（无下界发际线，
+    # 顶/上庭不标），中庭从眉心开始 → 跳过 order[0]。
+    if getattr(measure_result, "hairline_discarded", False):
+        court_cm = [measure_result.middle_cm, measure_result.lower_cm]
+        court_name = ["中庭", "下庭"]
+        n_court = 2
+        court_start = 1
+    elif variant == "v6":
         court_cm = [measure_result.upper_cm, measure_result.middle_cm, measure_result.lower_cm]
         court_name = ["上庭", "中庭", "下庭"]
         n_court = 3
+        court_start = 0
     else:
         court_cm = [measure_result.top_cm, measure_result.upper_cm,
                     measure_result.middle_cm, measure_result.lower_cm]
         court_name = ["顶庭", "上庭", "中庭", "下庭"]
         n_court = 4
+        court_start = 0
     arrow_x = max(arrow_size + 1, fx0 - pad)   # 竖箭头所在 x（脸左侧，贴近最左竖线）
+    court_total = sum(court_cm) or 1.0          # 各庭占比分母 = 四庭（v6 三庭）之和
     for i in range(n_court):
-        y_a, y_b = ys[i], ys[i + 1]
+        y_a, y_b = ys[court_start + i], ys[court_start + i + 1]
         # 竖向虚线双箭头，覆盖该庭高度（略收一点避免压到横线）
         inset = min(arrow_size, (y_b - y_a) * 0.12)
         draw_dashed_line_with_arrows(
             draw, arrow_x, y_a + inset, arrow_x, y_b - inset,
             dash_len=dash_len, gap_len=gap_len, arrow_size=arrow_size, width=line_w)
-        # 名 + 数值两行，右对齐到箭头左侧
+        # 名 + 数值(带 cm) + 百分比三行，右对齐到箭头左侧
         name = court_name[i]
-        val = f"{court_cm[i]:.2f}"
+        val = f"{court_cm[i]:.2f}cm"
+        pct = f"{court_cm[i] / court_total * 100:.1f}%"
         nw, _ = _text_size(draw, name, font)
         vw, _ = _text_size(draw, val, font)
+        pw, _ = _text_size(draw, pct, font)
         label_right = arrow_x - pad
         y_mid = (y_a + y_b) / 2
-        y_top = y_mid - line_h
+        y_top = y_mid - 1.5 * line_h
         draw.text((max(2, label_right - nw), y_top), name, fill=LINE_COLOR, font=font)
         draw.text((max(2, label_right - vw), y_top + line_h), val, fill=LINE_COLOR, font=font)
+        draw.text((max(2, label_right - pw), y_top + 2 * line_h), pct, fill=LINE_COLOR, font=font)
 
-    # --- 4. 七眼每段宽度：上下交替（上 3 / 下 4），横向虚线双箭头 + 数值（无 cm） ---
-    # 文字与箭头间留更大间距，避免文字压住箭头
+    # --- 4. 七眼每段宽度：上下交替（上 3 / 下 4），横向虚线双箭头 + 数值(带 cm) + 百分比 ---
+    # 每段两行：数值(带 cm) 上、百分比 下；百分比分母 = 整个头宽（七段之和）
     txt_off = arrow_size + pad * 2
-    y_arrow_top = max(txt_off + font_size + 2, fy0 - pad - arrow_size)
-    y_arrow_bot = min(h - txt_off - font_size - 2, fy1 + pad + arrow_size)
+    txt_block = 2 * line_h                     # 两行文字总高（数值 + 百分比）
+    y_arrow_top = max(txt_off + txt_block + 2, fy0 - pad - arrow_size)
+    y_arrow_bot = min(h - txt_off - txt_block - 2, fy1 + pad + arrow_size)
+    head_w = (xs[-1] - xs[0]) or 1.0           # 头宽（像素）= 百分比分母
     for i in range(len(xs) - 1):
         x_a, x_b = xs[i], xs[i + 1]
         if x_b - x_a < 1:
             continue
         seg_cm = (x_b - x_a) / pc
+        seg_pct = (x_b - x_a) / head_w * 100
         cx_seg = (x_a + x_b) / 2
-        text = f"{seg_cm:.2f}"
-        tw, th = _text_size(draw, text, font)
+        val = f"{seg_cm:.2f}cm"
+        pct = f"{seg_pct:.1f}%"
+        vw, _ = _text_size(draw, val, font)
+        pw, _ = _text_size(draw, pct, font)
         inset = min(arrow_size, (x_b - x_a) * 0.12)
         on_top = (i % 2 == 1)                 # 奇数段在上 → 上 3 / 下 4
         y_arrow = y_arrow_top if on_top else y_arrow_bot
         draw_dashed_line_with_arrows(
             draw, x_a + inset, y_arrow, x_b - inset, y_arrow,
             dash_len=dash_len, gap_len=gap_len, arrow_size=arrow_size, width=line_w)
-        ty = (y_arrow - th - txt_off) if on_top else (y_arrow + txt_off)
-        draw.text((cx_seg - tw / 2, ty), text, fill=LINE_COLOR, font=font)
-
-    # --- 5. 底部统一单位 ---
-    unit = "单位cm"
-    uw, uh = _text_size(draw, unit, font)
-    draw.text(((w - uw) / 2, h - uh - max(2, pad)), unit, fill=LINE_COLOR, font=font)
+        # 数值行在上、百分比行在下；on_top 时整块置于箭头上方，否则下方
+        text_top = (y_arrow - txt_off - txt_block) if on_top else (y_arrow + txt_off)
+        draw.text((cx_seg - vw / 2, text_top), val, fill=LINE_COLOR, font=font)
+        draw.text((cx_seg - pw / 2, text_top + line_h), pct, fill=LINE_COLOR, font=font)
 
     return canvas
 

face_analysis/face_mesh_landmarks.py

@@ -18,6 +18,8 @@ RIGHT_EYE_INNER = 362  # 右眼内角
 RIGHT_EYE_OUTER = 263  # 右眼外角
 LEFT_CHEEK = 234       # 左脸颧弓（脸宽左端）
 RIGHT_CHEEK = 454      # 右脸颧弓（脸宽右端）
+LEFT_POSITION = 21     # 左脸前侧定位点（脸颊/耳前区域，与 251 镜像）
+RIGHT_POSITION = 251   # 右脸前侧定位点（与 21 镜像）
 
 # --- 鼻尖（solvePnP 用，可选） ---
 NOSE_TIP = 1   # 鼻尖（也有用 4 的版本）

face_analysis/hairline_grow.py

@@ -35,6 +35,7 @@ from face_analysis.detector import detector
 from face_analysis.calibration import estimate_scale_factor
 from face_analysis.head_mask import (
     NoFaceError,
+    BASELINE_IDX,
     _baseline_points,
     _upper_region_mask,
     _bisenet_hair_mask,
@@ -46,8 +47,11 @@ from face_analysis.head_mask import (
     _draw_baseline,
 )
 
-# 调试日志：写 /home/xsl/hair/log/hairline_grow.log，每个步骤详细记录
-_LOG_DIR = "/home/xsl/hair/log"
+# 调试日志：写 <仓库根>/log/hairline_grow.log，每个步骤详细记录（可用 HAIR_LOG_DIR 覆盖）
+_LOG_DIR = os.getenv(
+    "HAIR_LOG_DIR",
+    os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "log"),
+)
 os.makedirs(_LOG_DIR, exist_ok=True)
 logger = logging.getLogger("hairline_grow")
 _log_fh = logging.FileHandler(os.path.join(_LOG_DIR, "hairline_grow.log"), encoding="utf-8")
@@ -60,20 +64,22 @@ SWAP_URL = os.getenv("SWAP_HAIR_URL", "http://127.0.0.1:8801/api/swapHair/v1")
 HAIRGROW_URL = os.getenv("HAIR_GROW_URL", "http://127.0.0.1:8801/api/hairGrow/v1")
 SWAP_TIMEOUT = float(os.getenv("SWAP_HAIR_TIMEOUT", "300"))
 
+# 多频段融合最细层羽化：羽化最细 FEATHER_LAYERS 层（每层核尺寸按尺度放大）。
+# 只羽最细1层效果极弱（其拉普拉斯系数幅度小），羽化 3 层才能明显软化发丝边缘锯齿。
+FEATHER_LAYERS = 3
+
 DEFAULTS = {
     "gen_backend": "swaphair",  # swaphair(换发型LoRA) | hairgrow(区域生发inpaint)
     "is_hr": False,
     "seg_model": "segformer",   # bisenet | segformer
-    "mask_type": "eroded",      # eroded | closed
-    "erode_cm": 1.2,
+    "hairline_push_cm": 1.0,    # 发际线径向外推距离（厘米）
+    "hairline_edge": "column",  # column（逐列下沿）
     "swap_mode": "ext_mask",    # ext_mask | as_is（仅 swaphair）
     "denoising_strength": 0.6,  # 仅 swaphair
     "hairgrow_strength": 0.75,  # 仅 hairgrow
-    "blend_method": "feather",  # feather | alpha_gradient | seamless | multiband
-    "feather_px": 15,
     "edge_erode_px": 3,
-    "color_match": False,     # True 时对生成图做 Reinhard 颜色校正（seamless 下自动跳过）
     "mb_levels": 5,           # multiband 金字塔层数（2~6，越大色差抹得越宽）
+    "erode_cm": 0.6,          # 接口12 固定值（pushed 模式下仅用于 baseline 截断参考，影响很小）
 }
 
 
@@ -96,11 +102,26 @@ def _png_b64(bgr_or_gray):
 
 
 def _gray_b64(gray_float):
-    """0~1 的浮点图 → 灰度 PNG data URI。"""
+    """0~1 的浮字图 → 灰度 PNG data URI。"""
     g = np.clip(gray_float * 255.0, 0, 255).astype(np.uint8)
     return _png_b64(g)
 
 
+def _red_mask_b64(mask_bool, h, w):
+    """布尔遮罩 → 纯红 alpha PNG data URI。
+    遮罩区域 RGBA=(255,0,0,255)，其余区域 RGBA=(0,0,0,0)。
+    供 ComfyUI 重绘接口（/api/v1/redraw）按 alpha 通道识别重绘区。
+
+    注意：cv2.imencode 写 PNG 用的是 **BGRA** 顺序（B,G,R,A），所以要得到
+    浏览器显示的红色 R=255，需赋值 (B=0,G=0,R=255,A=255)。
+    """
+    m = (mask_bool.astype(np.uint8)) * 255 if mask_bool is not None else np.zeros((h, w), np.uint8)
+    rgba = np.zeros((h, w, 4), np.uint8)
+    rgba[m > 0] = (0, 0, 255, 255)            # BGRA: B=0,G=0,R=255 → PNG 读出为红色 + 不透明
+    ok, buf = cv2.imencode(".png", rgba)
+    return "data:image/png;base64," + base64.b64encode(buf.tobytes()).decode() if ok else ""
+
+
 # ---------------------------------------------------------------------------
 # 步骤1：接口9 头发遮罩（复用 head_mask 构件）
 # ---------------------------------------------------------------------------
@@ -296,8 +317,48 @@ def _pushed_mask(hair_mask, upper, baseline_pts, push_px, rid="",
 
 
 
+def _redraw_band_mask(inner_pts, outer_pts, h, w, rid="", upper=None,
+                      lo_mult=0.5, hi_mult=1.5):
+    """重绘带遮罩：由发际线（①-f 内轮廓 inner_pts）沿径向外推方向，取
+    `lo_mult × push` 与 `hi_mult × push` 两条外推线之间的带状区域作为重绘 mask。
+
+    inner_pts / outer_pts 是一一对应的有序点列（outer = inner 径向外推 1.0×push_px），
+    故每点的 1.0× 位移向量 = outer - inner；下界线 = inner + lo_mult×位移，
+    上界线 = inner + hi_mult×位移。闭合环 = 下界线（正向）+ 上界线（反向）首尾相接。
+
+    lo_mult / hi_mult：外推倍率（相对 hairline_push_cm）。默认 0.5 / 1.5，即带位于
+    0.5×push ~ 1.5×push 之间（以内轮廓为 0×、原外推线为 1.0×）。
+
+    upper：①-a baseline 以上区域布尔掩码。传入时把重绘带与它求交集，只保留 baseline
+    以上的部分（两侧鬓角落到 baseline 以下的段会被截掉）。
+
+    返回 band_bool。
+    """
+    lg = lambda msg: logger.info("[%s] %s", rid, msg) if rid else None
+    if len(inner_pts) < 2 or len(outer_pts) < 2:
+        return np.zeros((h, w), dtype=bool)
+    inner_f = np.asarray(inner_pts, dtype=np.float32)
+    outer_f = np.asarray(outer_pts, dtype=np.float32)
+    disp = outer_f - inner_f                       # 每点 1.0×push 的径向位移向量
+    lo_line = inner_f + float(lo_mult) * disp       # 下界外推线（lo_mult×push）
+    hi_line = inner_f + float(hi_mult) * disp       # 上界外推线（hi_mult×push）
+    # 闭合多边形：下界线正向 + 上界线反向，端点自然相连
+    ring = np.vstack([lo_line.astype(np.int32), hi_line[::-1].astype(np.int32)])
+    band_u8 = np.zeros((h, w), dtype=np.uint8)
+    cv2.fillPoly(band_u8, [ring], 255)
+    band = band_u8 > 0
+    raw_px = int(band.sum())
+    # ①-a baseline 截断：只保留 baseline 以上的重绘带
+    if upper is not None:
+        band = band & upper
+    lg(f"_redraw_band_mask: 内轮廓点={len(inner_pts)} lo_mult={lo_mult} hi_mult={hi_mult} "
+      f"band像素(截断前)={raw_px} band像素(截断后)={int(band.sum())}")
+    return band
+
+
 def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm,
-                 hairline_push_cm=0.0, hairline_edge="column", rid=""):
+                 hairline_push_cm=0.0, hairline_edge="column", rid="", render_viz=True,
+                 hair_mask=None):
     """算出布尔遮罩 + 可视化。
 
     seg_model: bisenet | segformer。
@@ -305,6 +366,10 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     hairline_push_cm: 仅 pushed 模式——发际线往头发方向外推的厘米数（进入现有头发）。
     hairline_edge: 仅 pushed 模式——发际线提取方式 column(逐列最低点) | contour(形态学轮廓)。
     rid: 调用方的 request id，用于日志关联。
+    render_viz: 是否生成各阶段叠图 overlay JPG（接口11 调试页用）。接口2/12 路径传 False
+        可跳过 6+ 张 base64 编码，省 ~80ms；数据字段（_inner_pts/_outer_pts/_upper_mask/
+        mask_pixels 等）始终返回，不受影响。
+    hair_mask: 预计算的头发布尔遮罩（来自 SegFormer parse）。传入时跳过重复分割，省 ~0.9s。
     返回 (mask_bool, viz_dict)。
     """
     lg = lambda msg: logger.info("[%s] %s", rid, msg) if rid else None
@@ -318,13 +383,16 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     upper = _upper_region_mask(baseline_pts, w, h)
     lg(f"baseline 第一点={baseline_pts[0]} 末点={baseline_pts[-1]} upper像素={int(upper.sum())}")
 
-    if seg_model == "bisenet":
-        hair_mask = _bisenet_hair_mask(image_bgr, landmarks, w, h)
-    elif seg_model == "segformer":
-        hair_mask = _segformer_hair_mask(image_bgr)
+    if hair_mask is None:
+        if seg_model == "bisenet":
+            hair_mask = _bisenet_hair_mask(image_bgr, landmarks, w, h)
+        elif seg_model == "segformer":
+            hair_mask = _segformer_hair_mask(image_bgr)
+        else:
+            raise ValueError(f"未知 seg_model: {seg_model}")
+        lg(f"头发分割完成 seg_model={seg_model} hair_pixels={int(hair_mask.sum())}")
     else:
-        raise ValueError(f"未知 seg_model: {seg_model}")
-    lg(f"头发分割完成 seg_model={seg_model} hair_pixels={int(hair_mask.sum())}")
+        lg(f"头发分割跳过(复用外部传入) hair_pixels={int(hair_mask.sum())}")
 
     top_fill = _fill_to_baseline(hair_mask, upper)      # 含额头，延伸到图底
     closed = _largest_cc(top_fill & upper)              # 闭合区域：头发+额头，底=基线
@@ -336,7 +404,9 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     if mask_type == "pushed":
         push_px = int(round(max(0.0, hairline_push_cm) * px_per_cm))
         # 圆心 = 151 点（眉心）完整坐标，内侧判定与径向外推共用
-        center = baseline_pts[5] if len(baseline_pts) > 5 else None
+        # 按值查 151 在 BASELINE_IDX 中的位置，避免列表变动后索引错位（曾硬编码 [5]）
+        _idx151 = BASELINE_IDX.index(151) if 151 in BASELINE_IDX else -1
+        center = baseline_pts[_idx151] if _idx151 >= 0 else None
         # 下颌截断线：下巴关键点 152 的 y（内轮廓两侧向下画到这里为止）
         try:
             chin_y = int(round(landmarks.landmark[152].y * h))
@@ -363,48 +433,55 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     # 遮罩计算过程可视化：
     #   eroded/closed 走 top_fill→closed/eroded 流程；
     #   pushed 走 baseline→头发分割→发际线→外推 流程，与 top_fill/closed 无关，故置空。
+    # render_viz=False（接口2/12 路径）时跳过 overlay JPG 编码，只保留数据字段。
     viz = {
         "erode_px": r,
         "hair_pixels": int(hair_mask.sum()),
         "closed_pixels": int(closed.sum()),
         "mask_pixels": int(mask_bool.sum()),
         # 1. 发际线分割线（baseline）：151 中心点标红，其余点标绿，黄线含左右延长线
-        "baseline_overlay_base64": _jpg_b64(_draw_baseline(image_bgr, baseline_pts, w)),
+        "baseline_overlay_base64": _jpg_b64(_draw_baseline(image_bgr, baseline_pts, w)) if render_viz else "",
         # 2. 分割线以上区域（upper 半区）：青色叠加
-        "upper_overlay_base64": _jpg_b64(_overlay(image_bgr, upper, (0, 255, 255))),
+        "upper_overlay_base64": _jpg_b64(_overlay(image_bgr, upper, (0, 255, 255))) if render_viz else "",
         # 3. 头发分割原始结果（hair_mask）：绿色叠加在原图上
-        "hair_seg_overlay_base64": _jpg_b64(_overlay(image_bgr, hair_mask, (0, 255, 0))),
+        "hair_seg_overlay_base64": _jpg_b64(_overlay(image_bgr, hair_mask, (0, 255, 0))) if render_viz else "",
         # 4. top_fill / closed —— 仅 eroded/closed 流程用；pushed 流程无关，留空
-        "top_fill_overlay_base64": "" if mask_type == "pushed"
+        "top_fill_overlay_base64": "" if (mask_type == "pushed" or not render_viz)
             else _jpg_b64(_overlay(image_bgr, top_fill, (255, 0, 0))),
-        "closed_overlay_base64": "" if mask_type == "pushed"
+        "closed_overlay_base64": "" if (mask_type == "pushed" or not render_viz)
             else _jpg_b64(_overlay(image_bgr, closed, (255, 0, 255))),
         # 5. pushed 模式专有（发际线提取/外推）—— 非 pushed 留空
         "hairline_overlay_base64": "",
         "pushed_overlay_base64": "",
         # —— 最终遮罩 ——
-        "mask_overlay_base64": _jpg_b64(_overlay(image_bgr, mask_bool, (0, 0, 255))),
-        "mask_base64": _png_b64((mask_bool.astype(np.uint8)) * 255),
+        "mask_overlay_base64": _jpg_b64(_overlay(image_bgr, mask_bool, (0, 0, 255))) if render_viz else "",
+        "mask_base64": _png_b64((mask_bool.astype(np.uint8)) * 255) if render_viz else "",
     }
     # pushed 模式：补充内轮廓提取 + 外推线可视化
     if pushed_info is not None:
         inner_pts, outer_pts, push_px = pushed_info
-        # ①-f 提取内轮廓：绿=头发内轮廓线（额头弧+两侧到下颌），黄=baseline 折线
-        hl_img = _draw_baseline(image_bgr, baseline_pts, w)  # 画 baseline（黄线+关键点）
-        hl_img = _draw_polyline(hl_img, inner_pts, (0, 255, 0), 3)
-        viz["hairline_overlay_base64"] = _jpg_b64(hl_img)
-        # ①-g 外推：圆心红点(151) + 内轮廓（绿）+ 外推线（青）+ 遮罩（红半透明）
-        ps_img = _draw_polyline(image_bgr.copy(), inner_pts, (0, 255, 0), 2)
-        ps_img = _draw_polyline(ps_img, outer_pts, (0, 255, 255), 3)
-        # 画圆心（151 点）红点，标示径向外推的中心
-        if baseline_pts is not None and len(baseline_pts) > 5:
-            cx151, cy151 = baseline_pts[5]
-            cv2.circle(ps_img, (cx151, cy151), 6, (0, 0, 255), -1, cv2.LINE_AA)
-            cv2.putText(ps_img, "151", (cx151 + 8, cy151 - 8),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1, cv2.LINE_AA)
-        ps_img = _overlay(ps_img, mask_bool, (0, 0, 255), 0.3)
-        viz["pushed_overlay_base64"] = _jpg_b64(ps_img)
+        if render_viz:
+            # ①-f 提取内轮廓：绿=头发内轮廓线（额头弧+两侧到下颌），黄=baseline 折线
+            hl_img = _draw_baseline(image_bgr, baseline_pts, w)  # 画 baseline（黄线+关键点）
+            hl_img = _draw_polyline(hl_img, inner_pts, (0, 255, 0), 3)
+            viz["hairline_overlay_base64"] = _jpg_b64(hl_img)
+            # ①-g 外推：圆心红点(151) + 内轮廓（绿）+ 外推线（青）+ 遮罩（红半透明）
+            ps_img = _draw_polyline(image_bgr.copy(), inner_pts, (0, 255, 0), 2)
+            ps_img = _draw_polyline(ps_img, outer_pts, (0, 255, 255), 3)
+            # 画圆心（151 点）红点，标示径向外推的中心（_idx151 上方已按值查到）
+            if center is not None:
+                cx151, cy151 = center
+                cv2.circle(ps_img, (cx151, cy151), 6, (0, 0, 255), -1, cv2.LINE_AA)
+                cv2.putText(ps_img, "151", (cx151 + 8, cy151 - 8),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1, cv2.LINE_AA)
+            ps_img = _overlay(ps_img, mask_bool, (0, 0, 255), 0.3)
+            viz["pushed_overlay_base64"] = _jpg_b64(ps_img)
         viz["push_px"] = push_px
+        # 重绘带用原始数据：内轮廓点 + 外推点（供 _redraw_band_mask 连端点成带）
+        viz["_inner_pts"] = inner_pts
+        viz["_outer_pts"] = outer_pts
+        # baseline 以上区域，供重绘带按 ①-a baseline 截断（只留上面）
+        viz["_upper_mask"] = upper
     # 记录 viz 各字段是否非空（长度），便于排查前端取不到图的问题
     viz_summary = {k: (len(v) if isinstance(v, str) and v else 0)
                    for k, v in viz.items() if k.endswith("_base64")}
@@ -412,15 +489,32 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     return mask_bool, viz
 
 
+def _segment_hair(image_bgr, seg_model, landmarks, w, h):
+    """对任意图（如 hard_paste 重绘结果）重跑头发分割，返回 bool 掩码。
+
+    与 compute_mask 内部用的同一个 seg_model 逻辑（bisenet 需 landmarks，
+    segformer 不需要），保证第1步（原图头发）与第2步（重绘后头发）分割口径一致。
+    """
+    if seg_model == "bisenet":
+        return _bisenet_hair_mask(image_bgr, landmarks, w, h)
+    elif seg_model == "segformer":
+        return _segformer_hair_mask(image_bgr)
+    else:
+        raise ValueError(f"未知 seg_model: {seg_model}")
+
+
 # ---------------------------------------------------------------------------
 # 步骤2：调 change_hair 换发型
 # ---------------------------------------------------------------------------
 
-def _call_swap(image_bgr, hairline_id, is_hr, ext_mask_bool, denoising_strength):
+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):
     """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。
     """
     import requests
 
@@ -432,6 +526,9 @@ def _call_swap(image_bgr, hairline_id, is_hr, ext_mask_bool, denoising_strength)
         "user_img_path": "data:image/jpeg;base64," + base64.b64encode(ibuf.tobytes()).decode(),
         "output_format": "base64",
         "denoising_strength": float(denoising_strength),
+        "inpainting_fill": int(inpainting_fill),
+        "mask_blur": int(mask_blur),
+        "mask_dilate_scale": float(mask_dilate_scale),
     }
     if ext_mask_bool is not None:
         mbuf = cv2.imencode(".png", (ext_mask_bool.astype(np.uint8)) * 255)[1]
@@ -502,25 +599,60 @@ def _call_hairgrow(image_bgr, mask_bool, strength):
     return result
 
 
+_REPAINT_WORKFLOW = os.path.join(os.path.dirname(os.path.dirname(__file__)), "hair_repaint.json")
+
+
+def _call_comfyui(image_bgr, mask_bool, prompt=None):
+    """调远端 ComfyUI 的 Flux-2 inpaint 工作流（hair_repaint.json），返回与输入同分辨率的 BGR。
+
+    与 swapHair 的区别：ComfyUI 把「原图 VAE 编码作 reference latent + ColorMatch」双重保色，
+    天生不易染色；提示词自由可调（中文）。mask 经 RGBA alpha 通道传入（透明=重绘区）。
+    ComfyUI 不在线时抛 SwapError（由调用方捕获降级）。prompt=None 用工作流内置默认提示词。
+    """
+    import io
+    from hairline.mask import compose_comfy_rgba
+    from hairline.comfyui import COMFYUI_URL, run as comfyui_run, ping
+
+    if not ping():
+        raise SwapError(f"ComfyUI 不可达（{COMFYUI_URL}），redraw Flux-2 路跳过")
+    mask_u8 = (mask_bool.astype(np.uint8)) * 255
+    rgba_img = compose_comfy_rgba(image_bgr, mask_u8)   # alpha=255-mask：透明=重绘区
+    buf = io.BytesIO()
+    rgba_img.save(buf, format="PNG")
+    png_bytes = comfyui_run(buf.getvalue(), prompt=prompt, workflow_path=_REPAINT_WORKFLOW)
+    result = cv2.imdecode(np.frombuffer(png_bytes, np.uint8), cv2.IMREAD_COLOR)
+    if result is None:
+        raise SwapError("ComfyUI 结果解码失败")
+    if result.shape[:2] != image_bgr.shape[:2]:
+        result = cv2.resize(result, (image_bgr.shape[1], image_bgr.shape[0]),
+                            interpolation=cv2.INTER_LANCZOS4)
+    return result
+
+
 # ---------------------------------------------------------------------------
 # 步骤3+4：按遮罩贴回 + 接缝融合
 # ---------------------------------------------------------------------------
 
-def _color_match_to_orig(swap_result, orig, mask_bool):
+def _color_match_to_orig(swap_result, orig, mask_bool, strength=1.0):
     """在 mask_bool 区域内做 Reinhard 颜色迁移：逐通道把 swap_result 的均值/方差对齐 orig。
 
+    strength 控制迁移强度：1.0=完全对齐到 orig（原行为），<1.0 只迁移部分，
+    防止 Reinhard 在某些图上过度改色（如把生成发色整体拉向皮肤色）。
     遮罩外保持 swap_result 原样（不会越界污染）。返回 uint8 BGR。
     """
     m = mask_bool.astype(bool)
-    out = swap_result.astype(np.float32).copy()
+    src_f = swap_result.astype(np.float32)
+    out = src_f.copy()
     if m.sum() < 30:
         return swap_result.copy()
+    strength = float(min(max(strength, 0.0), 1.0))
     for c in range(3):
         src_pix = swap_result[..., c][m].astype(np.float32)
         dst_pix = orig[..., c][m].astype(np.float32)
         s_mean, s_std = src_pix.mean(), src_pix.std() + 1e-6
         d_mean, d_std = dst_pix.mean(), dst_pix.std() + 1e-6
-        out[..., c] = (out[..., c] - s_mean) * (d_std / s_std) + d_mean
+        aligned = (out[..., c] - s_mean) * (d_std / s_std) + d_mean
+        out[..., c] = src_f[..., c] * (1.0 - strength) + aligned * strength
     return np.clip(out, 0, 255).astype(np.uint8)
 
 
@@ -555,10 +687,17 @@ def _multiband_alpha(mask_bool, edge_erode_px):
     return m
 
 
-def _multiband_blend(orig, swap_result, mask_bool, levels, edge_erode_px):
+def _multiband_blend(orig, swap_result, mask_bool, levels, edge_erode_px,
+                     feather_px=1, transition_band_px=-1):
     """多频段（拉普拉斯金字塔）融合：低频用宽窗抹色差，高频用窄窗保发丝。
 
     levels：金字塔层数（2~6），越大则低频色差在越宽范围被抹平。
+    feather_px：最细若干层掩码轻羽化像素（0=不羽化，保持硬二值）。羽化最细 FEATHER_LAYERS
+        层（核尺寸按层尺度放大），消除发丝边缘 1px 硬切锯齿；粗层仍保持二值（否则粗层会
+        把整图混色）。注意：这是消除锯齿的微调，幅度有限（边界 Δ 约 1~3/255），
+        不要指望它做大范围过渡——那是 mb_levels/transition_band_px 的事。
+    transition_band_px：keep-region 外缘边距。-1=自动按层数 2**n（旧行为）；
+        >=0 则用绝对像素，使过渡带宽度与金字塔层数解耦。
     返回 uint8 BGR。
     """
     m = _multiband_alpha(mask_bool, edge_erode_px)
@@ -603,6 +742,21 @@ def _multiband_blend(orig, swap_result, mask_bool, levels, edge_erode_px):
     lb = lap_pyr(swap_result, n)
     ma = mask_pyr(m, n)
 
+    # 最细层（reversed 后末元素 = 全分辨率原始二值掩码）及其下若干层轻羽化，
+    # 消除发丝边缘 1px 硬切锯齿。注意：多频段融合中各层都贡献边界过渡，但最细层的
+    # 拉普拉斯系数幅度最小，只羽化它效果很弱（实测边界 Δ 仅 ~0.25/255）。因此对最细
+    # FEATHER_LAYERS 层都做按尺度放大的羽化（越细的层核越大），才能明显软化边缘。
+    # 粗层（低频）仍保持二值，否则会把整图混色，违反多频段融合的二值掩码前提。
+    fp = int(max(0, feather_px))
+    if fp > 0:
+        for li in range(1, FEATHER_LAYERS + 1):
+            idx = -li
+            if abs(idx) > len(ma):
+                break
+            scale = 2 ** (li - 1)
+            ksz = fp * 2 * scale + 1
+            ma[idx] = cv2.GaussianBlur(ma[idx], (ksz, ksz), sigmaX=fp * scale / 2.0)
+
     merged = []
     for a, b, mk in zip(la, lb, ma):
         m3 = mk[:, :, None]
@@ -620,33 +774,68 @@ def _multiband_blend(orig, swap_result, mask_bool, levels, edge_erode_px):
     # 这条带正是 mb_levels 要控制的东西。若像旧实现那样用原始硬二值遮罩钳回，
     # 过渡带会被整条抹掉（实测 levels 2↔6 边界差恒为 0），mb_levels 形同虚设。
     # 故按层数膨胀出一个外缘 keep 区：keep 内允许过渡，keep 外才强制还原原图。
-    margin = 2 ** n  # n=2→4px … n=6→64px，与粗层掩码的自然扩散宽度匹配
+    if transition_band_px is not None and transition_band_px >= 0:
+        margin = int(transition_band_px)  # 与金字塔层数解耦，用绝对像素
+    else:
+        margin = 2 ** n  # n=2→4px … n=6→64px，与粗层掩码的自然扩散宽度匹配
+    margin = max(0, margin)
     k = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * margin + 1, 2 * margin + 1))
     keep = cv2.dilate(mask_bool.astype(np.uint8), k).astype(bool)
     out[~keep] = orig[~keep]
     return out
 
 
+def _seamless_clone(orig, swap_result, mask_bool, edge_erode_px):
+    """泊松无缝克隆（cv2.seamlessClone NORMAL_CLONE）：梯度域调和整体色调。
+
+    返回调色后的整帧 uint8 BGR；掩码过小（<10px）时返回原图。
+    供 seamless 分支与 two_stage 两段式融合的第一段复用。
+    """
+    m = mask_bool.astype(np.uint8)
+    if edge_erode_px > 0:
+        k = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * edge_erode_px + 1,) * 2)
+        m = cv2.erode(m, k)
+    if m.sum() < 10:
+        return orig.copy()
+    ys, xs = np.where(m > 0)
+    center = (int((xs.min() + xs.max()) / 2), int((ys.min() + ys.max()) / 2))
+    return cv2.seamlessClone(swap_result, orig, m * 255, center, cv2.NORMAL_CLONE)
+
+
 def _composite(orig, swap_result, mask_bool, blend_method, feather_px, edge_erode_px,
-               color_match=False, mb_levels=5):
-    """把 swap_result 按遮罩贴回 orig，返回 (final_bgr, alpha_float or None)。"""
+               color_match=False, mb_levels=5, color_match_strength=1.0,
+               mb_feather_px=1, transition_band_px=-1):
+    """把 swap_result 按遮罩贴回 orig，返回 (final_bgr, alpha_float or None)。
+
+    blend_method:
+      - multiband : 多频段金字塔融合（默认）
+      - seamless  : 泊松无缝克隆（梯度域调色，自带色彩调和，故跳过 color_match）
+      - two_stage : 先 seamless 统一整体色调，再 multiband 贴发丝细节（大色差场景）
+      - feather/alpha_gradient : 单层 alpha 过渡
+    """
+    # seamless / two_stage 自带梯度域色彩调和，不叠 Reinhard 颜色迁移
     if blend_method == "seamless":
-        m = mask_bool.astype(np.uint8)
-        if edge_erode_px > 0:
-            k = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * edge_erode_px + 1,) * 2)
-            m = cv2.erode(m, k)
-        if m.sum() < 10:
-            return orig.copy(), None
-        ys, xs = np.where(m > 0)
-        center = (int((xs.min() + xs.max()) / 2), int((ys.min() + ys.max()) / 2))
-        final = cv2.seamlessClone(swap_result, orig, m * 255, center, cv2.NORMAL_CLONE)
+        final = _seamless_clone(orig, swap_result, mask_bool, edge_erode_px)
         return final, None
 
-    # 颜色校正前置（seamless 自带色彩调和，已在上面提前返回；其余分支在此生效）
-    src = _color_match_to_orig(swap_result, orig, mask_bool) if color_match else swap_result
+    if blend_method == "two_stage":
+        # 第一段：seamless 把整体色调拉平（生成图色调对齐到原图）
+        harmonized = _seamless_clone(orig, swap_result, mask_bool, edge_erode_px)
+        # 第二段：对调色后的结果再做 multiband 贴发丝细节（不加 color_match，避免重复改色）
+        final = _multiband_blend(orig, harmonized, mask_bool, mb_levels, edge_erode_px,
+                                 feather_px=mb_feather_px,
+                                 transition_band_px=transition_band_px)
+        alpha = (_multiband_alpha(mask_bool, edge_erode_px).astype(np.float32)) / 255.0
+        return final, alpha
+
+    # multiband / feather / alpha_gradient：先做 Reinhard 颜色迁移消除整体色差
+    src = (_color_match_to_orig(swap_result, orig, mask_bool, color_match_strength)
+           if color_match else swap_result)
 
     if blend_method == "multiband":
-        final = _multiband_blend(orig, src, mask_bool, mb_levels, edge_erode_px)
+        final = _multiband_blend(orig, src, mask_bool, mb_levels, edge_erode_px,
+                                 feather_px=mb_feather_px,
+                                 transition_band_px=transition_band_px)
         # 可视化用：用多频段的二值掩码做一层 alpha 标记（展示实际合成区）
         alpha = (_multiband_alpha(mask_bool, edge_erode_px).astype(np.float32)) / 255.0
         return final, alpha
@@ -661,22 +850,28 @@ def _composite(orig, swap_result, mask_bool, blend_method, feather_px, edge_erod
 # 主入口
 # ---------------------------------------------------------------------------
 
-def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segformer",
-                           mask_type="eroded", erode_cm=1.2, swap_mode="ext_mask",
-                           blend_method="feather", feather_px=15, edge_erode_px=3,
-                           denoising_strength=0.6, gen_backend="swaphair",
-                           hairgrow_strength=0.75, color_match=False, mb_levels=5,
-                           hairline_push_cm=0.0, hairline_edge="column", rid=None):
-    """接口11 完整管线。返回可直接进 ok() 的 data dict。未检出人脸抛 NoFaceError。
+def _grow_core(image_bgr, hairline_id, *, is_hr, seg_model, erode_cm, swap_mode,
+               edge_erode_px, denoising_strength, gen_backend, hairgrow_strength,
+               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):
+    """接口11 共享核心：遮罩(pushed)→生成→硬贴回→接缝融合，产出 ④ final。
 
-    rid: 调用方的 request id，用于日志关联。为 None 时自动生成。
+    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）：
+      final / swap_result / hard_paste / alpha / mask_bool / mask_viz /
+      px_per_cm / t_mask / t_swap / t_blend / h / w
+    未检出人脸抛 NoFaceError。
     """
-    if rid is None:
-        rid = uuid4().hex[:8]
-    logger.info("[%s] ===== generate_hairline_grow 开始 =====", rid)
-    logger.info("[%s] 参数: mask_type=%r erode_cm=%s blend=%s hairline_push_cm=%s hairline_edge=%r "
-                "seg=%s gen_backend=%s swap_mode=%s", rid, mask_type, erode_cm, blend_method,
-                hairline_push_cm, hairline_edge, seg_model, gen_backend, swap_mode)
+    mask_type = "pushed"        # 固定：只支持 pushed 遮罩算法
+    logger.info("[%s] _grow_core 参数(固定 mask=pushed): erode_cm=%s hairline_push_cm=%s "
+                "hairline_edge=%r mb_levels=%s seg=%s gen_backend=%s swap_mode=%s blend=%s "
+                "color_match=%s cm_strength=%s mb_feather_px=%s transition_band_px=%s "
+                "inpainting_fill=%s mask_blur=%s mask_dilate_scale=%s",
+                rid, erode_cm, hairline_push_cm, hairline_edge, mb_levels,
+                seg_model, gen_backend, swap_mode, blend_method, color_match,
+                color_match_strength, mb_feather_px, transition_band_px,
+                inpainting_fill, mask_blur, mask_dilate_scale)
     h, w = image_bgr.shape[:2]
     landmarks = detector.detect(image_bgr)
     if landmarks is None:
@@ -685,11 +880,12 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
     px_per_cm = estimate_scale_factor(landmarks, w, h)
     logger.info("[%s] 人脸检出 px_per_cm=%.3f 图尺寸=%dx%d", rid, px_per_cm, w, h)
 
-    # 步骤1：接口9 遮罩
+    # 步骤1：接口9 遮罩（固定 pushed）
     t0 = time.time()
     mask_bool, mask_viz = compute_mask(
         image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm,
-        hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge, rid=rid)
+        hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge, rid=rid,
+        render_viz=render_viz, hair_mask=hair_mask)
     t_mask = time.time() - t0
     logger.info("[%s] 步骤1 遮罩完成 耗时=%dms mask_pixels=%d", rid, int(t_mask*1000), int(mask_bool.sum()))
 
@@ -699,47 +895,105 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
         swap_result = _call_hairgrow(image_bgr, mask_bool, hairgrow_strength)
     else:
         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)
+        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)
     t_swap = time.time() - t0
 
     # 步骤3：严格按遮罩硬贴回（无融合，用于对比）
     hard_paste = image_bgr.copy()
     hard_paste[mask_bool] = swap_result[mask_bool]
 
-    # 步骤4：接缝融合
+    # 步骤4：接缝融合（默认 multiband）→ ④ final
     t0 = time.time()
     final, alpha = _composite(
-        image_bgr, swap_result, mask_bool, blend_method, feather_px, edge_erode_px,
-        color_match=color_match, mb_levels=mb_levels)
+        image_bgr, swap_result, mask_bool, blend_method, 0, edge_erode_px,
+        color_match=color_match, mb_levels=mb_levels,
+        color_match_strength=color_match_strength,
+        mb_feather_px=mb_feather_px, transition_band_px=transition_band_px)
     t_blend = time.time() - t0
 
+    return {
+        "final": final, "swap_result": swap_result, "hard_paste": hard_paste,
+        "alpha": alpha, "mask_bool": mask_bool, "mask_viz": mask_viz,
+        "px_per_cm": px_per_cm, "t_mask": t_mask, "t_swap": t_swap, "t_blend": t_blend,
+        "h": h, "w": w,
+    }
+
+
+# ---------------------------------------------------------------------------
+# 主入口
+# ---------------------------------------------------------------------------
+
+def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segformer",
+                           erode_cm=0.6, swap_mode="ext_mask",
+                           edge_erode_px=3,
+                           denoising_strength=0.6, gen_backend="swaphair",
+                           hairgrow_strength=0.75, mb_levels=5,
+                           hairline_push_cm=1.0, hairline_edge="column",
+                           blend_method="multiband", color_match=True,
+                           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):
+    """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。
+
+    遮罩算法固定为 pushed（发际线外推）。
+    融合算法 blend_method 默认 multiband（多频段金字塔），可选 seamless(泊松)/
+    two_stage(泊松→多频段两段式)/feather(羽化)/alpha_gradient(距离变换)。
+    color_match 默认开启 Reinhard 颜色迁移消除整体色差（对 multiband/feather 有效）。
+    inpainting_fill/mask_blur/mask_dilate_scale：透传 change_hair 服务端换发型重绘参数。
+    rid: 调用方的 request id，用于日志关联。为 None 时自动生成。
+    """
+    if rid is None:
+        rid = uuid4().hex[:8]
+    logger.info("[%s] ===== generate_hairline_grow 开始 =====", rid)
+    core = _grow_core(
+        image_bgr, hairline_id, is_hr=is_hr, seg_model=seg_model, erode_cm=erode_cm,
+        swap_mode=swap_mode, edge_erode_px=edge_erode_px, denoising_strength=denoising_strength,
+        gen_backend=gen_backend, hairgrow_strength=hairgrow_strength, mb_levels=mb_levels,
+        hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge, blend_method=blend_method,
+        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_viz = core["mask_viz"]
+    alpha = core["alpha"]
+    w, h = core["w"], core["h"]
+
     data = {
         "hairline_id": hairline_id,
         "gen_backend": gen_backend,
         "hairgrow_strength": round(float(hairgrow_strength), 3),
         "is_hr": is_hr,
         "seg_model": seg_model,
-        "mask_type": mask_type,
+        "mask_type": "pushed",
         "erode_cm": round(float(erode_cm), 2),
         "swap_mode": swap_mode,
         "blend_method": blend_method,
-        "feather_px": int(feather_px),
         "edge_erode_px": int(edge_erode_px),
-        "color_match": bool(color_match) and blend_method != "seamless",
         "mb_levels": int(mb_levels),
         "hairline_push_cm": round(float(hairline_push_cm), 2),
         "hairline_edge": hairline_edge,
         "denoising_strength": round(float(denoising_strength), 3),
-        "px_per_cm": round(float(px_per_cm), 4),
+        "color_match": bool(color_match),
+        "color_match_strength": round(float(color_match_strength), 3),
+        "mb_feather_px": int(mb_feather_px),
+        "transition_band_px": int(transition_band_px),
+        "inpainting_fill": int(inpainting_fill),
+        "mask_blur": int(mask_blur),
+        "mask_dilate_scale": round(float(mask_dilate_scale), 3),
+        "px_per_cm": round(float(core["px_per_cm"]), 4),
         "erode_px": mask_viz["erode_px"],
         "hair_pixels": mask_viz["hair_pixels"],
         "closed_pixels": mask_viz["closed_pixels"],
         "mask_pixels": mask_viz["mask_pixels"],
         "image_size": {"width": w, "height": h},
         "timings_ms": {
-            "mask": int(t_mask * 1000),
-            "swap": int(t_swap * 1000),
-            "blend": int(t_blend * 1000),
+            "mask": int(core["t_mask"] * 1000),
+            "swap": int(core["t_swap"] * 1000),
+            "blend": int(core["t_blend"] * 1000),
         },
         "steps": {
             "input_base64": _jpg_b64(image_bgr),
@@ -755,16 +1009,135 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
             # 最终遮罩
             "mask_overlay_base64": mask_viz["mask_overlay_base64"],
             "mask_base64": mask_viz["mask_base64"],
-            "swap_raw_base64": _jpg_b64(swap_result),
-            "hard_paste_base64": _jpg_b64(hard_paste),
+            "swap_raw_base64": _jpg_b64(core["swap_result"]),
+            "hard_paste_base64": _jpg_b64(core["hard_paste"]),
             "alpha_base64": _gray_b64(alpha) if alpha is not None else mask_viz["mask_base64"],
-            "final_base64": _jpg_b64(final),
+            "final_base64": _jpg_b64(core["final"]),
         },
-        "_rid": rid,  # 调试用：返回本次请求的日志关联 id
+        "_rid": rid,
     }
-    # 记录 steps 各图字段是否非空，供排查前端取图问题
     steps_summary = {k: (len(v) if isinstance(v, str) and v else 0)
                      for k, v in data["steps"].items() if k.endswith("_base64")}
     logger.info("[%s] 返回 steps 字段长度: %s", rid, steps_summary)
     logger.info("[%s] ===== generate_hairline_grow 完成 =====", rid)
     return data
+
+
+def generate_hairline_redraw(image_bgr, hairline_id, is_hr=False, seg_model="segformer",
+                             erode_cm=0.6, swap_mode="ext_mask",
+                             edge_erode_px=3,
+                             denoising_strength=0.6, gen_backend="swaphair",
+                             hairgrow_strength=0.75, mb_levels=5,
+                             hairline_push_cm=1.0, hairline_edge="column",
+                             blend_method="multiband", color_match=True,
+                             color_match_strength=1.0, mb_feather_px=1,
+                             transition_band_px=-1,
+                             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):
+    """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。
+
+    **本接口不再做 Flux-2 重绘**：只产出 `final`（接缝融合基底）+ 纯红遮罩
+    `redraw_band_mask`（RGBA，遮罩区=(255,0,0,255)、其余全透明），重绘交给后端
+    ComfyUI 重绘接口（/api/v1/redraw）完成。旧的 `redraw_full` / `redraw_band`
+    字段保留为空，仅作结构兼容。
+
+    返回可直接进 ok() 的 data dict。未检出人脸抛 NoFaceError。
+
+    comfyui_prompt：保留入参，但本接口不再使用（重绘提示词由外部服务自行决定）。
+    beauty_alpha：保留入参，但本接口不再使用（美颜由外部服务控制）。
+    band_lo_mult / band_hi_mult：重绘带外推倍率（相对 hairline_push_cm），带位于
+      lo×push ~ hi×push 之间（内轮廓=0×、原外推线=1.0×），默认 0.5 / 1.5。
+    其余参数含义与接口11 相同（用于内部生成 final 与重绘带）。
+    """
+    if rid is None:
+        rid = uuid4().hex[:8]
+    logger.info("[%s] ===== generate_hairline_redraw 开始 =====", rid)
+    core = _grow_core(
+        image_bgr, hairline_id, is_hr=is_hr, seg_model=seg_model, erode_cm=erode_cm,
+        swap_mode=swap_mode, edge_erode_px=edge_erode_px, denoising_strength=denoising_strength,
+        gen_backend=gen_backend, hairgrow_strength=hairgrow_strength, mb_levels=mb_levels,
+        hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge, blend_method=blend_method,
+        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, render_viz=False,
+        hair_mask=hair_mask)
+    final = core["final"]
+    mask_viz = core["mask_viz"]
+    w, h = core["w"], core["h"]
+    px_per_cm = core["px_per_cm"]
+
+    # ① 算重绘带（⑤-①）：发际线(内轮廓)↔外推发际线成带，经 baseline 截断只留上部
+    t0 = time.time()
+    inner_pts = mask_viz.get("_inner_pts")
+    outer_pts = mask_viz.get("_outer_pts")
+    upper_mask = mask_viz.get("_upper_mask")
+    push_px = int(round(max(0.0, hairline_push_cm) * px_per_cm))
+    redraw_band_overlay_b64 = ""
+    redraw_band_mask_b64 = ""   # 纯红 alpha PNG（遮罩区=(255,0,0,255)，其余全透明）
+    redraw_info = {"enabled": False}
+    band_mask = None
+    try:
+        band_mask = _redraw_band_mask(inner_pts, outer_pts, h, w, rid=rid, upper=upper_mask,
+                                      lo_mult=band_lo_mult, hi_mult=band_hi_mult)
+        if band_mask.sum() < 30:
+            raise RuntimeError("重绘带像素过少，可能内轮廓/外推线缺失")
+        logger.info("[%s] 重绘带 push_px=%d lo_mult=%s hi_mult=%s band_pixels=%d",
+                    rid, push_px, band_lo_mult, band_hi_mult, int(band_mask.sum()))
+        redraw_band_overlay_b64 = _jpg_b64(_overlay(final, band_mask, (255, 0, 255)))
+        # 纯红遮罩 PNG（供外部重绘服务按 alpha 识别重绘区）
+        redraw_band_mask_b64 = _red_mask_b64(band_mask, h, w)
+        redraw_info = {"enabled": True, "band_pixels": int(band_mask.sum()), "push_px": push_px,
+                       "band_lo_mult": float(band_lo_mult), "band_hi_mult": float(band_hi_mult)}
+    except Exception as ex:  # noqa: BLE001
+        logger.exception("[%s] 重绘带计算失败，整个重绘跳过", rid)
+        redraw_info = {"enabled": False, "error": f"band: {ex}"}
+
+    # ② Flux-2 重绘已下线：本接口现在只产出 final（接缝融合基底）+ 纯红重绘带遮罩，
+    #    重绘交给后端 ComfyUI 重绘接口（/api/v1/redraw）完成。
+    #    下面保留 redraw_full_b64 / redraw_band_b64 为空，保持返回结构兼容（旧字段）。
+    redraw_full_b64 = ""
+    redraw_band_b64 = ""
+    t_redraw = time.time() - t0
+
+    data = {
+        "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",
+        "beauty_alpha": beauty_alpha,
+        "px_per_cm": round(float(px_per_cm), 4),
+        "mask_pixels": mask_viz["mask_pixels"],
+        "image_size": {"width": w, "height": h},
+        "timings_ms": {
+            "mask": int(core["t_mask"] * 1000),
+            "swap": int(core["t_swap"] * 1000),
+            "blend": int(core["t_blend"] * 1000),
+            "redraw": int(t_redraw * 1000),
+        },
+        "steps": {
+            "input_base64": _jpg_b64(image_bgr),
+            # 接口11 的 ④ final —— 作为本接口的重绘输入基底
+            "final_base64": _jpg_b64(final),
+            # ⑤-① 发际线重绘带（紫，已按 baseline 截断只留上部）
+            "redraw_band_overlay_base64": redraw_band_overlay_b64,
+            # ⑤-② 发际线重绘带遮罩（纯红 alpha PNG，遮罩区=(255,0,0,255)）
+            "redraw_band_mask_base64": redraw_band_mask_b64,
+            # A：ComfyUI 整帧重绘+美颜（已下线，保留空字段兼容旧前端）
+            "redraw_full_base64": redraw_full_b64,
+            # B：加发只在发际线带、美颜保留全脸（已下线，保留空字段兼容旧前端）
+            "redraw_band_base64": redraw_band_b64,
+            # 兼容旧字段：指向 A（整帧版）
+            "redraw_c_base64": redraw_full_b64,
+        },
+        "redraw": redraw_info,
+        "_rid": rid,
+    }
+    steps_summary = {k: (len(v) if isinstance(v, str) and v else 0)
+                     for k, v in data["steps"].items() if k.endswith("_base64")}
+    logger.info("[%s] 返回 steps 字段长度: %s", rid, steps_summary)
+    logger.info("[%s] ===== generate_hairline_redraw 完成 =====", rid)
+    return data

face_analysis/head_mask.py

@@ -2,8 +2,8 @@
 
 流程（详见需求讨论）：
 1. MediaPipe 关键点检测。
-2. 底部分割线 = 关键点 [21,68,104,69,108,151,337,299,333,298,251] 的连线（左端21→中心151→右端251），
-   再把左端点 21 水平延伸到图片最左边、右端点 251 水平延伸到图片最右边。
+2. 底部分割线 = 关键点 [162,71,68,104,69,108,151,337,299,333,298,301,389] 的连线（左端162→中心151→右端389），
+   再把左端点 162 水平延伸到图片最左边、右端点 389 水平延伸到图片最右边。
 3. 上半区 = 分割线以上区域（多边形填充：左边缘→弧线→右边缘→上边缘闭合）。
 4. 头发分割：BiSeNet 与 SegFormer 各出一张 hair_mask（两套供对比）。
 5. 闭合区域（含额头）：每列从最顶端头发像素向下填充到分割线，把头发与画线之间的额头皮肤
@@ -22,8 +22,11 @@ import numpy as np
 from face_analysis.detector import detector
 from face_analysis.calibration import estimate_scale_factor, normalized_to_pixel
 
-# 底部额头弧线关键点（图像上从左到右：左端 21 → 中心 151 → 右端 251）
-BASELINE_IDX = [21, 68, 104, 69, 108, 151, 337, 299, 333, 298, 251]
+# 底部分割线关键点（图像上从左到右，眉骨弧线 → 中心 151 → 右侧对称）
+# 左端 104 → 中心 151 → 右端 333；首末点向图片左右边缘水平延长
+# BASELINE_IDX = [104, 69, 108, 151, 337, 299, 333]
+# BASELINE_IDX = [34, 139, 71, 68, 104, 69, 108, 151, 337, 299, 333, 298, 301, 368, 264]
+BASELINE_IDX = [71, 68, 104, 69, 108, 151, 337, 299, 333, 298, 301]
 CENTER_IDX = 151          # 内缩方向的目标点（额头中心）
 ERODE_CM = 1.2            # 外缘内缩距离（厘米，默认；可由入参覆盖）
 SEGFORMER_HAIR = 13       # jonathandinu/face-parsing 中 hair 类索引
@@ -50,12 +53,12 @@ def _baseline_points(landmarks, w, h):
 def _upper_region_mask(baseline_pts, w, h):
     """分割线以上区域（bool，H×W）。
 
-    多边形顶点：左上角 →（0, y54）→ 弧线各点 →（w-1, y284）→ 右上角，闭合后填充。
-    其中 54→左边缘、284→右边缘为两段水平延长线。
+    多边形顶点：左上角 →（0, y左端）→ 弧线各点 →（w-1, y右端）→ 右上角，闭合后填充。
+    左端/右端为两段水平延长线（向图片左右边缘延伸）。
     """
-    x54, y54 = baseline_pts[0]
-    x284, y284 = baseline_pts[-1]
-    poly = [(0, 0), (0, y54)] + baseline_pts + [(w - 1, y284), (w - 1, 0)]
+    x0, y0 = baseline_pts[0]
+    x1, y1 = baseline_pts[-1]
+    poly = [(0, 0), (0, y0)] + baseline_pts + [(w - 1, y1), (w - 1, 0)]
     mask = np.zeros((h, w), np.uint8)
     cv2.fillPoly(mask, [np.array(poly, np.int32)], 1)
     return mask.astype(bool)
@@ -108,9 +111,9 @@ def _overlay(image, mask_bool, color, alpha=0.45):
 def _draw_baseline(image, baseline_pts, w):
     """画分割线（含左右水平延长线）+ 关键点，中心点 151 标红。"""
     out = image.copy()
-    y54 = baseline_pts[0][1]
-    y284 = baseline_pts[-1][1]
-    chain = [(0, y54)] + baseline_pts + [(w - 1, y284)]
+    y0 = baseline_pts[0][1]
+    y1 = baseline_pts[-1][1]
+    chain = [(0, y0)] + baseline_pts + [(w - 1, y1)]
     for a, b in zip(chain[:-1], chain[1:]):
         cv2.line(out, a, b, (0, 255, 255), 2, cv2.LINE_AA)
     for idx, p in zip(BASELINE_IDX, baseline_pts):

face_analysis/measure.py

@@ -12,9 +12,9 @@ from face_analysis.calibration import (
     estimate_scale_factor, normalized_to_pixel, pixel_distance, _lm_list,
 )
 from face_analysis.face_mesh_landmarks import (
-    GLABELLA_9, GLABELLA_151, NOSE_BOTTOM, CHIN_TIP,
+    GLABELLA_9, NOSE_BOTTOM, CHIN_TIP,
     LEFT_EYE_OUTER, LEFT_EYE_INNER, RIGHT_EYE_INNER, RIGHT_EYE_OUTER,
-    LEFT_CHEEK, RIGHT_CHEEK,
+    LEFT_CHEEK, RIGHT_CHEEK, LEFT_POSITION, RIGHT_POSITION,
 )
 from face_analysis.hair_segmenter import locate_hairline_by_segmentation
 
@@ -24,10 +24,8 @@ _TOP_RATIO = 0.22 / 0.28      # 顶庭 ÷ 中庭（≈ 0.786）
 
 
 def _brow_center(lm, 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
+    """眉心 = 索引 9（眉间上点）。"""
+    return normalized_to_pixel(lm[GLABELLA_9], w, h)
 
 
 def estimate_vertical_landmarks(landmarks, image_width, image_height):
@@ -144,22 +142,47 @@ def measure_seven_eyes(landmarks, image_width, image_height):
     }
 
 
+def pt_or_none(vertical, name):
+    """vertical dict 的点 → {"x","y"}，值为 None 时返回 None。"""
+    v = vertical.get(name)
+    if v is None:
+        return None
+    return {"x": int(round(v[0])), "y": int(round(v[1]))}
+
+
 class MeasureResult:
     """测量结果，提供 to_response() 输出与接口文档同构的 data 字段。"""
 
-    def __init__(self, vertical, eyes, px_per_cm, hairline_source, head_pose):
+    # 发际线弃用阈值：发际线离头顶（顶庭）< 此值时判定分割不可靠，弃用发际线。
+    # hairline 与 hair_top 几乎重合（如稀疏头发中轴漏检只剩一小撮），说明发际线
+    # 定位无意义 → 顶/上庭置 null、标注图不画头顶/发际线。
+    HAIRLINE_DISCARD_TOP_CM = 0.7
+
+    def __init__(self, vertical, eyes, px_per_cm, hairline_source, head_pose,
+                 landmarks=None, image_width=None, image_height=None):
         self.vertical = vertical
         self.eyes = eyes
         self.px_per_cm = px_per_cm
         self.hairline_source = hairline_source
         self.head_pose = head_pose  # (yaw, pitch, roll) 或 None
+        # 原始 mediapipe 点集 + 图像尺寸，供 to_response 输出 21/251 号定位点
+        self.landmarks = landmarks
+        self.w = image_width
+        self.h = image_height
 
         # 各庭厘米
         self.top_cm = vertical["top_court_px"] / px_per_cm
         self.upper_cm = vertical["upper_court_px"] / px_per_cm
         self.middle_cm = vertical["middle_court_px"] / px_per_cm
         self.lower_cm = vertical["lower_court_px"] / px_per_cm
-        self.face_total_cm = self.top_cm + self.upper_cm + self.middle_cm + self.lower_cm
+        # 发际线弃用判定：顶庭（头顶→发际线）过小视为发际线贴近头顶、不可靠。
+        # 弃用时 hairline_source 改为 "discarded"，face_total 只算中庭+下庭。
+        self.hairline_discarded = self.top_cm < self.HAIRLINE_DISCARD_TOP_CM
+        if self.hairline_discarded:
+            self.hairline_source = "discarded"
+            self.face_total_cm = self.middle_cm + self.lower_cm
+        else:
+            self.face_total_cm = self.top_cm + self.upper_cm + self.middle_cm + self.lower_cm
 
         # 七眼厘米
         self.eye_width_cm = eyes["eye_width_px"] / px_per_cm
@@ -167,46 +190,88 @@ class MeasureResult:
         self.inter_eye_cm = eyes["inter_eye_distance_px"] / px_per_cm
 
     def to_response(self):
-        total_px = (self.vertical["top_court_px"] + self.vertical["upper_court_px"]
-                    + self.vertical["middle_court_px"] + self.vertical["lower_court_px"])
-        fw_px = self.eyes["face_width_px"]
-
-        def pt(name):
-            x, y = self.vertical[name]
-            return {"x": int(round(x)), "y": int(round(y))}
-
-        data = {
-            "face_total_height_cm": round(self.face_total_cm, 2),
-            "four_courts": {
-                "top_court_cm": round(self.top_cm, 2),
-                "upper_court_cm": round(self.upper_cm, 2),
-                "middle_court_cm": round(self.middle_cm, 2),
-                "lower_court_cm": round(self.lower_cm, 2),
-                "ratios": {
-                    "top_court": round(self.vertical["top_court_px"] / total_px, 3),
-                    "upper_court": round(self.vertical["upper_court_px"] / total_px, 3),
-                    "middle_court": round(self.vertical["middle_court_px"] / total_px, 3),
-                    "lower_court": round(self.vertical["lower_court_px"] / total_px, 3),
+        # 发际线弃用：顶/上庭相关字段置 null（保留键），ratio 分母只算中下庭；
+        # landmarks.hair_top/hairline 置 null。否则按四庭正常输出。
+        if self.hairline_discarded:
+            base_px = (self.vertical["middle_court_px"] + self.vertical["lower_court_px"])
+            data = {
+                "face_total_height_cm": round(self.face_total_cm, 2),
+                "four_courts": {
+                    "top_court_cm": None,
+                    "upper_court_cm": None,
+                    "middle_court_cm": round(self.middle_cm, 2),
+                    "lower_court_cm": round(self.lower_cm, 2),
+                    "ratios": {
+                        "top_court": None,
+                        "upper_court": None,
+                        "middle_court": round(self.vertical["middle_court_px"] / base_px, 3),
+                        "lower_court": round(self.vertical["lower_court_px"] / base_px, 3),
+                    },
                 },
-            },
-            "seven_eyes": {
-                "eye_width_cm": round(self.eye_width_cm, 2),
-                "face_width_cm": round(self.face_width_cm, 2),
-                "inter_eye_distance_cm": round(self.inter_eye_cm, 2),
-                "ratios": {
-                    "eye_width": round(self.eyes["eye_width_px"] / fw_px, 3),
-                    "inter_eye_distance": round(self.eyes["inter_eye_distance_px"] / fw_px, 3),
+                "seven_eyes": {
+                    "eye_width_cm": round(self.eye_width_cm, 2),
+                    "face_width_cm": round(self.face_width_cm, 2),
+                    "inter_eye_distance_cm": round(self.inter_eye_cm, 2),
+                    "ratios": {
+                        "eye_width": round(self.eyes["eye_width_px"] / self.eyes["face_width_px"], 3),
+                        "inter_eye_distance": round(self.eyes["inter_eye_distance_px"] / self.eyes["face_width_px"], 3),
+                    },
                 },
-            },
-            "landmarks": {
-                "hair_top": pt("hair_top"),
-                "hairline": pt("hairline"),
-                "brow_center": pt("brow_center"),
-                "nose_bottom": pt("nose_bottom"),
-                "chin_tip": pt("chin_tip"),
-            },
-            "hairline_source": self.hairline_source,
-        }
+                "landmarks": {
+                    "hair_top": None,
+                    "hairline": None,
+                    "brow_center": pt_or_none(self.vertical, "brow_center"),
+                    "nose_bottom": pt_or_none(self.vertical, "nose_bottom"),
+                    "chin_tip": pt_or_none(self.vertical, "chin_tip"),
+                },
+                "hairline_source": self.hairline_source,
+            }
+        else:
+            total_px = (self.vertical["top_court_px"] + self.vertical["upper_court_px"]
+                        + self.vertical["middle_court_px"] + self.vertical["lower_court_px"])
+            data = {
+                "face_total_height_cm": round(self.face_total_cm, 2),
+                "four_courts": {
+                    "top_court_cm": round(self.top_cm, 2),
+                    "upper_court_cm": round(self.upper_cm, 2),
+                    "middle_court_cm": round(self.middle_cm, 2),
+                    "lower_court_cm": round(self.lower_cm, 2),
+                    "ratios": {
+                        "top_court": round(self.vertical["top_court_px"] / total_px, 3),
+                        "upper_court": round(self.vertical["upper_court_px"] / total_px, 3),
+                        "middle_court": round(self.vertical["middle_court_px"] / total_px, 3),
+                        "lower_court": round(self.vertical["lower_court_px"] / total_px, 3),
+                    },
+                },
+                "seven_eyes": {
+                    "eye_width_cm": round(self.eye_width_cm, 2),
+                    "face_width_cm": round(self.face_width_cm, 2),
+                    "inter_eye_distance_cm": round(self.inter_eye_cm, 2),
+                    "ratios": {
+                        "eye_width": round(self.eyes["eye_width_px"] / self.eyes["face_width_px"], 3),
+                        "inter_eye_distance": round(self.eyes["inter_eye_distance_px"] / self.eyes["face_width_px"], 3),
+                    },
+                },
+                "landmarks": {
+                    "hair_top": pt_or_none(self.vertical, "hair_top"),
+                    "hairline": pt_or_none(self.vertical, "hairline"),
+                    "brow_center": pt_or_none(self.vertical, "brow_center"),
+                    "nose_bottom": pt_or_none(self.vertical, "nose_bottom"),
+                    "chin_tip": pt_or_none(self.vertical, "chin_tip"),
+                },
+                "hairline_source": self.hairline_source,
+            }
+        # left/right_position：mediapipe 21/251 号定位点（原图像素，与 landmarks 同坐标系）。
+        # landmarks 缺省（如测试直构 MeasureResult）时不输出，保持向后兼容。
+        if self.landmarks is not None and self.w and self.h:
+            lm = _lm_list(self.landmarks)
+
+            def _pt_lm(idx):
+                px, py = normalized_to_pixel(lm[idx], self.w, self.h)
+                return {"x": int(round(px)), "y": int(round(py))}
+
+            data["left_position"] = _pt_lm(LEFT_POSITION)
+            data["right_position"] = _pt_lm(RIGHT_POSITION)
         if self.head_pose is not None:
             yaw, pitch, roll = self.head_pose
             data["head_pose"] = {
@@ -220,7 +285,8 @@ def measure_face(landmarks, hair_mask, image_width, image_height, head_pose=None
     vertical, source = decide_vertical(landmarks, image_width, image_height, hair_mask)
     eyes = measure_seven_eyes(landmarks, image_width, image_height)
     px_per_cm = estimate_scale_factor(landmarks, image_width, image_height)
-    return MeasureResult(vertical, eyes, px_per_cm, source, head_pose)
+    return MeasureResult(vertical, eyes, px_per_cm, source, head_pose,
+                         landmarks, image_width, image_height)
 
 
 if __name__ == "__main__":

face_analysis/pose.py

@@ -50,12 +50,21 @@ def estimate_head_pose(landmarks, image_width, image_height):
                            [0, 0, 1]], dtype=np.float64)
     dist = np.zeros((4, 1))  # 假设无畸变
 
-    success, rvec, _tvec = cv2.solvePnP(
+    success, rvec, tvec = cv2.solvePnP(
         _MODEL_POINTS, image_points, cam_matrix, dist,
         flags=cv2.SOLVEPNP_ITERATIVE,
     )
     if not success:
         return None
+    # ITERATIVE 偶发收敛到相机后方的翻转解（tz<0），此时 roll 落在 ±180° 附近，
+    # 会把真正的正面照误判为 1003。改用 SQPNP 重解正深度解。
+    if float(tvec[2, 0]) < 0:
+        ok2, rvec2, tvec2 = cv2.solvePnP(
+            _MODEL_POINTS, image_points, cam_matrix, dist,
+            flags=cv2.SOLVEPNP_SQPNP,
+        )
+        if ok2 and float(tvec2[2, 0]) > 0:
+            rvec = rvec2
     rot, _ = cv2.Rodrigues(rvec)
     # 在「相机坐标系」(x右 y下 z内) 下抽取 Tait-Bryan 欧拉角，物理含义对齐：
     #   yaw   = 绕 Y（竖轴）转 → 左右扭头

face_features.py

@@ -37,7 +37,8 @@ _PROMPT = (
     "基因风格（戏剧型/睿智型/自然型/古典型/优雅型/浪漫型/前卫型/少女型/少年型）"
 )
 
-_client = None
+_client = None                      # 缓存的 Ark client（api_key 变更时自动重建）
+_client_key: str | None = None      # _client 构建时使用的 api_key，用于检测配置变更
 
 
 def _load_api_key() -> str | None:
@@ -60,13 +61,21 @@ def _load_api_key() -> str | None:
 
 
 def get_client():
-    global _client
-    if _client is None:
+    """返回 Ark client。
+
+    client 全局缓存，但每次都会重新读取 api_key —— 一旦配置（环境变量 /
+    worker_config.json / gateway/config.json 的 ark_api_key）发生变化，
+    自动重建 client。这样换 key 后无需重启进程。
+    """
+    global _client, _client_key
+    key = _load_api_key()
+    if not key:
+        raise RuntimeError("缺少火山方舟 API Key（设 ARK_API_KEY 或 worker_config.json.ark_api_key）")
+    # client 未建、或 key 变了 → 重建
+    if _client is None or key != _client_key:
         from volcenginesdkarkruntime import Ark
-        key = _load_api_key()
-        if not key:
-            raise RuntimeError("缺少火山方舟 API Key（设 ARK_API_KEY 或 worker_config.json.ark_api_key）")
         _client = Ark(base_url=ARK_BASE_URL, api_key=key)
+        _client_key = key
     return _client
 
 

hair-worker.service

@@ -1,16 +1,15 @@
 [Unit]
-Description=Hair Worker (GPU) - 四庭七眼测量 接口1
-After=network.target
+Description=hair GPU worker FastAPI (0.0.0.0:8187)
+After=network-online.target comfyui.service change_hair-hair.service
+Wants=comfyui.service change_hair-hair.service
 
 [Service]
 Type=simple
-User=xsl
-WorkingDirectory=/home/xsl/hair
-# 鉴权密码：优先 worker_config.json；也可在此用环境变量覆盖
-# Environment=WORKER_ACCEPT_PASSWORDS=your-strong-secret
-ExecStart=/home/xsl/hair/venv/bin/uvicorn app:app --host 0.0.0.0 --port 8187
-Restart=always
-RestartSec=3
+User=ubuntu
+WorkingDirectory=/home/ubuntu/hair
+ExecStart=/home/ubuntu/hair/venv/bin/uvicorn app:app --host 0.0.0.0 --port 8187
+Restart=on-failure
+RestartSec=5
 
 [Install]
 WantedBy=multi-user.target

hair_repaint.json

@@ -0,0 +1,450 @@
+{
+  "1": {
+    "inputs": {
+      "scheduler": "simple",
+      "steps": 6,
+      "denoise": 1,
+      "model": [
+        "2",
+        0
+      ]
+    },
+    "class_type": "BasicScheduler",
+    "_meta": {
+      "title": "基本调度器"
+    }
+  },
+  "2": {
+    "inputs": {
+      "max_shift": 1.15,
+      "base_shift": 0.5,
+      "width": [
+        "14",
+        0
+      ],
+      "height": [
+        "14",
+        1
+      ],
+      "model": [
+        "16",
+        0
+      ]
+    },
+    "class_type": "ModelSamplingFlux",
+    "_meta": {
+      "title": "采样算法（Flux）"
+    }
+  },
+  "3": {
+    "inputs": {
+      "vae_name": "flux2-vae.safetensors"
+    },
+    "class_type": "VAELoader",
+    "_meta": {
+      "title": "加载VAE"
+    }
+  },
+  "5": {
+    "inputs": {
+      "conditioning": [
+        "19",
+        0
+      ],
+      "latent": [
+        "13",
+        0
+      ]
+    },
+    "class_type": "ReferenceLatent",
+    "_meta": {
+      "title": "参考Latent"
+    }
+  },
+  "6": {
+    "inputs": {
+      "noise_seed": 808990860769642
+    },
+    "class_type": "RandomNoise",
+    "_meta": {
+      "title": "随机噪波"
+    }
+  },
+  "7": {
+    "inputs": {
+      "width": [
+        "14",
+        0
+      ],
+      "height": [
+        "14",
+        1
+      ],
+      "batch_size": 1
+    },
+    "class_type": "EmptySD3LatentImage",
+    "_meta": {
+      "title": "空Latent图像（SD3）"
+    }
+  },
+  "8": {
+    "inputs": {
+      "sampler_name": "euler"
+    },
+    "class_type": "KSamplerSelect",
+    "_meta": {
+      "title": "K采样器选择"
+    }
+  },
+  "9": {
+    "inputs": {
+      "noise": [
+        "6",
+        0
+      ],
+      "guider": [
+        "20",
+        0
+      ],
+      "sampler": [
+        "8",
+        0
+      ],
+      "sigmas": [
+        "1",
+        0
+      ],
+      "latent_image": [
+        "7",
+        0
+      ]
+    },
+    "class_type": "SamplerCustomAdvanced",
+    "_meta": {
+      "title": "自定义采样器（高级）"
+    }
+  },
+  "10": {
+    "inputs": {
+      "samples": [
+        "9",
+        0
+      ],
+      "vae": [
+        "3",
+        0
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE解码"
+    }
+  },
+  "13": {
+    "inputs": {
+      "pixels": [
+        "44",
+        0
+      ],
+      "vae": [
+        "3",
+        0
+      ]
+    },
+    "class_type": "VAEEncode",
+    "_meta": {
+      "title": "VAE编码"
+    }
+  },
+  "14": {
+    "inputs": {
+      "image": [
+        "44",
+        0
+      ]
+    },
+    "class_type": "GetImageSize+",
+    "_meta": {
+      "title": "🔧 Get Image Size"
+    }
+  },
+  "16": {
+    "inputs": {
+      "unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
+      "weight_dtype": "fp8_e4m3fn"
+    },
+    "class_type": "UnetLoaderGGUF",
+    "_meta": {
+      "title": "UNet加载器"
+    }
+  },
+  "17": {
+    "inputs": {
+      "filename_prefix": "ComfyUI",
+      "images": [
+        "62",
+        0
+      ]
+    },
+    "class_type": "SaveImage",
+    "_meta": {
+      "title": "保存图像"
+    }
+  },
+  "19": {
+    "inputs": {
+      "guidance": 1,
+      "conditioning": [
+        "22",
+        0
+      ]
+    },
+    "class_type": "FluxGuidance",
+    "_meta": {
+      "title": "Flux引导"
+    }
+  },
+  "20": {
+    "inputs": {
+      "model": [
+        "2",
+        0
+      ],
+      "conditioning": [
+        "5",
+        0
+      ]
+    },
+    "class_type": "BasicGuider",
+    "_meta": {
+      "title": "基本引导器"
+    }
+  },
+  "22": {
+    "inputs": {
+      "text": [
+        "60",
+        0
+      ],
+      "clip": [
+        "61",
+        0
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP文本编码"
+    }
+  },
+  "26": {
+    "inputs": {
+      "image": "clipspace/clipspace-painted-masked-1784045785080.png [input]"
+    },
+    "class_type": "LoadImage",
+    "_meta": {
+      "title": "加载图像"
+    }
+  },
+  "31": {
+    "inputs": {
+      "image": [
+        "26",
+        0
+      ]
+    },
+    "class_type": "easy imageSize",
+    "_meta": {
+      "title": "图像尺寸"
+    }
+  },
+  "32": {
+    "inputs": {
+      "aspect_ratio": "custom",
+      "proportional_width": [
+        "31",
+        0
+      ],
+      "proportional_height": [
+        "31",
+        1
+      ],
+      "fit": "letterbox",
+      "method": "lanczos",
+      "round_to_multiple": "8",
+      "scale_to_side": "None",
+      "scale_to_length": 1024,
+      "background_color": "#000000",
+      "image": [
+        "26",
+        0
+      ],
+      "mask": [
+        "37",
+        0
+      ]
+    },
+    "class_type": "LayerUtility: ImageScaleByAspectRatio V2",
+    "_meta": {
+      "title": "LayerUtility: ImageScaleByAspectRatio V2"
+    }
+  },
+  "33": {
+    "inputs": {
+      "masks": [
+        "26",
+        1
+      ]
+    },
+    "class_type": "Mask Fill Holes",
+    "_meta": {
+      "title": "遮罩填充漏洞"
+    }
+  },
+  "36": {
+    "inputs": {
+      "masks": [
+        "33",
+        0
+      ]
+    },
+    "class_type": "Convert Masks to Images",
+    "_meta": {
+      "title": "遮罩到图像"
+    }
+  },
+  "37": {
+    "inputs": {
+      "method": "intensity",
+      "image": [
+        "39",
+        0
+      ]
+    },
+    "class_type": "Image To Mask",
+    "_meta": {
+      "title": "图像到遮罩"
+    }
+  },
+  "39": {
+    "inputs": {
+      "upscale_method": "nearest-exact",
+      "width": [
+        "31",
+        0
+      ],
+      "height": [
+        "31",
+        1
+      ],
+      "crop": "disabled",
+      "image": [
+        "36",
+        0
+      ]
+    },
+    "class_type": "ImageScale",
+    "_meta": {
+      "title": "缩放图像"
+    }
+  },
+  "44": {
+    "inputs": {
+      "mask_opacity": 1,
+      "mask_color": "FFFF00",
+      "pass_through": true,
+      "image": [
+        "32",
+        0
+      ],
+      "mask": [
+        "32",
+        1
+      ]
+    },
+    "class_type": "ImageAndMaskPreview",
+    "_meta": {
+      "title": "图像与遮罩预览"
+    }
+  },
+  "45": {
+    "inputs": {
+      "images": [
+        "44",
+        0
+      ]
+    },
+    "class_type": "PreviewImage",
+    "_meta": {
+      "title": "预览图像"
+    }
+  },
+  "53": {
+    "inputs": {
+      "rgthree_comparer": {
+        "images": [
+          {
+            "name": "A",
+            "selected": true,
+            "url": "/api/view?filename=rgthree.compare._temp_kzrpg_00019_.png&type=temp&subfolder=&rand=0.8964945384546902"
+          },
+          {
+            "name": "B",
+            "selected": true,
+            "url": "/api/view?filename=rgthree.compare._temp_kzrpg_00020_.png&type=temp&subfolder=&rand=0.6762414189274947"
+          }
+        ]
+      },
+      "image_a": [
+        "62",
+        0
+      ],
+      "image_b": [
+        "26",
+        0
+      ]
+    },
+    "class_type": "Image Comparer (rgthree)",
+    "_meta": {
+      "title": "图像对比"
+    }
+  },
+  "60": {
+    "inputs": {
+      "text": "填充遮罩区域的头发"
+    },
+    "class_type": "JjkText",
+    "_meta": {
+      "title": "Text"
+    }
+  },
+  "61": {
+    "inputs": {
+      "clip_name": "qwen_3_8b_fp8mixed.safetensors",
+      "type": "flux2",
+      "device": "default"
+    },
+    "class_type": "CLIPLoader",
+    "_meta": {
+      "title": "加载CLIP"
+    }
+  },
+  "62": {
+    "inputs": {
+      "method": "mkl",
+      "strength": 1,
+      "multithread": true,
+      "image_ref": [
+        "26",
+        0
+      ],
+      "image_target": [
+        "10",
+        0
+      ]
+    },
+    "class_type": "ColorMatch",
+    "_meta": {
+      "title": "Color Match"
+    }
+  }
+}

hairline/comfyui.py

@@ -1,8 +1,9 @@
 """ComfyUI 客户端：用 add_hair.json / add_hair2.json 工作流跑生发图（Flux-2 inpaint）。
 
-worker 不跑 Flux，只把「划线图 + 遮罩」的 RGBA 上传到本机 ComfyUI(默认 8188)，
+worker 不跑 Flux，只把「划线图 + 遮罩」的 RGBA 上传到远端 ComfyUI
+（默认 http://10.60.74.221:8188，可用环境变量 COMFYUI_URL 覆盖），
 替换工作流节点 26 的输入图、随机 seed，提交 /prompt，轮询 /history，取回 /view 输出。
-ComfyUI 开启了 HTTP Basic Auth（user `admin` + 密码），所有请求都带凭据。
+ComfyUI 若开启了 HTTP Basic Auth（user `admin` + 密码），所有请求都带凭据。
 
 支持多工作流：run() 可通过 workflow_path 指定不同工作流 JSON，自动检测 SaveImage 输出节点。
 """
@@ -10,6 +11,7 @@ from __future__ import annotations
 
 import copy
 import json
+import logging
 import os
 import random
 import time
@@ -28,6 +30,19 @@ _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
+
+# Flux 模型 → 配套文本编码器映射。切换 unet 时自动同步编码器，避免维度不匹配。
+# 规则：4b 系列配 qwen_3_4b，9b 系列（fp8/GGUF）配 qwen_3_8b_fp8mixed。
+def _clip_for_unet(unet_name: str) -> str | None:
+    """根据 unet 文件名推断配套的文本编码器文件名；无法推断返回 None。"""
+    low = unet_name.lower()
+    if "4b" in low and "9b" not in low:
+        return "qwen_3_4b.safetensors"
+    if "9b" in low:
+        return "qwen_3_8b_fp8mixed.safetensors"
+    return None
 
 _wf_cache: dict[str, dict] = {}      # path → workflow JSON
 _wf_output_node: dict[str, str] = {}  # path → SaveImage 节点 ID
@@ -84,11 +99,18 @@ 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) -> 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)
@@ -103,14 +125,55 @@ def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = Non
         name = (up.get("subfolder") + "/" if up.get("subfolder") else "") + up["name"]
 
         # 2. 改工作流：节点26 输入图 + 随机 seed
+        try:
+            import io as _io
+            from PIL import Image as _Img
+            _sz = _Img.open(_io.BytesIO(rgba_png_bytes)).size
+            logging.getLogger("hair.worker").info(
+                "ComfyUI 输入尺寸 %dx%d workflow=%s", _sz[0], _sz[1], os.path.basename(path))
+        except Exception:  # noqa: BLE001
+            pass
         wf = copy.deepcopy(_load_workflow(path))
         wf[_INPUT_NODE]["inputs"]["image"] = name
         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
 
-        # 3. 提交
-        r = cli.post("/prompt", json={"prompt": wf, "client_id": client_id})
+        # 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:
+            import os as _os
+            _diag = _os.path.join(_os.path.dirname(_os.path.dirname(_os.path.abspath(__file__))),
+                                  "log", "comfyui_last_submit")
+            _os.makedirs(_diag, exist_ok=True)
+            with open(_os.path.join(_diag, "workflow.json"), "w", encoding="utf-8") as _f:
+                json.dump(wf, _f, ensure_ascii=False, indent=2)
+            with open(_os.path.join(_diag, "input.png"), "wb") as _f:
+                _f.write(rgba_png_bytes)
+            with open(_os.path.join(_diag, "prompt.txt"), "w", encoding="utf-8") as _f:
+                _f.write(prompt if prompt is not None else "(None=用工作流内置默认)")
+        except Exception:  # noqa: BLE001
+            pass
+
+        # 3. 提交（front=True 时插队到队列最前）
+        payload = {"prompt": wf, "client_id": client_id}
+        if front:
+            payload["front"] = True
+        r = cli.post("/prompt", json=payload)
         r.raise_for_status()
         prompt_id = r.json()["prompt_id"]
 
@@ -129,7 +192,7 @@ def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = Non
                 outputs = entry.get("outputs")
                 if outputs and output_node in outputs:
                     break
-            time.sleep(1.0)
+            time.sleep(0.05)
         if not outputs or output_node not in outputs:
             raise TimeoutError(f"ComfyUI 出图超时({timeout}s) prompt_id={prompt_id}")
 

hairline/hairline_2d.py

@@ -186,6 +186,53 @@ def smooth_hairline_corner_aware(
     return out
 
 
+def clamp_hairline_to_silhouette(
+    hairline_norm: np.ndarray,
+    parse_map: np.ndarray,
+    margin_px: float = 2.0,
+) -> np.ndarray:
+    """把发际线点的 y 钳制在 (skin∪hair) silhouette 上沿之下（不含 margin 以上）。
+
+    根因（见 issue：男性 ellipse 发际线贴到头部外面）：`sample_hairline` 对射线
+    未命中 hair 像素的锚点会 fallback 成「锚点 + 固定 0.18 归一化偏移」，与头部实际
+    大小/位置无关 —— 短发/剃光头场景下这个偏移量常常把点顶到头部轮廓外面的背景里，
+    在有效/失效锚点交界处形成尖角，被贴图上的不透明像素蒙到就会露出戳出头部的线条。
+
+    本函数在几何检测之后追加一步「安全网」：对每个点按其 x 所在列，取 silhouette
+    （SegFormer skin∪hair 类，近似头部实际轮廓）上沿 y，若点比这个上沿还高（y 更
+    小），直接钳制到 上沿 + margin_px —— 保证曲线永远不会跑到头部轮廓外面的背景。
+    """
+    h, w = parse_map.shape
+    cols_with_head, top_y = _head_top_y_per_column(parse_map, use_full_hair=True)
+    if cols_with_head.size == 0:
+        return hairline_norm
+    out = hairline_norm.copy()
+    for i in range(out.shape[0]):
+        x_px = float(out[i, 0]) * w
+        idx = int(np.searchsorted(cols_with_head, x_px))
+        idx = min(max(idx, 0), cols_with_head.size - 1)
+        sil_y = float(top_y[idx]) + margin_px
+        y_px = float(out[i, 1]) * h
+        if y_px < sil_y:
+            out[i, 1] = sil_y / h
+    return out
+
+
+def sample_hairline_clamped(
+    landmarks_norm: np.ndarray,
+    parse_map: np.ndarray,
+    fallback_extrapolation: float = 0.18,
+) -> tuple[np.ndarray, np.ndarray]:
+    """策略 A（baseline + 头部轮廓钳制）：与默认 `sample_hairline` 完全一致的检测，
+    额外用 `clamp_hairline_to_silhouette` 兜底 —— 检测失效 fallback 出的点不再可能
+    跑到头部外面的背景，而是贴着头部实际轮廓顶部。改动小、风险低，只在检测失效/
+    fallback 越界时才生效，正常长发照片的结果与 baseline 完全一致。
+    """
+    hairline, valid = sample_hairline(landmarks_norm, parse_map, fallback_extrapolation)
+    hairline = clamp_hairline_to_silhouette(hairline, parse_map)
+    return hairline, valid
+
+
 # ---------------------------------------------------------------------------
 # Alternative hairline sampling strategies.
 #

hairline/redraw.py

@@ -0,0 +1,80 @@
+"""直接调 ComfyUI 重绘 — 替代 local_test HTTP 服务。
+
+将 local_test/app.py 的核心逻辑（遮罩处理 + ComfyUI 调用）提取为 Python 函数，
+不再需要独立 Flask 服务。使用 0716add-hair-api.json 工作流（steps=4）。
+"""
+from __future__ import annotations
+
+import io
+import logging
+import os
+
+import numpy as np
+from PIL import Image, ImageFilter
+
+from . import comfyui
+
+logger = logging.getLogger("hair.worker")
+
+_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")
+
+
+def _process_mask_to_rgba(image_bytes: bytes, mask_bytes: bytes) -> bytes:
+    """将分开的 image + mask 处理为 ComfyUI 用的 RGBA PNG bytes。
+
+    复制 local_test/app.py 的遮罩处理逻辑：
+    1. 加载 image 为 RGB
+    2. 加载 mask 为 RGBA，取所有通道 max 值（支持红/白/alpha 遮罩）
+    3. resize mask 到与 image 一致
+    4. 高斯模糊(radius=4) 柔化边缘
+    5. alpha = 255 - mask（绘制区=255 → alpha=0 → 重绘区）
+    6. 合成 RGBA PNG
+    """
+    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    mask_img = Image.open(io.BytesIO(mask_bytes)).convert("RGBA")
+    mask_arr = np.array(mask_img)
+    mask_data = np.max(mask_arr, axis=2)  # (H, W) uint8
+
+    mask_data_img = Image.fromarray(mask_data, mode="L")
+    if mask_data_img.size != image.size:
+        mask_data_img = mask_data_img.resize(image.size, Image.LANCZOS)
+    mask_data_img = mask_data_img.filter(ImageFilter.GaussianBlur(radius=4))
+
+    # ComfyUI LoadImage: mask = 1.0 - (alpha/255)
+    # alpha=0 -> mask=1.0 (inpaint), alpha=255 -> mask=0.0 (keep)
+    comfyui_alpha = Image.eval(mask_data_img, lambda x: 255 - x)
+
+    r, g, b = image.split()
+    rgba = Image.merge("RGBA", (r, g, b, comfyui_alpha))
+
+    buf = io.BytesIO()
+    rgba.save(buf, format="PNG")
+    return buf.getvalue()
+
+
+def run_redraw(image_bytes: bytes, mask_bytes: bytes,
+               prompt: str | None = None, timeout: float = 300.0,
+               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"
+        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
+
+    Raises:
+        RuntimeError: ComfyUI 执行失败
+        TimeoutError: ComfyUI 超时
+    """
+    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,
+                       unet_name=unet_name)

hairline/service.py

@@ -14,21 +14,82 @@ from . import constants as C
 from . import comfyui
 from .face_landmarks import FaceLandmarker
 from .face_parsing import FaceParser
-from .hairline_2d import sample_hairline, smooth_hairline
+from .hairline_2d import (
+    smooth_hairline, sample_hairline_clamped,
+)
 from .lift_3d import lift_hairline_to_3d, build_middle_row, assemble_full
 from .render import load_ext_mesh, load_texture_rgba, render_hairline_overlay, build_overlay_layer
 from .mask import build_inpaint_mask, compose_comfy_rgba, mask_from_curve
 from .marker_detect import detect_marker_hairline, path_to_curve_mask
 
+import base64
 import io
 import logging
 
 logger = logging.getLogger("hair.worker")
 
+# 接口2 女性发型 key → change_hair hair_id（chang_*）映射：换发型+Flux-2 整帧重绘用。
+# 与接口12 final 的 5 型一一对应。
+_FEMALE_KEY_TO_CHANG = {
+    "ellipse": "chang_tuoyuan",   # 椭圆
+    "flower": "chang_huaban",     # 花瓣
+    "heart": "chang_xinxing",     # 心形
+    "straight": "chang_zhixian",  # 直线
+    "wave": "chang_bolang",       # 波浪
+}
+
 _REPO = os.path.dirname(os.path.dirname(__file__))
 _TEXTURE_DIR = os.path.join(_REPO, "hairline_texture")
 _BLACK_TEXTURE_DIR = os.path.join(_REPO, "hairline_texture_black")
 
+# 三接口（接口2女重绘 / 接口2男 / 接口3）统一的 ComfyUI 重绘 prompt。
+# 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")
+
+# 接口2 女重绘整条管线（swapHair + ComfyUI）送模型前限边。真实照片常达 1257x1495：
+# 全分辨率 ComfyUI 重绘要 13~21s 且激活显存把模型挤出。
+# 策略：输入图长边 > REDRAW_MAX_SIDE 才等比缩到该长边；≤ 时原图分辨率直送（不放大）。
+# 默认 1024：大于 1024 的图压到 1024（画质/速度均衡），≤1024 的小图保持原分辨率重绘。
+# 可用 REDRAW_MAX_SIDE 覆盖；0=永不缩图（原图直送）。
+_REDRAW_MAX_SIDE = int(os.getenv("REDRAW_MAX_SIDE", "1024"))
+
+def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0,
+                       max_side=None, unet_name=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。
+    """
+    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 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)
+            msk_s = cv2.resize(msk, (nw, nh), interpolation=cv2.INTER_NEAREST)
+            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, eff_side)
+    # front=True：接口2 时延敏感，插到 ComfyUI 队列最前，避免排在接口3/5 的批量任务后面
+    out = run_redraw(image_png_bytes, mask_png_bytes, timeout=timeout,
+                     prompt=_REDRAW_PROMPT, front=True, unet_name=unet_name)
+    if scale < 1.0 and out:
+        out = _upscale_png_to(out, orig_w, orig_h)
+    return out
+
 # 发际线贴图档位：middle=默认(hairline_texture/)，high/low 各自独立文件夹。
 _TEXTURE_DIRS = {
     "middle": _TEXTURE_DIR,
@@ -36,9 +97,8 @@ _TEXTURE_DIRS = {
     "low": os.path.join(_REPO, "hairline_texture_low"),
 }
 
-# ⚠️ 本 worker 是 RTX 5090(sm_120)，torch 2.2.2(cu121) 只编到 sm_90，CUDA 跑算子会报
-#    "no kernel image"。SegFormer 默认走 CPU（~2.5s/张）。换 torch cu128 后可设 SEG_DEVICE=cuda。
-_SEG_DEVICE = os.getenv("SEG_DEVICE", "cpu")
+# torch 2.7.1+cu128 已支持 RTX 5090 (sm_120)，SegFormer 走 GPU（~0.05s/张）
+_SEG_DEVICE = os.getenv("SEG_DEVICE", "cuda")
 
 _landmarker = None
 _parser = None
@@ -100,13 +160,19 @@ def extract_502(image_bgr: np.ndarray):
 
 
 def extract_context(image_bgr: np.ndarray):
-    """照片(BGR) → {landmarks, parse_map, points, valid}。无人脸返回 None。"""
+    """照片(BGR) → {landmarks, parse_map, points, valid}。无人脸返回 None。
+
+    发际线几何检测固定用 `sample_hairline_clamped`（射线检测 + 头部轮廓钳制）：
+    短发/剃光头照片（如 man_test.jpg）中间锚点检测失效时，纯射线检测的固定 fallback
+    偏移会把点顶到头部轮廓外面的背景，产生"发际线贴到头部外面"的视觉 bug；钳制兜底后
+    fallback 点不会再跑出头部轮廓，正常长发照片结果与旧行为一致。
+    """
     rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
     landmarks = get_landmarker().detect(rgb)
     if landmarks is None:
         return None
     parse_map = get_parser().parse(rgb)
-    hairline_2d, valid = sample_hairline(landmarks, parse_map)
+    hairline_2d, valid = sample_hairline_clamped(landmarks, parse_map)
     hairline_2d = smooth_hairline(hairline_2d, valid)
     hairline_3d = lift_hairline_to_3d(landmarks, hairline_2d)
     middle_3d = build_middle_row(landmarks, hairline_3d)
@@ -140,8 +206,9 @@ 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):
-    """指定发际线类型：预览图(白线) + 生发图(ComfyUI)。
+                         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。
       为 None 时生成全部（兼容旧调用）。
@@ -149,7 +216,8 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
       False 时用**干净原图 + 空遮罩**送 ComfyUI（不烧黑色模板线）。
     prompt（默认 None）：ComfyUI 提示词，非 None 时替换工作流节点60文本。
     workflow_path（默认 None）：ComfyUI 工作流 JSON 路径，None 用默认 add_hair.json。
-    Returns: list[dict] {"hairline_type","order","image_bgr"(预览), "grown_png"(bytes 或 None)}。
+    Returns: list[dict] {"hairline_type","order","overlay"((H,W,4) RGBA 透明层),
+                        "grown_png"(bytes 或 None)}。
     无人脸返回 None。某张 ComfyUI 失败时该项 grown_png=None，不抛异常。
     """
     if gender not in ("male", "female"):
@@ -170,16 +238,23 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
     if not use_mask:
         try:
             h, w = image_bgr.shape[:2]
+            img_s, msk_s, gsc = _prep_comfy_input(image_bgr, np.zeros((h, w), np.uint8))
             buf = io.BytesIO()
-            compose_comfy_rgba(image_bgr, np.zeros((h, w), np.uint8)).save(buf, format="PNG")
-            shared_grown = comfyui.run(buf.getvalue(), prompt=prompt, workflow_path=workflow_path)
+            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,
+                                       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
             logger.warning("接口2 生发图失败(无遮罩)：%s", e)
 
     results = []
+    h, w = image_bgr.shape[:2]
     for order, (key, white_path) in items:
         white = load_texture_rgba(white_path)
-        preview = render_hairline_overlay(image_bgr, ctx["points"], ext_faces, uv, white)
+        overlay = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
 
         if not use_mask:
             grown_png = shared_grown
@@ -189,14 +264,124 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
                 black = load_texture_rgba(_black_texture_path(white_path))
                 marked, mask = build_inpaint_mask(
                     image_bgr, ctx["landmarks"], ctx["parse_map"], ctx["points"], black)
+                m_s, msk_s, gsc = _prep_comfy_input(marked, mask)
                 buf = io.BytesIO()
-                compose_comfy_rgba(marked, mask).save(buf, format="PNG")
-                grown_png = comfyui.run(buf.getvalue(), prompt=prompt, workflow_path=workflow_path)
+                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,
+                                        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
                 logger.warning("接口2 生发图失败 type=%s：%s", key, e)
 
         results.append({"hairline_type": key, "order": order,
-                        "image_bgr": preview, "grown_png": grown_png})
+                        "overlay": overlay, "grown_png": grown_png})
+    return results
+
+
+def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | None,
+                               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，
+    调 face_analysis.hairline_grow.generate_hairline_redraw（= 接口12 final 管线，参数用
+    redraw_defaults）拿到 ④ final（接缝融合基底）+ ⑤-② 纯红遮罩 PNG，再**后端直接调
+    ComfyUI**（0716add-hair-api.json 工作流）完成发际线带重绘，重绘结果作为生发图。
+
+    overlay 仍是发际线曲线透明层（与 generate_grow_results 完全一致）。
+    Returns: list[dict] {"hairline_type","order","overlay","grown_png"(jpg bytes 或 None)}；
+             无人脸返回 None。单个发型换发型/重绘失败时 grown_png=None，不抛异常。
+    """
+    from face_analysis.hairline_grow import generate_hairline_redraw, NoFaceError
+    from face_analysis.head_mask import SEGFORMER_HAIR
+
+    ctx = extract_context(image_bgr)
+    if ctx is None:
+        return None
+    uv, ext_faces = load_ext_mesh()
+
+    # 复用 extract_context 已算好的 SegFormer parse_map，避免 generate_hairline_redraw 内部重复分割
+    hair_mask_reuse = (ctx["parse_map"] == SEGFORMER_HAIR)
+
+    textures = get_texture_map()["female"]  # [(key, path), ...] 已排序
+    if hair_styles is not None:
+        items = [(s, textures[s - 1]) for s in hair_styles]
+    else:
+        items = list(enumerate(textures, start=1))
+
+    results = []
+    h, w = image_bgr.shape[:2]
+
+    # 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 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, eff_side)
+
+    for order, (key, white_path) in items:
+        white = load_texture_rgba(white_path)
+        overlay = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
+
+        grown_png = None
+        chang_id = _FEMALE_KEY_TO_CHANG.get(key)
+        if chang_id is None:
+            logger.warning("接口2 换发型：female key=%s 无对应 chang_id，跳过生发图", key)
+        else:
+            try:
+                import time as _t
+                _ts0 = _t.perf_counter()
+                data = generate_hairline_redraw(redraw_img, chang_id, hair_mask=hair_mask_redraw, **redraw_defaults)
+                _ts1 = _t.perf_counter()
+                steps = data.get("steps") or {}
+                # ④ final（接缝融合基底）+ ⑤-② 纯红遮罩 PNG
+                final_b64 = steps.get("final_base64") or ""
+                mask_b64 = steps.get("redraw_band_mask_base64") or ""
+                if not final_b64 or not mask_b64:
+                    logger.warning("接口2 换发型：type=%s final/遮罩缺失（final=%d mask=%d）",
+                                   key, len(final_b64), len(mask_b64))
+                else:
+                    # 去掉 data URI 前缀
+                    if final_b64.startswith("data:"):
+                        final_b64 = final_b64.split(",", 1)[1]
+                    if mask_b64.startswith("data:"):
+                        mask_b64 = mask_b64.split(",", 1)[1]
+                    final_bytes = base64.b64decode(final_b64)
+                    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,
+                                                   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",
+                                key, _ts1 - _ts0,
+                                _tm.get("mask", 0), _tm.get("swap", 0), _tm.get("blend", 0),
+                                _tr1 - _tr0)
+                    if grown_png is None:
+                        logger.warning("接口2 换发型：type=%s 重绘结果为空", key)
+                    elif redraw_img is not image_bgr:
+                        # 管线在降分辨率图上跑，结果放大回原尺寸
+                        grown_png = _upscale_png_to(grown_png, w, h)
+            except NoFaceError:
+                logger.warning("接口2 换发型：type=%s 未检出人脸", key)
+            except Exception as e:  # noqa: BLE001  单张失败不拖垮整请求
+                logger.warning("接口2 换发型图失败 type=%s：%s", key, e)
+
+        results.append({"hairline_type": key, "order": order,
+                        "overlay": overlay, "grown_png": grown_png})
     return results
 
 
@@ -216,7 +401,7 @@ def _grow_from_texture(image_bgr: np.ndarray, ctx: dict, white_path: str | None,
             h, w = image_bgr.shape[:2]
             marked, mask = image_bgr, np.zeros((h, w), np.uint8)
         buf = io.BytesIO()
-        compose_comfy_rgba(marked, mask).save(buf, format="PNG")
+        compose_comfy_rgba(marked, mask).save(buf, format="PNG", compress_level=1)
         return comfyui.run(buf.getvalue(), prompt=prompt)
     except Exception as e:  # noqa: BLE001  单张失败不拖垮整请求
         logger.warning("接口5 生发图失败：%s", e)
@@ -225,15 +410,27 @@ def _grow_from_texture(image_bgr: np.ndarray, ctx: dict, white_path: str | None,
 
 def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
                            hair_styles: list[int], use_mask: bool = True,
-                           prompt: str | None = None):
-    """接口5：对选中发型返回 middle/high/low 三档发际线叠图 + 生发图(同接口2)。
+                           prompt: str | None = None,
+                           generate_grow_image: bool = True,
+                           redraw_max_side: int | None = None,
+                           unet_name: str | None = None,
+                           v2_defaults: dict | None = None):
+    """接口5：对选中发型返回 middle/high/low 三档发际线透明叠图 + 生发图(同接口2)。
 
     入参同接口2：先选 gender，再多选 hair_styles（必填，1-indexed 按贴图排序）。
-    每个选中发型返回三档叠图（middle/high/low）与一张生发图；三档贴图同名，
-    生发黑模板固定取自 hairline_texture_black/（middle），故生发目标固定 middle 档。
-    use_mask/prompt：同接口2 的生发参数。
-    Returns: {"images":[{hairline_type,order,overlays:{middle,high,low}(BGR),grown_png}],
-              "best_center":(x,y)}；无人脸 None。best_center 取首个选中发型的 middle 档。
+    每个选中发型返回三档叠图（middle/high/low，RGBA 透明层只含发际线曲线）与一张生发图；
+
+    生发机制（同接口2，按性别分流）：
+      - female：generate_grow_results_swap（swapHair + Flux-2 整帧重绘）
+      - male：generate_grow_results（ComfyUI add_hair inpaint）
+    redraw_max_side / unet_name / v2_defaults：female 路径参数，同接口2。
+      male 路径仅用 unet_name；redraw_max_side/v2_defaults 对 male 无意义（忽略）。
+    use_mask/prompt：仅 male 路径生效（同接口2 male）。
+    generate_grow_image（默认 True）：是否生成生发图（最耗时）。False 时跳过生发，
+        各发型 grown_png 恒为 None，可大幅降低耗时（仅留三档发际线叠图与中心点）。
+    Returns: {"images":[{hairline_type,order,overlays:{middle,high,low}((H,W,4) RGBA 透明层),grown_png}],
+              "best_centers":{"middle":(x,y),"high":(x,y),"low":(x,y)}}；无人脸 None。
+              best_centers 取首个选中发型三档各自的发际线中点。
     """
     if gender not in ("male", "female"):
         raise ValueError(f"gender 必须是 male/female，收到 {gender!r}")
@@ -251,33 +448,96 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
     # 三档贴图表（同性别、同 key 顺序，因三个文件夹同名）
     tex_by_level = {lv: get_texture_map(lv)[gender] for lv in _TEXTURE_DIRS}
 
-    # use_mask=False：干净原图+空遮罩与贴图无关，只跑一次 ComfyUI，选中项复用
-    shared_grown = None
-    if not use_mask:
-        shared_grown = _grow_from_texture(image_bgr, ctx, None, use_mask=False, prompt=prompt)
+    # 生发图（同接口2，按性别分流）：一次性算出所有选中发型的生发图，按 order 对应回叠图。
+    # female→generate_grow_results_swap（swapHair+Flux-2 整帧重绘）；
+    # male→generate_grow_results（ComfyUI add_hair inpaint）。
+    # generate_grow_image=False 时跳过，grown_by_order 为空 dict（各发型 grown_png 恒 None）。
+    grown_by_order: dict[int, bytes | None] = {}
+    if generate_grow_image:
+        try:
+            if gender == "female":
+                items = generate_grow_results_swap(
+                    image_bgr, hair_styles, v2_defaults or {},
+                    redraw_max_side=redraw_max_side, unet_name=unet_name)
+            else:
+                items = generate_grow_results(
+                    image_bgr, gender, use_mask, prompt, hair_styles,
+                    unet_name=unet_name)
+            if items is None:
+                return None  # 无人脸（同接口2 的 None 语义）
+            for it in items:
+                grown_by_order[it["order"]] = it.get("grown_png")
+        except Exception as e:  # noqa: BLE001  整批生发失败不拖垮叠图主结果
+            logger.warning("接口5 生发批量失败（gender=%s）：%s", gender, e)
 
-    images, best_center = [], None
+    def _center_of(overlay):
+        """从某档发际线透明叠图取面部中轴处的发际线中点 (x,y)，无像素返回 None。"""
+        ys, xs = np.where(overlay[:, :, 3] > 40)
+        if not xs.size:
+            return None
+        near = np.abs(xs - face_cx) <= max(2, int(w * 0.02))
+        col_ys = ys[near] if near.any() else ys[np.argsort(np.abs(xs - face_cx))[:20]]
+        return (int(round(face_cx)), int(round(float(col_ys.mean()))))
+
+    images, best_centers = [], None
     for s in hair_styles:  # s = 1-indexed 发型序号
-        key, mid_path = tex_by_level["middle"][s - 1]
+        key, _mid_path = tex_by_level["middle"][s - 1]
         overlays = {}
         for lv in _TEXTURE_DIRS:
             white = load_texture_rgba(tex_by_level[lv][s - 1][1])
-            overlays[lv] = render_hairline_overlay(image_bgr, ctx["points"], ext_faces, uv, white)
-        # 生发：固定 middle 黑模板
-        grown_png = shared_grown if not use_mask else \
-            _grow_from_texture(image_bgr, ctx, mid_path, use_mask=True, prompt=prompt)
+            overlays[lv] = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
+        # 生发图：从按性别算好的结果里按 order 取（generate_grow_image=False 时缺省 None）
+        grown_png = grown_by_order.get(s)
         images.append({"hairline_type": key, "order": s,
                        "overlays": overlays, "grown_png": grown_png})
-        # best_center：首个选中发型的 middle 档发际线中点（面部中轴处的发际线 y）
-        if best_center is None:
-            white = load_texture_rgba(mid_path)
-            overlay = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
-            ys, xs = np.where(overlay[:, :, 3] > 40)
-            if xs.size:
-                near = np.abs(xs - face_cx) <= max(2, int(w * 0.02))
-                col_ys = ys[near] if near.any() else ys[np.argsort(np.abs(xs - face_cx))[:20]]
-                best_center = (int(round(face_cx)), int(round(float(col_ys.mean()))))
-    return {"images": images, "best_center": best_center}
+        # best_centers：首个选中发型三档（middle/high/low）发际线中点
+        if best_centers is None:
+            best_centers = {lv: _center_of(overlays[lv]) for lv in _TEXTURE_DIRS}
+    return {"images": images, "best_centers": best_centers}
+
+
+# 接口3 送 ComfyUI 前限边，降低峰值显存，避免与接口2 切换时把 Flux 挤出。
+# 统一 prompt 后 Flux 不再被 CLIP 挤出，接口3 可用较高分辨率。可用 GROW_B_MAX_SIDE 覆盖。
+_GROW_B_MAX_SIDE = int(os.getenv("GROW_B_MAX_SIDE", "1024"))
+
+def _downscale_max_side(img_bgr: np.ndarray, max_side: int) -> tuple[np.ndarray, float]:
+    """长边超过 max_side 时等比例缩小；返回 (图, scale)，scale=新/旧。"""
+    h, w = img_bgr.shape[:2]
+    m = max(h, w)
+    if max_side <= 0 or m <= max_side:
+        return img_bgr, 1.0
+    scale = max_side / float(m)
+    nw = max(1, int(round(w * scale)))
+    nh = max(1, int(round(h * scale)))
+    out = cv2.resize(img_bgr, (nw, nh), interpolation=cv2.INTER_AREA)
+    return out, scale
+
+
+def _upscale_png_to(png_bytes: bytes, out_w: int, out_h: int) -> bytes:
+    """把 Comfy 输出 PNG 双线性拉回原图尺寸（仅展示对齐，不增加推理细节）。"""
+    arr = np.frombuffer(png_bytes, np.uint8)
+    img = cv2.imdecode(arr, cv2.IMREAD_UNCHANGED)
+    if img is None:
+        return png_bytes
+    if img.shape[1] == out_w and img.shape[0] == out_h:
+        return png_bytes
+    resized = cv2.resize(img, (out_w, out_h), interpolation=cv2.INTER_LINEAR)
+    ok, buf = cv2.imencode(".png", resized)
+    return buf.tobytes() if ok else png_bytes
+
+
+def _prep_comfy_input(img_bgr: np.ndarray, mask: np.ndarray) -> tuple[np.ndarray, np.ndarray, float]:
+    """单段 ComfyUI 生发（接口2男 / 接口3）送图前限边到 GROW_B_MAX_SIDE。
+    返回 (缩后图, 缩后遮罩, scale)；scale<1 时调用方需把结果放大回原尺寸。"""
+    h, w = img_bgr.shape[:2]
+    if _GROW_B_MAX_SIDE <= 0 or max(h, w) <= _GROW_B_MAX_SIDE:
+        return img_bgr, mask, 1.0
+    out, scale = _downscale_max_side(img_bgr, _GROW_B_MAX_SIDE)
+    nh, nw = out.shape[:2]
+    msk = cv2.resize(mask, (nw, nh), interpolation=cv2.INTER_NEAREST)
+    logger.info("接口2男/接口3 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
+                w, h, nw, nh, _GROW_B_MAX_SIDE)
+    return out, msk, scale
 
 
 def generate_grow_b(marked_bgr: np.ndarray, use_mask: bool = True, prompt: str = None):
@@ -286,12 +546,23 @@ def generate_grow_b(marked_bgr: np.ndarray, use_mask: bool = True, prompt: str =
     检测路径只用来**建遮罩**；ComfyUI 输入图用 **marked 原图**（含医生手绘线，
     工作流提示词会清除黑线再生发）。
 
+    进 Comfy 前若长边 > GROW_B_MAX_SIDE（默认 896）会先等比例缩小，降低峰值显存；
+    输出再拉回原图尺寸。
+
     use_mask（默认 True）：是否启用自动检测的遮罩，用于测试对比。
       - True：检测手绘线 → 建遮罩 → alpha=255−mask（透明区=重绘区，节点44 画黄色参考区）。
       - False：跳过检测，直接送划线图，alpha 全 255（空遮罩，节点26 mask 为空），
         模型仅凭医生黑线参考生发。无需改工作流，唯一变量是遮罩。
     Returns: {"grown_png": bytes 或 None, "status": "ok"|"no_face"|"no_line"}。
     """
+    orig_h, orig_w = marked_bgr.shape[:2]
+    marked_bgr, _scale = _downscale_max_side(marked_bgr, _GROW_B_MAX_SIDE)
+    if _scale < 1.0:
+        logger.info(
+            "接口3 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
+            orig_w, orig_h, marked_bgr.shape[1], marked_bgr.shape[0], _GROW_B_MAX_SIDE,
+        )
+
     h, w = marked_bgr.shape[:2]
     if use_mask:
         rgb = cv2.cvtColor(marked_bgr, cv2.COLOR_BGR2RGB)
@@ -309,8 +580,10 @@ def generate_grow_b(marked_bgr: np.ndarray, use_mask: bool = True, prompt: str =
         mask = np.zeros((h, w), np.uint8)   # 空遮罩：alpha 全 255，跳过检测
 
     buf = io.BytesIO()
-    compose_comfy_rgba(marked_bgr, mask).save(buf, format="PNG")   # marked 原图 + 遮罩
+    compose_comfy_rgba(marked_bgr, mask).save(buf, format="PNG", compress_level=1)   # marked + 遮罩
     grown_png = comfyui.run(buf.getvalue(), prompt=prompt)
+    if _scale < 1.0 and grown_png:
+        grown_png = _upscale_png_to(grown_png, orig_w, orig_h)
     return {"grown_png": grown_png, "status": "ok"}
 
 

image/compare_test/archive_20260725/batch_test.log

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

image/compare_test/archive_20260725/report.csv

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

image/compare_test/archive_20260725/report.json

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

image/compare_test/batch_test.py

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

image/compare_test/gen_report.py

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

image/compare_test/report-server.service

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

image/reports/index.html

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

image/reports/report-server.service

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

image/reports/v1

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

image/reports/v2

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

image/reports/wave

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

image/res_test/batch_test.py

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

image/res_test/gen_report.py

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

image/res_test/report-server-v2.service

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

image/wave_test/batch_test.py

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

image/wave_test/gen_report.py

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

image/wave_test/report-server-wave.service

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

local_test/README.md

@@ -0,0 +1,174 @@
+# 发型补全服务 API 文档
+
+## 服务概述
+
+本服务提供基于 ComfyUI 的发型补全（局部重绘）能力。通过传入人物图片和遮罩图片，调用 ComfyUI 工作流（`0716add-hair.json`）生成补全后的图片。
+
+## 技术栈
+
+- **框架**: Flask
+- **依赖**: requests, Pillow, numpy
+- **后端**: ComfyUI (http://127.0.0.1:8188)
+
+## 服务地址
+
+- **HTTP**: `http://127.0.0.1:8899`
+- **前端页面**: `http://127.0.0.1:8899/`
+- **API接口**: `http://127.0.0.1:8899/api/generate`
+
+## 启动方式
+
+### 使用脚本（推荐）
+
+```bash
+# 启动服务
+cd /home/ubuntu/hair/local_test
+./start.sh
+
+# 停止服务
+./stop.sh
+```
+
+### 直接运行
+
+```bash
+cd /home/ubuntu/hair/local_test
+/home/ubuntu/ComfyUI/venv/bin/python app.py
+```
+
+## API 接口
+
+### POST /api/generate
+
+调用 ComfyUI 工作流，传入图片和遮罩，返回生成结果。
+
+#### 请求参数
+
+| 参数名 | 类型 | 必填 | 说明 |
+|--------|------|------|------|
+| 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" |
+
+#### 遮罩图片格式说明
+
+服务支持多种遮罩格式，自动提取遮罩区域：
+
+| 格式类型 | 示例 | 遮罩区域标识 |
+|----------|------|--------------|
+| 红色遮罩 | 红色画笔绘制 | R=255 的像素 |
+| 白色遮罩 | 白色画笔绘制 | R=G=B=255 的像素 |
+| Alpha 遮罩 | 透明背景 | A=255 的像素 |
+
+服务会取所有通道的最大值作为遮罩强度，因此以上格式均可混用。
+
+**注意**: 遮罩区域表示需要重绘的部分，非遮罩区域保持原图不变。
+
+#### 请求示例（curl）
+
+```bash
+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" \
+  --output result.png
+```
+
+#### 请求示例（Python）
+
+```python
+import requests
+
+url = "http://127.0.0.1:8899/api/generate"
+files = {
+    "image": open("person.jpg", "rb"),
+    "mask": open("mask.png", "rb"),
+}
+data = {
+    "prompt": "填充遮罩区域的头发，皮肤加一点磨皮"
+}
+
+resp = requests.post(url, files=files, data=data, timeout=600)
+if resp.status_code == 200:
+    with open("result.png", "wb") as f:
+        f.write(resp.content)
+else:
+    print(f"Error: {resp.json()}")
+```
+
+#### 响应
+
+**成功 (HTTP 200)**:
+
+返回 PNG 图片二进制数据，Content-Type: `image/png`。
+
+**失败 (HTTP 4xx/5xx)**:
+
+返回 JSON 格式错误信息：
+
+```json
+{
+    "error": "错误描述"
+}
+```
+
+#### 错误码
+
+| 状态码 | 说明 |
+|--------|------|
+| 500 | 内部错误（文件处理失败、ComfyUI 返回错误等） |
+| 503 | 无法连接到 ComfyUI（服务未启动或端口错误） |
+| 500 | 超时（工作流执行超过 5 分钟） |
+
+## 工作流说明
+
+服务使用的工作流 `0716add-hair.json` 包含以下处理步骤：
+
+1. **加载模型**: Flux 2 Klein 9B (FP8) + Qwen 3.8B CLIP
+2. **图片上传**: 将原图与遮罩合成为 RGBA 格式上传至 ComfyUI
+3. **遮罩处理**: 填充孔洞 → 转换为图像 → 缩放 → 转换回遮罩
+4. **图像缩放**: 按比例缩放至合适尺寸（最大边长 1024，8 的倍数）
+5. **VAE 编码**: 将图像编码为 latent
+6. **采样生成**: 使用 Flux 模型 + ReferenceLatent 进行局部重绘
+7. **VAE 解码**: 将 latent 解码为图像
+8. **颜色匹配**: 使用 ColorMatch 保持颜色一致
+9. **保存结果**: 返回生成的图片
+
+## 前置依赖
+
+启动服务前需确保：
+
+1. **ComfyUI 已启动**: `http://127.0.0.1:8188` 可访问
+2. **模型文件存在**:
+   - `models/unet/flux2.0/flux-2-klein-9b-fp8.safetensors`
+   - `models/vae/flux2-vae.safetensors`
+   - `models/clip/qwen_3_8b_fp8mixed.safetensors`
+3. **虚拟环境已激活**: 使用 `/home/ubuntu/ComfyUI/venv/bin/python`
+
+## 文件结构
+
+```
+/home/ubuntu/hair/local_test/
+├── app.py          # Flask 后端服务
+├── index.html      # 前端测试页面
+├── test_api.py     # API 测试脚本
+├── README.md       # 本文档
+├── output/         # 测试结果输出目录
+├── 用来重绘.jpg     # 测试人物图片
+└── 用来重绘.png     # 测试遮罩图片
+```
+
+## 使用流程
+
+1. 启动 ComfyUI（`python main.py --listen`）
+2. 启动本服务（`python app.py`）
+3. 调用 API 或访问前端页面上传图片和遮罩
+4. 等待生成完成（通常 30-60 秒）
+5. 获取返回的 PNG 图片
+
+## 注意事项
+
+- 请求超时时间为 5 分钟，生成复杂图片可能需要较长时间
+- 遮罩图片尺寸需与人物图片一致，服务会自动缩放对齐
+- 建议使用红色或白色绘制遮罩，确保遮罩强度足够
+- 服务会自动对遮罩边缘进行高斯模糊（radius=4），避免硬边

local_test/app.py

@@ -0,0 +1,339 @@
+#!/usr/bin/env python3
+"""Hair inpainting service - calls ComfyUI workflow with image + mask."""
+import io
+import json
+import time
+import random
+import logging
+import traceback
+import requests
+from flask import Flask, request, jsonify, send_file
+import numpy as np
+from PIL import Image, ImageFilter
+
+# 让 PIL 支持 iPhone 的 HEIC/HEIF 照片（浏览器 accept="image/*" 会允许选中它们）。
+try:
+    from pillow_heif import register_heif_opener
+    register_heif_opener()
+    _HEIF_OK = True
+except Exception:
+    _HEIF_OK = False
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+)
+log = logging.getLogger("hair")
+
+app = Flask(__name__)
+COMFYUI_URL = "http://127.0.0.1:8188"
+
+# 允许浏览器跨域直连本服务（如 hair 项目的测试页）。
+# 不引入 flask-cors 依赖，直接在响应头 + OPTIONS 预检里处理。
+_CORS_HEADERS = {
+    "Access-Control-Allow-Origin": "*",
+    "Access-Control-Allow-Methods": "POST, OPTIONS",
+    "Access-Control-Allow-Headers": "Content-Type",
+    "Access-Control-Expose-Headers": "X-Generate-Time",
+}
+
+
+@app.after_request
+def _add_cors(resp):
+    for k, v in _CORS_HEADERS.items():
+        resp.headers[k] = v
+    return resp
+
+
+@app.route("/api/generate", methods=["OPTIONS"])
+def _generate_preflight():
+    """CORS 预检：浏览器 POST 前会先发 OPTIONS。"""
+    return ("", 204)
+
+
+def build_workflow(image_filename, prompt_text, seed=None):
+    """Build ComfyUI API workflow from the 0716add-hair.json structure."""
+    if seed is None:
+        seed = random.randint(0, 2**53)
+
+    return {
+        # Loaders
+        "16": {"class_type": "UNETLoader", "inputs": {
+            "unet_name": "flux2.0/flux-2-klein-9b-fp8.safetensors",
+            "weight_dtype": "fp8_e4m3fn_fast"}},
+        "3": {"class_type": "VAELoader", "inputs": {
+            "vae_name": "flux2-vae.safetensors"}},
+        "61": {"class_type": "CLIPLoader", "inputs": {
+            "clip_name": "qwen_3_8b_fp8mixed.safetensors",
+            "type": "flux2",
+            "device": "default"}},
+
+        # Input image (with mask in alpha channel)
+        "26": {"class_type": "LoadImage", "inputs": {
+            "image": image_filename}},
+
+        # Prompt
+        "60": {"class_type": "JjkText", "inputs": {
+            "text": prompt_text}},
+        "22": {"class_type": "CLIPTextEncode", "inputs": {
+            "clip": ["61", 0],
+            "text": ["60", 0]}},
+
+        # Image size
+        "31": {"class_type": "easy imageSize", "inputs": {
+            "image": ["26", 0]}},
+
+        # Mask processing: fill holes -> convert to image -> scale -> back to mask
+        "33": {"class_type": "Mask Fill Holes", "inputs": {
+            "masks": ["26", 1]}},
+        "36": {"class_type": "Convert Masks to Images", "inputs": {
+            "masks": ["33", 0]}},
+        "39": {"class_type": "ImageScale", "inputs": {
+            "image": ["36", 0],
+            "upscale_method": "nearest-exact",
+            "width": ["31", 0],
+            "height": ["31", 1],
+            "crop": "disabled"}},
+        "37": {"class_type": "Image To Mask", "inputs": {
+            "image": ["39", 0],
+            "method": "intensity"}},
+
+        # Scale image+mask by aspect ratio
+        "32": {"class_type": "LayerUtility: ImageScaleByAspectRatio V2", "inputs": {
+            "image": ["26", 0],
+            "mask": ["37", 0],
+            "aspect_ratio": "custom",
+            "proportional_width": ["31", 0],
+            "proportional_height": ["31", 1],
+            "fit": "letterbox",
+            "method": "lanczos",
+            "round_to_multiple": "8",
+            "scale_to_side": "None",
+            "scale_to_length": 1024,
+            "background_color": "#000000"}},
+
+        # Preview (pass_through=true, just passes the image through)
+        "44": {"class_type": "ImageAndMaskPreview", "inputs": {
+            "image": ["32", 0],
+            "mask": ["32", 1],
+            "mask_opacity": 1,
+            "mask_color": "FFFF00",
+            "pass_through": True}},
+
+        # Get size of scaled image
+        "14": {"class_type": "GetImageSize+", "inputs": {
+            "image": ["44", 0]}},
+
+        # VAE encode the image
+        "13": {"class_type": "VAEEncode", "inputs": {
+            "pixels": ["44", 0],
+            "vae": ["3", 0]}},
+
+        # Flux model setup
+        "2": {"class_type": "ModelSamplingFlux", "inputs": {
+            "model": ["16", 0],
+            "max_shift": 1.15,
+            "base_shift": 0.5,
+            "width": ["14", 0],
+            "height": ["14", 1]}},
+        "19": {"class_type": "FluxGuidance", "inputs": {
+            "conditioning": ["22", 0],
+            "guidance": 1}},
+        "5": {"class_type": "ReferenceLatent", "inputs": {
+            "conditioning": ["19", 0],
+            "latent": ["13", 0]}},
+
+        # Empty latent for sampling
+        "7": {"class_type": "EmptySD3LatentImage", "inputs": {
+            "width": ["14", 0],
+            "height": ["14", 1],
+            "batch_size": 1}},
+
+        # Scheduler & guider
+        "1": {"class_type": "BasicScheduler", "inputs": {
+            "model": ["2", 0],
+            "scheduler": "simple",
+            "steps": 4,
+            "denoise": 1}},
+        "20": {"class_type": "BasicGuider", "inputs": {
+            "model": ["2", 0],
+            "conditioning": ["5", 0]}},
+
+        # Noise & sampler
+        "6": {"class_type": "RandomNoise", "inputs": {
+            "noise_seed": seed}},
+        "8": {"class_type": "KSamplerSelect", "inputs": {
+            "sampler_name": "euler"}},
+        "9": {"class_type": "SamplerCustomAdvanced", "inputs": {
+            "noise": ["6", 0],
+            "guider": ["20", 0],
+            "sampler": ["8", 0],
+            "sigmas": ["1", 0],
+            "latent_image": ["7", 0]}},
+
+        # VAE decode
+        "10": {"class_type": "VAEDecode", "inputs": {
+            "samples": ["9", 0],
+            "vae": ["3", 0]}},
+
+        # Color match with original image
+        "62": {"class_type": "ColorMatch", "inputs": {
+            "image_ref": ["26", 0],
+            "image_target": ["10", 0],
+            "method": "mkl",
+            "strength": 1,
+            "multithread": True}},
+
+        # Save result
+        "17": {"class_type": "SaveImage", "inputs": {
+            "images": ["62", 0],
+            "filename_prefix": "hair_inpaint"}},
+    }
+
+
+@app.route("/")
+def index():
+    return send_file("index.html")
+
+
+@app.route("/api/generate", methods=["POST"])
+def generate():
+    t_start = time.time()
+    try:
+        if "image" not in request.files or "mask" not in request.files:
+            msg = f"缺少上传文件: files={list(request.files.keys())}"
+            log.warning(msg)
+            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")
+        log.info(
+            "收到请求: image=%s mask=%s prompt=%r",
+            image_file.filename, mask_file.filename, prompt_text,
+        )
+
+        # Load original image as RGB
+        try:
+            image = Image.open(image_file).convert("RGB")
+        except Exception as e:
+            log.error("无法解码人物图片 %s: %s", image_file.filename, e)
+            hint = "" if _HEIF_OK else "（当前不支持 HEIC）"
+            return jsonify({
+                "error": f"无法识别人物图片格式{hint}，请改用 JPG/PNG: {e}"
+            }), 400
+
+        # Load mask and extract mask data from ALL channels (R, G, B, A)
+        # This handles different mask formats:
+        #   - Red mask (R=255 where drawn): user-provided PNG
+        #   - White mask (R=G=B=255 where drawn): frontend canvas
+        #   - Alpha mask (A=255 where drawn): transparent brush
+        try:
+            mask_img = Image.open(mask_file).convert("RGBA")
+        except Exception as e:
+            log.error("无法解码遮罩图片 %s: %s", mask_file.filename, e)
+            return jsonify({
+                "error": f"无法识别遮罩图片格式，请改用 JPG/PNG: {e}"
+            }), 400
+        mask_arr = np.array(mask_img)
+        # Use max of all channels: 255 where any color/alpha is drawn, 0 where empty
+        mask_data = np.max(mask_arr, axis=2)  # (H, W) uint8
+
+        # Ensure mask matches image size
+        mask_data_img = Image.fromarray(mask_data, mode="L")
+        if mask_data_img.size != image.size:
+            mask_data_img = mask_data_img.resize(image.size, Image.LANCZOS)
+
+        # Apply slight blur for soft edges (similar to ComfyUI's brush)
+        mask_data_img = mask_data_img.filter(ImageFilter.GaussianBlur(radius=4))
+
+        # ComfyUI LoadImage: mask = 1.0 - (alpha/255)
+        # So alpha=0 -> mask=1.0 (inpaint), alpha=255 -> mask=0.0 (keep)
+        # We want: drawn area (mask_data=255) -> inpaint -> alpha=0
+        #          undrawn area (mask_data=0) -> keep -> alpha=255
+        comfyui_alpha = Image.eval(mask_data_img, lambda x: 255 - x)
+
+        # Combine into RGBA (split RGB into separate channels first)
+        r, g, b = image.split()
+        rgba = Image.merge("RGBA", (r, g, b, comfyui_alpha))
+
+        # Upload to ComfyUI
+        img_bytes = io.BytesIO()
+        rgba.save(img_bytes, format="PNG")
+        img_bytes.seek(0)
+
+        upload_resp = requests.post(
+            f"{COMFYUI_URL}/upload/image",
+            files={"image": ("hair_input.png", img_bytes, "image/png")},
+            timeout=30,
+        )
+        upload_data = upload_resp.json()
+        if "name" not in upload_data:
+            log.error("ComfyUI 上传图片失败: %s", upload_data)
+            return jsonify({"error": f"Upload failed: {upload_data}"}), 500
+        image_filename = upload_data["name"]
+
+        # Build and queue workflow
+        workflow = build_workflow(image_filename, prompt_text)
+        prompt_resp = requests.post(
+            f"{COMFYUI_URL}/prompt",
+            json={"prompt": workflow},
+            timeout=30,
+        )
+        prompt_data = prompt_resp.json()
+        if "error" in prompt_data:
+            log.error(
+                "ComfyUI /prompt 校验失败: error=%s node_errors=%s",
+                prompt_data.get("error"), prompt_data.get("node_errors"),
+            )
+            return jsonify({"error": json.dumps(prompt_data["error"], ensure_ascii=False)}), 500
+        prompt_id = prompt_data["prompt_id"]
+
+        # Poll for completion (5 min timeout, 0.1s interval)
+        for _ in range(3000):
+            time.sleep(0.1)
+            history_resp = requests.get(
+                f"{COMFYUI_URL}/history/{prompt_id}", timeout=10
+            )
+            history_data = history_resp.json()
+            if prompt_id in history_data:
+                status = history_data[prompt_id].get("status", {})
+                if status.get("status_str") == "error":
+                    log.error(
+                        "ComfyUI 工作流执行失败: %s",
+                        json.dumps(status, ensure_ascii=False),
+                    )
+                    return jsonify({
+                        "error": "Workflow execution failed",
+                        "detail": status.get("messages", status),
+                    }), 500
+                outputs = history_data[prompt_id].get("outputs", {})
+                if "17" in outputs:  # SaveImage node
+                    image_info = outputs["17"]["images"][0]
+                    filename = image_info["filename"]
+                    subfolder = image_info.get("subfolder", "")
+                    img_type = image_info.get("type", "output")
+                    view_resp = requests.get(
+                        f"{COMFYUI_URL}/view",
+                        params={"filename": filename, "subfolder": subfolder, "type": img_type},
+                        timeout=30,
+                    )
+                    elapsed = time.time() - t_start
+                    log.info("重绘完成，服务端耗时 %.2fs", elapsed)
+                    resp = send_file(
+                        io.BytesIO(view_resp.content), mimetype="image/png"
+                    )
+                    resp.headers["X-Generate-Time"] = f"{elapsed:.2f}"
+                    return resp
+
+        return jsonify({"error": "Timeout: workflow did not complete in 5 minutes"}), 500
+
+    except requests.ConnectionError:
+        log.error("无法连接 ComfyUI @ %s", COMFYUI_URL)
+        return jsonify({"error": "Cannot connect to ComfyUI at " + COMFYUI_URL + ". Is it running?"}), 503
+    except Exception as e:
+        log.error("生成失败，未捕获异常:\n%s", traceback.format_exc())
+        return jsonify({"error": f"{type(e).__name__}: {e}"}), 500
+
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=8899, debug=False)

local_test/bench.py

@@ -0,0 +1,77 @@
+#!/usr/bin/env python3
+"""Benchmark ComfyUI hair-inpaint workflow across model / dtype / steps."""
+import io, time, sys
+import requests
+import numpy as np
+from PIL import Image, ImageFilter
+import app as A
+
+COMFY = "http://127.0.0.1:8188"
+
+
+def prep_and_upload():
+    image = Image.open("用来重绘.jpg").convert("RGB")
+    mask_img = Image.open("用来重绘.png").convert("RGBA")
+    mask_data = np.max(np.array(mask_img), axis=2)
+    m = Image.fromarray(mask_data, mode="L")
+    if m.size != image.size:
+        m = m.resize(image.size, Image.LANCZOS)
+    m = m.filter(ImageFilter.GaussianBlur(radius=4))
+    alpha = Image.eval(m, lambda x: 255 - x)
+    r, g, b = image.split()
+    rgba = Image.merge("RGBA", (r, g, b, alpha))
+    buf = io.BytesIO(); rgba.save(buf, format="PNG"); buf.seek(0)
+    up = requests.post(f"{COMFY}/upload/image",
+                       files={"image": ("hair_input.png", buf, "image/png")}).json()
+    return up["name"]
+
+
+def run_once(fname, model, dtype, steps):
+    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
+    r = requests.post(f"{COMFY}/prompt", json={"prompt": wf}).json()
+    if "prompt_id" not in r:
+        raise RuntimeError(f"submit failed: {str(r)[:300]}")
+    pid = r["prompt_id"]
+    deadline = time.time() + 180
+    while time.time() < deadline:
+        time.sleep(0.1)
+        h = requests.get(f"{COMFY}/history/{pid}").json()
+        if pid not in h:
+            continue
+        st = h[pid].get("status", {})
+        if st.get("status_str") == "error":
+            for m in st.get("messages", []):
+                if m[0] == "execution_error":
+                    raise RuntimeError(str(m[1])[:300])
+            raise RuntimeError("execution error")
+        if "17" in h[pid].get("outputs", {}):
+            ts = {mm[0]: mm[1].get("timestamp") for mm in st["messages"]}
+            return (ts["execution_success"] - ts["execution_start"]) / 1000.0
+    raise TimeoutError("run exceeded 180s")
+
+
+CONFIGS = [
+    ("flux2.0/flux-2-klein-9b-fp8.safetensors", "fp8_e4m3fn",      6, "9B fp8 (当前)"),
+    ("flux2.0/flux-2-klein-9b-fp8.safetensors", "fp8_e4m3fn_fast", 6, "9B fp8-fast"),
+    ("flux2.0/flux-2-klein-9b-fp8.safetensors", "fp8_e4m3fn_fast", 4, "9B fp8-fast s4"),
+    ("flux-2-klein-4b-fp8.safetensors",         "fp8_e4m3fn",      6, "4B fp8"),
+    ("flux-2-klein-4b-fp8.safetensors",         "fp8_e4m3fn_fast", 6, "4B fp8-fast"),
+    ("flux-2-klein-4b-fp8.safetensors",         "fp8_e4m3fn_fast", 4, "4B fp8-fast s4"),
+]
+
+fname = prep_and_upload()
+print("input uploaded:", fname)
+print(f"{'配置':<22}{'warmup':>10}{'run1':>10}{'run2':>10}{'best':>10}")
+for model, dtype, steps, label in CONFIGS:
+    times = []
+    for i in range(3):  # 1 warmup + 2 measured
+        try:
+            t = run_once(fname, model, dtype, steps)
+        except Exception as e:
+            t = float('nan'); print("ERR", label, e)
+        times.append(t)
+    best = min(times[1:])
+    print(f"{label:<22}{times[0]:>9.2f}s{times[1]:>9.2f}s{times[2]:>9.2f}s{best:>9.2f}s")

local_test/bench2.py

@@ -0,0 +1,69 @@
+#!/usr/bin/env python3
+"""Steps sweep + resolution test on the working 9B fp8-fast config."""
+import io, time
+import requests
+import numpy as np
+from PIL import Image, ImageFilter
+import app as A
+
+COMFY = "http://127.0.0.1:8188"
+MODEL = "flux2.0/flux-2-klein-9b-fp8.safetensors"
+DTYPE = "fp8_e4m3fn_fast"
+
+
+def upload(scale=1.0):
+    image = Image.open("用来重绘.jpg").convert("RGB")
+    mask_img = Image.open("用来重绘.png").convert("RGBA")
+    if scale != 1.0:
+        w, h = image.size
+        w, h = int(w * scale) // 8 * 8, int(h * scale) // 8 * 8
+        image = image.resize((w, h), Image.LANCZOS)
+    mask_data = np.max(np.array(mask_img), axis=2)
+    m = Image.fromarray(mask_data, mode="L")
+    if m.size != image.size:
+        m = m.resize(image.size, Image.LANCZOS)
+    m = m.filter(ImageFilter.GaussianBlur(radius=4))
+    alpha = Image.eval(m, lambda x: 255 - x)
+    r, g, b = image.split()
+    rgba = Image.merge("RGBA", (r, g, b, alpha))
+    buf = io.BytesIO(); rgba.save(buf, format="PNG"); buf.seek(0)
+    up = requests.post(f"{COMFY}/upload/image",
+                       files={"image": ("hair_input.png", buf, "image/png")}).json()
+    return up["name"], image.size
+
+
+def run(fname, steps):
+    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
+    pid = requests.post(f"{COMFY}/prompt", json={"prompt": wf}).json()["prompt_id"]
+    dl = time.time() + 120
+    while time.time() < dl:
+        time.sleep(0.1)
+        h = requests.get(f"{COMFY}/history/{pid}").json()
+        if pid in h and "17" in h[pid].get("outputs", {}):
+            ts = {m[0]: m[1].get("timestamp") for m in h[pid]["status"]["messages"]}
+            return (ts["execution_success"] - ts["execution_start"]) / 1000.0
+    return float("nan")
+
+
+print("=== 步数扫描 (9B fp8-fast, 原分辨率 1024x775) ===", flush=True)
+fname, sz = upload(1.0)
+run(fname, 6)  # warmup
+res = {}
+for s in [2, 3, 4, 6, 8]:
+    t = min(run(fname, s), run(fname, s))
+    res[s] = t
+    print(f"  steps={s}: {t:.2f}s", flush=True)
+# derive per-step cost & fixed overhead via two points
+per = (res[8] - res[2]) / 6
+fixed = res[2] - per * 2
+print(f"  -> 每步 ~{per:.3f}s, 固定开销(VAE/编码/colormatch/加载) ~{fixed:.2f}s", flush=True)
+
+print("\n=== 分辨率影响 (steps=4) ===", flush=True)
+for scale in [1.0, 0.75, 0.6]:
+    fn, s2 = upload(scale)
+    run(fn, 4)  # warmup
+    t = min(run(fn, 4), run(fn, 4))
+    print(f"  {s2[0]}x{s2[1]} ({s2[0]*s2[1]/1e6:.2f}MP): {t:.2f}s", flush=True)

local_test/bench_quality.py

@@ -0,0 +1,69 @@
+#!/usr/bin/env python3
+"""Generate result images at different step counts for quality comparison."""
+import io, time
+import requests
+import numpy as np
+from PIL import Image, ImageFilter
+import app as A
+
+COMFY = "http://127.0.0.1:8188"
+MODEL = "flux2.0/flux-2-klein-9b-fp8.safetensors"
+DTYPE = "fp8_e4m3fn_fast"
+OUT = "output"
+
+image = Image.open("用来重绘.jpg").convert("RGB")
+mask_img = Image.open("用来重绘.png").convert("RGBA")
+mask_data = np.max(np.array(mask_img), axis=2)
+m = Image.fromarray(mask_data, mode="L")
+if m.size != image.size:
+    m = m.resize(image.size, Image.LANCZOS)
+m = m.filter(ImageFilter.GaussianBlur(radius=4))
+alpha = Image.eval(m, lambda x: 255 - x)
+r, g, b = image.split()
+rgba = Image.merge("RGBA", (r, g, b, alpha))
+buf = io.BytesIO(); rgba.save(buf, format="PNG"); buf.seek(0)
+fname = requests.post(f"{COMFY}/upload/image",
+                      files={"image": ("hair_input.png", buf, "image/png")}).json()["name"]
+
+# fixed seed for fair comparison
+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", seed=SEED)
+    wf["16"]["inputs"]["unet_name"] = MODEL
+    wf["16"]["inputs"]["weight_dtype"] = DTYPE
+    wf["1"]["inputs"]["steps"] = steps
+    pid = requests.post(f"{COMFY}/prompt", json={"prompt": wf}).json()["prompt_id"]
+    t0 = time.time()
+    while True:
+        time.sleep(0.1)
+        h = requests.get(f"{COMFY}/history/{pid}").json()
+        if pid in h and "17" in h[pid].get("outputs", {}):
+            ts = {mm[0]: mm[1].get("timestamp") for mm in h[pid]["status"]["messages"]}
+            dur = (ts["execution_success"] - ts["execution_start"]) / 1000.0
+            info = h[pid]["outputs"]["17"]["images"][0]
+            data = requests.get(f"{COMFY}/view", params={
+                "filename": info["filename"], "subfolder": info.get("subfolder", ""),
+                "type": info.get("type", "output")}).content
+            im = Image.open(io.BytesIO(data)).convert("RGB")
+            imgs.append(im)
+            labels.append(f"steps={steps}  {dur:.2f}s")
+            print(f"steps={steps}: {dur:.2f}s", flush=True)
+            break
+
+# build side-by-side contact sheet
+from PIL import ImageDraw
+h0 = imgs[0].height
+w0 = imgs[0].width
+pad = 10
+bar = 28
+sheet = Image.new("RGB", (w0 * len(imgs) + pad * (len(imgs) + 1),
+                          h0 + bar + pad * 2), (26, 26, 46))
+d = ImageDraw.Draw(sheet)
+for i, (im, lb) in enumerate(zip(imgs, labels)):
+    x = pad + i * (w0 + pad)
+    sheet.paste(im, (x, bar + pad))
+    d.text((x + 4, 6), lb, fill=(233, 69, 96))
+sheet.save(f"{OUT}/compare_steps.png")
+print("saved:", f"{OUT}/compare_steps.png", flush=True)

local_test/index.html

@@ -0,0 +1,192 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>发型补全工具</title>
+<style>
+* { margin: 0; padding: 0; box-sizing: border-box; }
+body { font-family: -apple-system, sans-serif; background: #1a1a2e; color: #eee; min-height: 100vh; padding: 20px; }
+h1 { text-align: center; margin-bottom: 20px; color: #e94560; font-size: 28px; }
+.container { max-width: 1400px; margin: 0 auto; display: grid; grid-template-columns: 1fr 1fr; gap: 24px; }
+.panel { background: #16213e; border-radius: 12px; padding: 20px; }
+.panel h2 { margin-bottom: 16px; font-size: 18px; color: #e94560; }
+.controls { display: flex; flex-wrap: wrap; gap: 12px; margin-bottom: 16px; align-items: center; }
+.controls label { font-size: 14px; color: #aaa; }
+input[type="file"] { color: #ddd; }
+input[type="text"] { flex: 1; min-width: 200px; padding: 8px 12px; border-radius: 6px; border: 1px solid #444; background: #0f3460; color: #eee; font-size: 14px; }
+button { padding: 10px 24px; border: none; border-radius: 6px; cursor: pointer; font-size: 14px; font-weight: 600; transition: all 0.2s; }
+.btn-upload { background: #0f3460; color: #eee; border: 1px solid #e94560; }
+.btn-upload:hover { background: #1a4a7a; }
+.btn-generate { background: #e94560; color: #fff; font-size: 16px; padding: 12px 36px; }
+.btn-generate:hover { background: #c73650; }
+.btn-generate:disabled { background: #555; cursor: not-allowed; }
+.image-wrapper { display: flex; gap: 12px; flex-wrap: wrap; border: 2px dashed #444; border-radius: 8px; padding: 12px; min-height: 300px; background: #0f3460; }
+.image-item { flex: 1; min-width: 200px; }
+.image-item img { max-width: 100%; border-radius: 6px; }
+.image-item h4 { font-size: 12px; color: #aaa; margin-bottom: 6px; }
+.placeholder { color: #666; font-size: 16px; text-align: center; padding: 60px 20px; width: 100%; }
+.result-area { display: flex; gap: 16px; flex-wrap: wrap; }
+.result-area img { max-width: 100%; border-radius: 8px; }
+.result-item { flex: 1; min-width: 250px; }
+.result-item h3 { font-size: 14px; color: #aaa; margin-bottom: 8px; text-align: center; }
+.loading { text-align: center; padding: 40px; color: #e94560; font-size: 18px; }
+.loading .spinner { display: inline-block; width: 40px; height: 40px; border: 4px solid #333; border-top-color: #e94560; border-radius: 50%; animation: spin 1s linear infinite; margin-bottom: 12px; }
+@keyframes spin { to { transform: rotate(360deg); } }
+.error { color: #ff6b6b; padding: 16px; background: #2a1a1a; border-radius: 8px; margin-top: 12px; }
+</style>
+</head>
+<body>
+<h1>💇 发型补全工具</h1>
+<div class="container">
+    <!-- Left: Input -->
+    <div class="panel">
+        <h2>1. 上传图片 & 遮罩</h2>
+        <div class="controls">
+            <label>人物图片:</label>
+            <input type="file" id="imageInput" accept="image/*" class="btn-upload">
+        </div>
+        <div class="controls">
+            <label>遮罩图片:</label>
+            <input type="file" id="maskInput" accept="image/*" class="btn-upload">
+        </div>
+        <div class="image-wrapper" id="imageWrapper">
+            <div class="placeholder" id="placeholder">请上传人物图片和遮罩图片</div>
+        </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">
+        </div>
+        <div style="text-align:center; margin-top:16px">
+            <button class="btn-generate" id="generateBtn" disabled>🚀 生成</button>
+        </div>
+    </div>
+
+    <!-- Right: Result -->
+    <div class="panel">
+        <h2>2. 对比结果</h2>
+        <div id="resultArea">
+            <div class="placeholder">生成结果将显示在这里</div>
+        </div>
+    </div>
+</div>
+
+<script>
+const imageInput = document.getElementById('imageInput');
+const maskInput = document.getElementById('maskInput');
+const imageWrapper = document.getElementById('imageWrapper');
+const placeholder = document.getElementById('placeholder');
+const promptInput = document.getElementById('promptInput');
+const generateBtn = document.getElementById('generateBtn');
+const resultArea = document.getElementById('resultArea');
+
+let originalImage = null;
+let maskImage = null;
+
+imageInput.addEventListener('change', (e) => {
+    const file = e.target.files[0];
+    if (!file) return;
+    const reader = new FileReader();
+    reader.onload = (ev) => {
+        const img = new Image();
+        img.onload = () => {
+            originalImage = { img: img, file: file };
+            updatePreview();
+            checkReady();
+        };
+        img.src = ev.target.result;
+    };
+    reader.readAsDataURL(file);
+});
+
+maskInput.addEventListener('change', (e) => {
+    const file = e.target.files[0];
+    if (!file) return;
+    const reader = new FileReader();
+    reader.onload = (ev) => {
+        const img = new Image();
+        img.onload = () => {
+            maskImage = { img: img, file: file };
+            updatePreview();
+            checkReady();
+        };
+        img.src = ev.target.result;
+    };
+    reader.readAsDataURL(file);
+});
+
+function updatePreview() {
+    placeholder.style.display = 'none';
+    let html = '';
+    if (originalImage) {
+        html += `<div class="image-item"><h4>人物图片</h4><img src="${originalImage.img.src}" alt="原图"></div>`;
+    }
+    if (maskImage) {
+        html += `<div class="image-item"><h4>遮罩图片</h4><img src="${maskImage.img.src}" alt="遮罩"></div>`;
+    }
+    imageWrapper.innerHTML = html;
+}
+
+function checkReady() {
+    generateBtn.disabled = !(originalImage && maskImage);
+}
+
+generateBtn.addEventListener('click', async () => {
+    if (!originalImage || !maskImage) return;
+
+    generateBtn.disabled = true;
+    generateBtn.textContent = '⏳ 生成中...';
+
+    const startTime = performance.now();
+    resultArea.innerHTML = '<div class="loading"><div class="spinner"></div><br>正在调用 ComfyUI 生成，请耐心等待...<div id="liveTimer" style="margin-top:8px;font-size:15px;color:#aaa">已用时 0.0s</div></div>';
+    const liveTimer = document.getElementById('liveTimer');
+    const timerId = setInterval(() => {
+        if (liveTimer) liveTimer.textContent = '已用时 ' + ((performance.now() - startTime) / 1000).toFixed(1) + 's';
+    }, 100);
+
+    try {
+        const formData = new FormData();
+        formData.append('image', originalImage.file, 'original.' + originalImage.file.name.split('.').pop());
+        formData.append('mask', maskImage.file, 'mask.' + maskImage.file.name.split('.').pop());
+        formData.append('prompt', promptInput.value);
+
+        const resp = await fetch('/api/generate', { method: 'POST', body: formData });
+        if (!resp.ok) {
+            const err = await resp.json();
+            throw new Error(err.error || 'Generation failed');
+        }
+
+        const resultBlob = await resp.blob();
+        const resultUrl = URL.createObjectURL(resultBlob);
+        const elapsed = ((performance.now() - startTime) / 1000).toFixed(1);
+        // 服务端纯推理耗时（若返回该响应头）
+        const serverTime = resp.headers.get('X-Generate-Time');
+        const serverInfo = serverTime ? `，服务端推理 ${parseFloat(serverTime).toFixed(1)}s` : '';
+
+        resultArea.innerHTML = `
+            <div style="text-align:center;margin-bottom:12px;color:#4ade80;font-size:16px;font-weight:600">
+                ⏱️ 本次重绘耗时 ${elapsed}s${serverInfo}
+            </div>
+            <div class="result-area">
+                <div class="result-item">
+                    <h3>原图</h3>
+                    <img src="${originalImage.img.src}" alt="原图">
+                </div>
+                <div class="result-item">
+                    <h3>生成结果</h3>
+                    <img src="${resultUrl}" alt="生成结果">
+                </div>
+            </div>
+        `;
+    } catch (err) {
+        const elapsed = ((performance.now() - startTime) / 1000).toFixed(1);
+        resultArea.innerHTML = `<div class="error">❌ ${err.message}<br><span style="color:#aaa;font-size:13px">（耗时 ${elapsed}s）</span></div>`;
+    } finally {
+        clearInterval(timerId);
+        generateBtn.disabled = false;
+        generateBtn.textContent = '🚀 生成';
+    }
+});
+</script>
+</body>
+</html>

local_test/start.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+# 启动发型补全服务
+
+cd "$(dirname "$0")"
+
+PID_FILE="hair_service.pid"
+LOG_FILE="hair_service.log"
+
+# 检查是否已在运行
+if [ -f "$PID_FILE" ]; then
+    PID=$(cat "$PID_FILE")
+    if kill -0 "$PID" 2>/dev/null; then
+        echo "服务已在运行 (PID: $PID)"
+        exit 0
+    else
+        echo "清理无效的 PID 文件..."
+        rm "$PID_FILE"
+    fi
+fi
+
+# 检查 ComfyUI 是否运行
+if ! curl -s -o /dev/null -w "%{http_code}" http://127.0.0.1:8188/ >/dev/null 2>&1; then
+    echo "警告: ComfyUI 未运行 (http://127.0.0.1:8188)"
+    echo "请先启动 ComfyUI: python /home/ubuntu/ComfyUI/main.py --listen"
+fi
+
+# 启动服务
+echo "启动发型补全服务..."
+/home/ubuntu/ComfyUI/venv/bin/python app.py >> "$LOG_FILE" 2>&1 &
+PID=$!
+echo "$PID" > "$PID_FILE"
+
+# 等待启动
+sleep 2
+if curl -s -o /dev/null -w "%{http_code}" http://127.0.0.1:8899/ | grep -q "200"; then
+    echo "服务启动成功!"
+    echo "本机: http://127.0.0.1:8899"
+    # 打印局域网 IP，方便其他机器访问（app.py 已绑定 0.0.0.0）
+    LAN_IPS=$(hostname -I 2>/dev/null | tr ' ' '\n' | grep -v '^$' || true)
+    if [ -n "$LAN_IPS" ]; then
+        echo "外网/局域网访问:"
+        for ip in $LAN_IPS; do
+            echo "  http://${ip}:8899"
+        done
+    fi
+    echo "PID: $PID"
+    echo "日志: $LOG_FILE"
+else
+    echo "服务启动失败，请检查日志: $LOG_FILE"
+    rm "$PID_FILE"
+    exit 1
+fi

local_test/stop.sh

@@ -0,0 +1,37 @@
+#!/bin/bash
+# 停止发型补全服务
+
+cd "$(dirname "$0")"
+
+PID_FILE="hair_service.pid"
+
+if [ ! -f "$PID_FILE" ]; then
+    echo "服务未运行"
+    exit 0
+fi
+
+PID=$(cat "$PID_FILE")
+
+if kill -0 "$PID" 2>/dev/null; then
+    echo "正在停止服务 (PID: $PID)..."
+    kill "$PID"
+    
+    # 等待进程退出
+    for i in {1..10}; do
+        if ! kill -0 "$PID" 2>/dev/null; then
+            echo "服务已停止"
+            rm "$PID_FILE"
+            exit 0
+        fi
+        sleep 1
+    done
+    
+    # 强制终止
+    echo "强制终止进程..."
+    kill -9 "$PID"
+    rm "$PID_FILE"
+    echo "服务已停止"
+else
+    echo "进程已不存在，清理 PID 文件..."
+    rm "$PID_FILE"
+fi

local_test/test_api.py

@@ -0,0 +1,42 @@
+#!/usr/bin/env python3
+"""Test script: send image+mask to the local service and save result."""
+import requests
+import sys
+import os
+
+SERVICE_URL = "http://127.0.0.1:8899"
+IMAGE_PATH = "/home/ubuntu/hair/local_test/用来重绘.jpg"
+MASK_PATH = "/home/ubuntu/hair/local_test/用来重绘.png"
+OUTPUT_DIR = "/home/ubuntu/hair/local_test/output"
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+print(f"Sending image: {IMAGE_PATH}")
+print(f"Sending mask:  {MASK_PATH}")
+
+with open(IMAGE_PATH, "rb") as f:
+    img_data = f.read()
+with open(MASK_PATH, "rb") as f:
+    mask_data = f.read()
+
+resp = requests.post(
+    f"{SERVICE_URL}/api/generate",
+    files={
+        "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"},
+    timeout=600,
+)
+
+print(f"Status: {resp.status_code}")
+print(f"Content-Type: {resp.headers.get('Content-Type')}")
+
+if resp.status_code == 200 and "image" in resp.headers.get("Content-Type", ""):
+    out_path = os.path.join(OUTPUT_DIR, "result.png")
+    with open(out_path, "wb") as f:
+        f.write(resp.content)
+    print(f"SUCCESS! Result saved to: {out_path}")
+else:
+    print(f"FAILED! Response: {resp.text[:2000]}")
+    sys.exit(1)

scripts/batch_grow_v2.py

@@ -1,156 +0,0 @@
-"""批量调用接口12（/api/v1/hairline/grow_v2）生成对比素材。
-
-20 张女生照片 × 5 种发际线发型 × {高清, 非高清} = 200 张输出。
-并发 4，失败的跳过并记录原因。结果图落盘到 static/report_hairline_v2/img/，
-元数据落盘 static/report_hairline_v2/results.json，供生成报告用。
-
-用法： python scripts/batch_grow_v2.py
-"""
-import base64
-import json
-import os
-import time
-from concurrent.futures import ThreadPoolExecutor, as_completed
-
-import httpx
-
-API = "http://127.0.0.1:8187/api/v1/hairline/grow_v2"
-TOKEN = "dev-shared-secret-2026"
-CONCURRENCY = 2
-
-INPUT_DIR = "/home/xsl/hair/image/test"
-OUT_DIR = "/home/xsl/hair/static/report_hairline_v2"
-IMG_DIR = os.path.join(OUT_DIR, "img")
-ORIG_DIR = os.path.join(OUT_DIR, "orig")
-
-# 5 种发际线发型（= change_hair hair_id）
-HAIRSTYLES = [
-    ("chang_zhixian", "直线"),
-    ("chang_tuoyuan", "椭圆"),
-    ("chang_bolang", "波浪"),
-    ("chang_xinxing", "心形"),
-    ("chang_huaban", "花瓣"),
-]
-HR_OPTIONS = [(True, "hr"), (False, "nohr")]
-
-
-def list_inputs():
-    files = sorted(f for f in os.listdir(INPUT_DIR) if f.lower().endswith((".jpg", ".png")))
-    return files
-
-
-def one_call(stem, face_file, hair_id, hair_cn, is_hr, hr_tag):
-    """调用一次接口，落盘结果图。返回结果 dict。"""
-    src = os.path.join(INPUT_DIR, face_file)
-    out_name = f"{stem}__{hair_id}__{hr_tag}.jpg"
-    out_path = os.path.join(IMG_DIR, out_name)
-    # 断点续跑：已存在的图直接跳过，不重复调用
-    if os.path.exists(out_path) and os.path.getsize(out_path) > 1024:
-        return {
-            "stem": stem, "face_file": face_file, "hair_id": hair_id, "hair_cn": hair_cn,
-            "is_hr": is_hr, "hr_tag": hr_tag, "ok": True,
-            "out": f"img/{out_name}", "size": None,
-            "ms": 0, "error": None, "skipped": True,
-        }
-    t0 = time.time()
-    try:
-        with open(src, "rb") as fh:
-            files = {"image_file": (face_file, fh.read(), "image/jpeg")}
-        data = {"hairline_id": hair_id, "is_hr": str(is_hr).lower()}
-        with httpx.Client(timeout=180.0) as c:
-            resp = c.post(API, headers={"X-Internal-Token": TOKEN}, files=files, data=data)
-        j = resp.json()
-        if j.get("code") != 0 or not j.get("data"):
-            raise RuntimeError(f"code={j.get('code')} msg={j.get('message')}")
-        b64 = j["data"]["final_base64"].split(",", 1)[1]
-        raw = base64.b64decode(b64)
-        with open(out_path, "wb") as fh:
-            fh.write(raw)
-        return {
-            "stem": stem, "face_file": face_file, "hair_id": hair_id, "hair_cn": hair_cn,
-            "is_hr": is_hr, "hr_tag": hr_tag, "ok": True,
-            "out": f"img/{out_name}", "size": j["data"].get("image_size"),
-            "ms": int((time.time() - t0) * 1000), "error": None,
-        }
-    except Exception as ex:  # noqa: BLE001
-        return {
-            "stem": stem, "face_file": face_file, "hair_id": hair_id, "hair_cn": hair_cn,
-            "is_hr": is_hr, "hr_tag": hr_tag, "ok": False,
-            "out": None, "size": None, "ms": int((time.time() - t0) * 1000),
-            "error": str(ex)[:200],
-        }
-
-
-def main():
-    os.makedirs(IMG_DIR, exist_ok=True)
-    os.makedirs(ORIG_DIR, exist_ok=True)
-    faces = list_inputs()
-    print(f"输入 {len(faces)} 张脸 × {len(HAIRSTYLES)} 发型 × {len(HR_OPTIONS)} = "
-          f"{len(faces)*len(HAIRSTYLES)*len(HR_OPTIONS)} 次调用，并发 {CONCURRENCY}")
-
-    # 1. 先把原图拷一份到 orig/（报告要用）
-    import shutil
-    for f in faces:
-        stem = os.path.splitext(f)[0]
-        dst = os.path.join(ORIG_DIR, f"{stem}.jpg")
-        if not os.path.exists(dst):
-            shutil.copy2(os.path.join(INPUT_DIR, f), dst)
-
-    # 2. 构造全部任务
-    tasks = []
-    for f in faces:
-        stem = os.path.splitext(f)[0]
-        for hair_id, hair_cn in HAIRSTYLES:
-            for is_hr, hr_tag in HR_OPTIONS:
-                tasks.append((stem, f, hair_id, hair_cn, is_hr, hr_tag))
-
-    results = []
-    done = 0
-    total = len(tasks)
-    t_start = time.time()
-    with ThreadPoolExecutor(max_workers=CONCURRENCY) as ex:
-        futs = {ex.submit(one_call, *t): t for t in tasks}
-        for fut in as_completed(futs):
-            r = fut.result()
-            results.append(r)
-            done += 1
-            status = "OK " if r["ok"] else "FAIL"
-            if r.get("skipped"):
-                print(f"[{done}/{total}] SKIP {r['stem']} {r['hair_cn']} {r['hr_tag']}")
-            elif r["ok"]:
-                print(f"[{done}/{total}] {status} {r['stem']} {r['hair_cn']} {r['hr_tag']} "
-                      f"({r['ms']}ms)", flush=True)
-            else:
-                print(f"[{done}/{total}] {status} {r['stem']} {r['hair_cn']} {r['hr_tag']} "
-                      f"-> {r['error']}")
-
-    elapsed = time.time() - t_start
-    ok = sum(1 for r in results if r["ok"])
-    fail = len(results) - ok
-    # 按稳定顺序排序，报告好看
-    order = {s: i for i, s in enumerate(HAIRSTYLES)}
-    hr_order = {True: 0, False: 1}
-    face_order = {os.path.splitext(f)[0]: i for i, f in enumerate(faces)}
-    results.sort(key=lambda r: (face_order.get(r["stem"], 0),
-                                order.get((r["hair_id"], r["hair_cn"]), 0),
-                                hr_order.get(r["is_hr"], 0)))
-
-    meta = {
-        "total": total, "ok": ok, "fail": fail,
-        "elapsed_sec": round(elapsed, 1), "concurrency": CONCURRENCY,
-        "hairstyles": [{"id": h, "cn": c} for h, c in HAIRSTYLES],
-        "hr_options": [{"is_hr": True, "tag": "hr"}, {"is_hr": False, "tag": "nohr"}],
-        "faces": [os.path.splitext(f)[0] for f in faces],
-        "generated_at": time.strftime("%Y-%m-%d %H:%M:%S"),
-    }
-    out = {"meta": meta, "results": results}
-    with open(os.path.join(OUT_DIR, "results.json"), "w", encoding="utf-8") as fh:
-        json.dump(out, fh, ensure_ascii=False, indent=2)
-
-    print(f"\n完成：{ok}/{total} 成功，{fail} 失败，耗时 {elapsed:.1f}s")
-    print(f"结果图 -> {IMG_DIR}")
-    print(f"元数据 -> {OUT_DIR}/results.json")
-
-
-if __name__ == "__main__":
-    main()

scripts/gen_report_hairline_v2.py

@@ -1,229 +0,0 @@
-"""根据 results.json 生成发际线生发对比报告 HTML。
-
-报告布局（原图 vs 结果对比）：
-- 顶部：总览统计（成功/失败数、耗时、参数）
-- 按脸分组，每张脸一个区块：
-    - 左：原图
-    - 右：5 发型 × {高清, 非高清} 网格（共 10 张），失败的格子标注原因
-- 失败用例汇总表
-
-输出：static/report_hairline_v2/index.html
-"""
-import html
-import json
-import os
-from urllib.parse import quote
-
-OUT_DIR = "/home/xsl/hair/static/report_hairline_v2"
-RESULTS = os.path.join(OUT_DIR, "results.json")
-TARGET = os.path.join(OUT_DIR, "index.html")
-
-
-def url(path):
-    """把相对路径里的中文做 URL 编码，分隔符 '/' 保留。
-    Starlette StaticFiles 对未编码中文路径返回 400，编码后所有浏览器稳定可加载。
-    """
-    return "/".join(quote(seg) for seg in path.split("/"))
-
-
-def main():
-    with open(RESULTS, encoding="utf-8") as fh:
-        data = json.load(fh)
-    meta = data["meta"]
-    results = data["results"]
-
-    hairstyles = meta["hairstyles"]           # [{id, cn}]
-    faces = meta["faces"]                      # [stem, ...]
-    hr_opts = meta["hr_options"]               # [{is_hr, tag}]
-
-    # 索引：(stem, hair_id, hr_tag) -> result
-    idx = {}
-    for r in results:
-        idx[(r["stem"], r["hair_id"], r["hr_tag"])] = r
-
-    # 统计
-    ok = sum(1 for r in results if r["ok"])
-    fail = len(results) - ok
-
-    parts = []
-    parts.append(f"""<!DOCTYPE html>
-<html lang="zh-CN">
-<head>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1">
-<title>发际线生发对比报告 · 接口12 grow_v2</title>
-<style>
-  :root {{
-    --bg:#0f1115; --card:#1a1d24; --border:#2a2f3a; --txt:#e6e6e6;
-    --muted:#8a93a3; --accent:#6ea8fe; --ok:#4ade80; --fail:#f87171;
-  }}
-  * {{ box-sizing:border-box; }}
-  body {{ margin:0; background:var(--bg); color:var(--txt);
-         font-family:-apple-system,"Segoe UI","PingFang SC","Microsoft YaHei",sans-serif;
-         line-height:1.5; }}
-  header {{ padding:24px 32px; border-bottom:1px solid var(--border); }}
-  header h1 {{ margin:0 0 8px; font-size:22px; }}
-  header .sub {{ color:var(--muted); font-size:14px; }}
-  .stats {{ display:flex; gap:16px; flex-wrap:wrap; margin-top:16px; }}
-  .stat {{ background:var(--card); border:1px solid var(--border); border-radius:8px;
-           padding:12px 16px; min-width:120px; }}
-  .stat .n {{ font-size:24px; font-weight:600; }}
-  .stat .l {{ font-size:12px; color:var(--muted); }}
-  .stat.ok .n {{ color:var(--ok); }}
-  .stat.fail .n {{ color:var(--fail); }}
-  main {{ padding:24px 32px; }}
-  .face-block {{ background:var(--card); border:1px solid var(--border);
-                border-radius:12px; padding:20px; margin-bottom:24px; }}
-  .face-head {{ display:flex; align-items:center; gap:12px; margin-bottom:16px; }}
-  .face-head h2 {{ margin:0; font-size:18px; }}
-  .face-head .orig-thumb {{ width:64px; height:64px; object-fit:cover;
-                            border-radius:8px; border:1px solid var(--border); }}
-  .grid {{ display:grid; grid-template-columns:140px repeat({len(hairstyles)*2}, 1fr);
-           gap:8px; align-items:start; }}
-  .col-hdr {{ font-size:12px; color:var(--muted); text-align:center; padding:6px 4px;
-              border-bottom:1px solid var(--border); }}
-  .row-lbl {{ font-size:13px; color:var(--accent); padding:8px 4px;
-              border-right:1px solid var(--border); }}
-  .cell {{ position:relative; }}
-  .cell img {{ width:100%; border-radius:6px; display:block;
-               border:1px solid var(--border); }}
-  .cell .cap {{ font-size:11px; color:var(--muted); margin-top:4px; text-align:center; }}
-  .cell.fail .failbox {{ aspect-ratio:3/4; background:#2a1518; border:1px solid #5c2a30;
-                         border-radius:6px; display:flex; align-items:center;
-                         justify-content:center; padding:8px; text-align:center;
-                         font-size:11px; color:var(--fail); }}
-  .tag {{ display:inline-block; font-size:11px; padding:1px 6px; border-radius:4px;
-          background:#243044; color:var(--accent); margin-left:6px; }}
-  .tag.no {{ background:#3a3526; color:#e0c97a; }}
-  .legend {{ font-size:13px; color:var(--muted); margin-bottom:16px; }}
-  .fail-table {{ width:100%; border-collapse:collapse; font-size:13px; margin-top:8px; }}
-  .fail-table th, .fail-table td {{ border:1px solid var(--border); padding:6px 10px; text-align:left; }}
-  .fail-table th {{ background:#1f232c; color:var(--muted); }}
-  .anchor {{ display:block; height:0; overflow:hidden; }}
-  footer {{ padding:24px 32px; color:var(--muted); font-size:12px; border-top:1px solid var(--border); }}
-  /* 点击放大 */
-  img.zoomable {{ cursor:zoom-in; transition:opacity .12s; }}
-  img.zoomable:hover {{ opacity:.85; }}
-  #lightbox {{ position:fixed; inset:0; background:rgba(0,0,0,.92); display:none;
-               align-items:center; justify-content:center; z-index:9999; padding:24px;
-               cursor:zoom-out; }}
-  #lightbox.open {{ display:flex; }}
-  #lightbox img {{ max-width:100%; max-height:100%; object-fit:contain;
-                  border-radius:8px; box-shadow:0 8px 40px rgba(0,0,0,.6); }}
-  #lightbox .lb-cap {{ position:absolute; bottom:16px; left:0; right:0; text-align:center;
-                       color:var(--muted); font-size:13px; }}
-</style>
-</head>
-<body>
-<header>
-  <h1>发际线生发对比报告 <span class="tag">接口12 · grow_v2</span></h1>
-  <div class="sub">固定参数：blend_method=multiband · mb_levels=5 · erode_cm=0.6 · 生成后端=swaphair</div>
-  <div class="sub">生成时间：{html.escape(meta['generated_at'])} · 并发 {meta['concurrency']} · 耗时 {meta['elapsed_sec']}s</div>
-  <div class="stats">
-    <div class="stat"><div class="n">{meta['total']}</div><div class="l">总调用</div></div>
-    <div class="stat ok"><div class="n">{ok}</div><div class="l">成功</div></div>
-    <div class="stat fail"><div class="n">{fail}</div><div class="l">失败</div></div>
-    <div class="stat"><div class="n">{len(faces)}</div><div class="l">人脸数</div></div>
-    <div class="stat"><div class="n">{len(hairstyles)}</div><div class="l">发型数</div></div>
-  </div>
-</header>
-<main>
-""")
-
-    # 图例
-    parts.append('<div class="legend">每张脸横向为 5 种发际线发型，每种发型分 '
-                 '<span class="tag">高清 is_hr=true</span>'
-                 '<span class="tag no">非高清 is_hr=false</span> 两列。</div>')
-
-    # 每张脸一个区块
-    for stem in faces:
-        orig_rel = f"orig/{stem}.jpg"
-        parts.append('<div class="face-block">')
-        parts.append(f'  <div class="face-head">'
-                     f'<img class="orig-thumb zoomable" src="{url(orig_rel)}" '
-                     f'data-full="{url(orig_rel)}" alt="原图 {html.escape(stem)}">'
-                     f'<h2>{html.escape(stem)}</h2>'
-                     f'</div>')
-        # 网格表头
-        parts.append('<div class="grid">')
-        parts.append('<div class="col-hdr"></div>')
-        for h in hairstyles:
-            parts.append(f'<div class="col-hdr" style="grid-column:span 2;">{html.escape(h["cn"])}<br><span style="opacity:.6">{html.escape(h["id"])}</span></div>')
-        # 行：原图占满左侧第一列的高度由第一行承载；每行 = 一个 hr 选项
-        for hro in hr_opts:
-            hr_tag = hro["tag"]
-            is_hr = hro["is_hr"]
-            tag_cls = "" if is_hr else " no"
-            tag_txt = "高清" if is_hr else "非高清"
-            parts.append(f'<div class="row-lbl">{tag_txt}<span class="tag{tag_cls}">{hr_tag}</span></div>')
-            for h in hairstyles:
-                r = idx.get((stem, h["id"], hr_tag))
-                if r and r["ok"]:
-                    ms = r["ms"]
-                    cap = f"{html.escape(h['cn'])} · {tag_txt} · {ms}ms"
-                    parts.append(
-                        f'<div class="cell"><img class="zoomable" src="{url(r["out"])}" '
-                        f'data-full="{url(r["out"])}" loading="lazy" '
-                        f'alt="{html.escape(stem)} {html.escape(h["cn"])} {hr_tag}">'
-                        f'<div class="cap">{ms}ms</div></div>')
-                else:
-                    err = (r or {}).get("error", "未执行")
-                    parts.append(
-                        f'<div class="cell fail"><div class="failbox">{html.escape(err)}</div></div>')
-        parts.append('</div>')  # grid
-        parts.append('</div>')  # face-block
-
-    # 失败汇总
-    fails = [r for r in results if not r["ok"]]
-    if fails:
-        parts.append('<div class="face-block">')
-        parts.append(f'<h2>失败用例（{len(fails)}）</h2>')
-        parts.append('<table class="fail-table"><thead><tr>'
-                     '<th>人脸</th><th>发型</th><th>高清</th><th>耗时(ms)</th><th>原因</th>'
-                     '</tr></thead><tbody>')
-        for r in fails:
-            parts.append(
-                f'<tr><td>{html.escape(r["stem"])}</td>'
-                f'<td>{html.escape(r["hair_cn"])}</td>'
-                f'<td>{r["hr_tag"]}</td>'
-                f'<td>{r["ms"]}</td>'
-                f'<td>{html.escape(r["error"] or "")}</td></tr>')
-        parts.append('</tbody></table></div>')
-
-    parts.append(f"""
-<footer>
-  接口：<code>POST /api/v1/hairline/grow_v2</code> ·
-  固定 multiband / mb_levels=5 / erode_cm=0.6 ·
-  数据源 results.json · 点击任意图片可放大查看
-</footer>
-</main>
-<div id="lightbox"><img><div class="lb-cap"></div></div>
-<script>
-(function(){{
-  var lb=document.getElementById('lightbox'),lbImg=lb.querySelector('img'),
-      lbCap=lb.querySelector('.lb-cap');
-  function open(src,cap){{
-    lbImg.src=src; lbCap.textContent=cap||''; lb.classList.add('open');
-  }}
-  function close(){{ lb.classList.remove('open'); lbImg.src=''; }}
-  document.addEventListener('click',function(e){{
-    var t=e.target.closest('img.zoomable');
-    if(t){{ open(t.dataset.full||t.src, t.alt||''); }}
-    else if(e.target===lb||e.target===lbImg){{ close(); }}
-  }});
-  document.addEventListener('keydown',function(e){{
-    if(e.key==='Escape') close();
-  }});
-}})();
-</script>
-</body>
-</html>""")
-
-    with open(TARGET, "w", encoding="utf-8") as fh:
-        fh.write("".join(parts))
-    print(f"报告已生成：{TARGET}")
-    print(f"成功 {ok}/{meta['total']}，失败 {fail}")
-
-
-if __name__ == "__main__":
-    main()