16 Commits
Author SHA1 Message Date
xslandCursor fe0e74ece8 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>
2026-07-30 23:03:33 +08:00
xsl 52913c94fc fix: 删除 MeasureResult.__init__ 中重复的七眼厘米赋值块 2026-07-27 23:17:22 +08:00
xsl 13526cb5b8 feat: 接口1/5/6 发际线弃用逻辑(顶庭<0.7cm)
发际线离头顶<0.7cm时判定分割不可靠,弃用发际线:
顶/上庭字段置null、face_total只算中下庭、标注图保留头顶线去掉发际线、
只标中/下庭。eye1/7竖向范围改用眉心。
2026-07-27 23:10:54 +08:00
xsl f509fe99b4 docs: 同步 integration.html / 接口文档.md / test_interface5.html
- 接口5: 补 generate_grow_image 参数说明(接口文档/integration/test_interface5 加控件)
- 接口1/5/6: 补 left_position/right_position 字段(MediaPipe 21/251号点)
- 接口4: features 字段纠正为固定6个英文字段(原误写~42项含中文, 与代码不符)
- 接口7: 完全移除(代码已 deprecated=True 固定返回错误, 文档却当正常接口详述)
- 错误码: 删错误的'1004已废弃'(1004仍用于接口2/5 gender校验), 补 1004 正确描述 + 1009(X-Internal-Token鉴权)
- test_interface5.html: 加 generate_grow_image 复选框
2026-07-24 00:42:34 +08:00
xsl a1d458eb20 feat(接口1/5/6): 返回数据新增 left_position/right_position(MediaPipe 21/251号点)
- face_mesh_landmarks.py: 加常量 LEFT_POSITION=21 / RIGHT_POSITION=251
- measure.py: MeasureResult 收 landmarks/宽高, to_response 顶层输出两点(原图像素 {x,y}, 与 landmarks 同格式)
- measure_face 透传 landmarks(签名不变, 6处调用零改动); __init__ 用 None 默认值守卫向后兼容
- 三接口自动生效: 接口1/6 在 data 顶层, 接口5 在 face_measure 对象里(复用同一 to_response)
- 实测坐标左右镜像合理, 44 个现有测试全过无回归
2026-07-24 00:42:26 +08:00
xsl 92e628b0d5 feat(接口5): 新增 generate_grow_image 参数控制是否生成生发效果图
- app.py: 接口5 路由加表单参数 generate_grow_image(bool, 默认 True)并透传
- hairline/service.py: generate_hairline_pngs 加同名参数, False 时跳过 ComfyUI 生发、grown_png 恒 None
- 默认行为不变(向后兼容); false 时仅返回三档发际线叠图与中心点, 大幅降低耗时
- 网关字节级透传 multipart, 新参数自动到达 worker, 无需改网关
2026-07-24 00:42:18 +08:00
xslandCursor b4714cedf3 perf(接口2女): REDRAW_MAX_SIDE 默认 1024→896 兜底
1024 档下部分大图(swapHair ~5.3s 地板 + ComfyUI 重绘)仍会踩 12s 线。
压到 896 后 ComfyUI 段稳定 ~4s,女性路径总耗时 9~11s,留出安全余量。
追画质可用环境变量 REDRAW_MAX_SIDE=1024 覆盖;接口2男/接口3 的
GROW_B_MAX_SIDE 保持 1024 不变(单段 ComfyUI,无 swapHair 地板)。

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-07-23 01:42:27 +08:00
xslandCursor e7b62f2b2e perf(接口2): 稳定混跑耗时至12s内 —— ComfyUI插队 + CLIP挪CPU + 提示词全局统一
问题:接口2 与接口3/5 乱序调用时耗时抖动(最差 15~22s)。两个根因:
1. GPU 24G 常驻 21.4G,Flux-2(3.9G) 无法完全驻留显存,每次采样动态换页,
   速度随空闲显存波动(2s~8s);
2. ComfyUI 单队列 FIFO,接口2 排在接口3/5 批量任务后面。

改动:
- hairline/comfyui.py: run() 新增 front 参数,/prompt 带 "front": true 插队到队列最前;
  redraw.py 透传;service.py 接口2 三处调用(女重绘 + 男有/无遮罩)传 front=True,
  接口3/5 仍走普通队列。
- add_hair.json / 0716add-hair-api.json: 节点61 CLIPLoader device default→cpu。
  qwen CLIP(4G) 不再占显存(文本条件缓存常年命中),ComfyUI 显存 8.8G→4.5G,
  Flux-2 完全驻留,采样稳定 ~3-5s。代价:换 prompt 后首次请求 CPU 编码 ~11s(一次性)。
- 提示词全局统一为「填充遮罩区域的头发,皮肤加一点磨皮,再加一点美颜」:
  app.py 4处默认值、service.py _REDRAW_PROMPT、redraw.py _DEFAULT_PROMPT、
  4个工作流节点60内置文案、测试页(test_interface2/3/7/12/12_final)、local_test。
  任何两个不同 prompt 交替提交都会打爆 CLIP 编码缓存(--cache-classic 只存最近一次),
  之前测试页旧文案与服务端不一致导致交替测试每次 +11s。
- app.py: 接口7 /api/v1/hair/grow-v2 下线(业务弃用;add_hair2.json 的 Klein-9b
  会把常驻 Klein-4b 挤出显存)。保留 stub 返回 1007 明确报错,避免裸 404。

实测(1024 档):接口2女 8.5~10s、接口2男 ~5s、接口3 ~7-10s,交替混跑无尖刺。

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-07-23 01:01:48 +08:00
xslandCursor 4291f125d4 fix(接口2): 发际线钳制到头部轮廓,修复短发/光头照片发际线贴到头部外面
短发/剃光头照片(如男性椭圆发际线)中间锚点射线检测命中不到 hair 像素时,
sample_hairline 的 fallback 会用固定 0.18 归一化偏移把点顶到头部轮廓外的背景,
在有效/失效锚点交界处形成尖角,被贴图不透明像素蒙到后露出戳出头部的线条。

新增 clamp_hairline_to_silhouette + sample_hairline_clamped,在几何检测后按每列
SegFormer(skin∪hair) 轮廓上沿做安全网钳制;extract_context 固定改用钳制版本。
只在 fallback 越界时生效,正常长发照片结果与旧行为一致,纯 numpy/opencv 与 GPU 无关。

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-07-22 23:41:52 +08:00
xslandCursor 7fc0210ce6 fix(pose): 修正正面照被误判为1003(solvePnP翻转解)
ITERATIVE 偶发收敛到相机后方(tz<0),roll≈±180° 超阈值,
把正面照误判为非正面。检测到负深度时回退 SQPNP 重解正深度解。
补充回归测试。

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-07-21 16:47:04 +08:00
xslandCursor 085ad3ced0 接口1 四庭七眼标注:字体更小、数值带cm、增加百分比、线名右移居中对齐
Co-authored-by: Cursor <cursoragent@cursor.com>
2026-07-19 23:37:59 +08:00
xslandCursor 2993a0b948 perf(接口2/3): 混合调用零模型换出且响应<12s
- 三接口统一 ComfyUI 重绘 prompt「填充遮罩区域的头发,皮肤加一点磨皮」,
  避免 CLIP 文本条件缓存失效导致的反复重载(单卡装不下 Flux+CLIP 同驻)。
- 接口2 女重绘整条管线(swapHair+ComfyUI)送模型前限边 REDRAW_MAX_SIDE(默认1024),
  overlay 预览保持全分辨率,结果放大回原尺寸。
- 接口2 男/接口3 单段推理经 _prep_comfy_input 限边 GROW_B_MAX_SIDE(默认1024)。
- 修复真实大图(1257x1495)全分辨率送模型导致 13~21s 且把 Flux 挤出显存的问题。
- comfyui.py 增加输入尺寸日志;service.py 增加女重绘分段计时(swap/blend/ComfyUI)。

实测真实图三接口任意交替: 女9~12s / 男7.8s / 接口3 6.6s,CLIP/Flux 重载 0 次。

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-07-19 20:08:21 +08:00
xslandCursor 08b31a3baa fix: 测试页同时兼容 base64 与 URL 图片字段
直连 worker 返回 *_base64、经网关则改写为 *_url;统一 resolveImgSrc 后接口1/2/3/5/6 测试页都能正确显示结果图。

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-07-19 15:49:46 +08:00
xsl 51e313e845 save code 2026-07-19 00:51:46 +08:00
xsl fbbcd48418 feat: 系统配置文件适配 RTX 3090
更新系统服务配置文件:
- hair-worker.service: 路径从 /home/xsl/ 改为 /home/ubuntu/,添加 comfyui/change_hair 依赖
- comfyui.service: 添加 --cache-classic --fast 标志优化性能
2026-07-18 19:18:52 +08:00
xsl 74ccab0ff8 feat: 适配 RTX 3090 (24GB) 环境优化
硬件迁移:从 RTX 5090 (32GB) 迁移到 RTX 3090 (24GB)

主要改动:
1. hairline/comfyui.py: 轮询间隔从 0.2s 降到 0.05s
2. hairline/service.py: PNG 编码 compress_level=1,节省 ~240ms
3. add_hair.json: 工作流使用 4B FP8 模型 + steps=4
4. static/test_interface3.html: 修复图片显示(添加 data:image/jpeg;base64, 前缀)

性能优化后接口3响应时间:6.6-7.3s(之前 8.66s)
2026-07-18 18:58:40 +08:00
336 changed files with 575 additions and 13352 deletions
-29
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@@ -53,32 +53,3 @@ 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.*
# 脸型测试素材(人像照片,体积大,不入 git;仅保留 6 张基准标注图)
face/test_img/脸型测试集合/
face/test_img/girl/
face/test_img/man/
# 脸型特征缓存(由 face/dump_features.py 生成,可随时重跑)
face/cache/
# 脸型报告输出(标注图体积大,不入 git)
static/face_shape_report/
static/face_shape_report.html
static/facetest_report/
static/facetest_report.html
static/facetest_all_report/
static/facetest_all_report.html
+1 -1
View File
@@ -29,7 +29,7 @@
"60": {
"class_type": "JjkText",
"inputs": {
"text": "填充遮罩区域的头发"
"text": "填充遮罩区域的头发,皮肤加一点磨皮,再加一点美颜"
}
},
"22": {
-117
View File
@@ -1,117 +0,0 @@
{
"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"}},
"26": {"class_type": "LoadImage", "inputs": {
"image": "placeholder.png"}},
"60": {"class_type": "JjkText", "inputs": {
"text": "填充遮罩区域的头发,皮肤加一点磨皮"}},
"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"}}
}
+1 -1
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File diff suppressed because one or more lines are too long
+1 -2
View File
@@ -82,8 +82,7 @@ model.safetensors https://huggingface.co/jonathandinu/face-parsing/resol
模型已就位,但**内网机还需要 Python 依赖的离线 wheel 包**,否则 `pip install` 在内网无法联网安装。这部分**与目标机的操作系统、Python 版本、CUDA 版本强相关**,需确认后单独打包:
- **workerGPU 机)**`mediapipe` / `opencv-python` / `numpy<2` / `Pillow` / **`torch`+`torchvision` 的 CUDA 版**(按 GPU 的 CUDA 版本选 cu118/cu121 等)/ `transformers`(接口2 SegFormer+ FastAPI/uvicorn 全家桶。
- **网关机**FastAPI/uvicorn/httpx 等代理依赖 + **接口4 现需 `mediapipe`/`opencv-python`/`numpy<2`**(脸型本机计算),
仍**不需要 torch**(无 GPU 推理需求)。
- **网关机**很轻,只需 FastAPI/uvicorn/httpx 等代理依赖**不需要 torch/mediapipe**。
> 架构已拆分(见 `docs/实现说明.md`):算法依赖只装在 worker,网关保持轻量。
+3 -3
View File
@@ -171,7 +171,7 @@
"16": {
"inputs": {
"unet_name": "flux-2-klein-4b-fp8.safetensors",
"weight_dtype": "fp8_e4m3fn"
"weight_dtype": "fp8_e4m3fn_fast"
},
"class_type": "UNETLoader",
"_meta": {
@@ -410,7 +410,7 @@
},
"60": {
"inputs": {
"text": "填充遮罩区域的头发"
"text": "填充遮罩区域的头发,皮肤加一点磨皮,再加一点美颜"
},
"class_type": "JjkText",
"_meta": {
@@ -447,4 +447,4 @@
"title": "Color Match"
}
}
}
}
-450
View File
@@ -1,450 +0,0 @@
{
"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": 217742615722421
},
"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": "flux2.0/flux-2-klein-9b-fp8.safetensors",
"weight_dtype": "fp8_e4m3fn"
},
"class_type": "UNETLoader",
"_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-1781655058499.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": "Mask Fill Holes"
}
},
"36": {
"inputs": {
"masks": [
"33",
0
]
},
"class_type": "Convert Masks to Images",
"_meta": {
"title": "Convert Masks to Images"
}
},
"37": {
"inputs": {
"method": "intensity",
"image": [
"39",
0
]
},
"class_type": "Image To Mask",
"_meta": {
"title": "Image To Mask"
}
},
"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": "ImageAndMaskPreview"
}
},
"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_zuixa_00119_.png&type=temp&subfolder=&rand=0.7926013627811991"
},
{
"name": "B",
"selected": true,
"url": "/api/view?filename=rgthree.compare._temp_zuixa_00120_.png&type=temp&subfolder=&rand=0.43692946883795813"
}
]
},
"image_a": [
"62",
0
],
"image_b": [
"26",
0
]
},
"class_type": "Image Comparer (rgthree)",
"_meta": {
"title": "Image Comparer (rgthree)"
}
},
"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"
},
"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"
}
}
}
+2 -2
View File
@@ -381,7 +381,7 @@
},
"60": {
"inputs": {
"text": "充遮罩区域的头发"
"text": "充遮罩区域的头发,区域内填充满头发,不要保留皮肤,发际线下移填充头发。自然的头发生长方向,逼真的头发质感,自然发质。"
},
"class_type": "JjkText",
"_meta": {
@@ -684,7 +684,7 @@
},
"87": {
"inputs": {
"text": "填充遮罩区域的头发",
"text": "去掉头发接缝的黄色痕迹,头发完美融合,保持发型不变,发色不变。其他不变。",
"clip": [
"78",
0
+122 -105
View File
@@ -1,6 +1,6 @@
"""旷视五接口 — worker 侧(高性能 GPU 后端)。
接口 1 等算法在 worker;接口4(用户特征)已迁到网关本机(调豆包),worker 同路径只返回明确错误、无 Mock。
接口 1(四庭七眼测量)已为**真实算法实现**(见 face_analysis 包);接口 2~5 仍为 Mock。
拆分架构:worker 跑算法、返回 `annotated_image_base64`(不落盘、不拼 URL,由网关完成)。
worker 对 `/api/*` 校验内网鉴权头 `X-Internal-Token``/health` 供网关探测不校验。
"""
@@ -139,7 +139,46 @@ 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", "/api/v1/redraw")
"/api/v1/debug", "/api/v1/redraw",
"/api/swapHair", "/hairColor")
# ---------------------------------------------------------------------------
# change_hair 代理路由(解决 CORS 问题)
# ---------------------------------------------------------------------------
_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")
@@ -488,7 +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
""",
@@ -593,7 +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={
@@ -736,7 +775,7 @@ async def hair_grow(
hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔(必填),如 1,2,3。female:1-5 male:1-4"),
beauty_enabled: bool = Form(default=False, description="是否开启美颜(本期不生效)"),
use_mask: bool = Form(default=True, description="是否启用 inpaint 遮罩(测试对比用)。false 时用干净原图生成(空遮罩,不烧模板线)"),
prompt: str = Form(default="填充遮罩区域的头发", 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,皮肤加一点磨皮,再加一点美颜", description="ComfyUI 提示词,会替换工作流节点60的文本"),
):
# 1. gender 必填校验(非法/缺失 → 1004)
if gender not in ("male", "female"):
@@ -808,7 +847,6 @@ async def hair_grow_v2():
"/api/v1/hair/grow-b",
summary="接口3 B端生发(医生/操作端)",
tags=["生发"],
deprecated=True,
description="""
医生/操作端在用户照片上**手动用马克笔划线标注**目标发际线后,**只需上传这一张划线图**,返回:
- 生发后效果图(系统检测划线 → 据此生成「植发 3 个月」效果)
@@ -848,11 +886,36 @@ async def hair_grow_b(
marked_image_url: Optional[str] = Form(default=None, description="划线图片 URL"),
marked_image_base64: Optional[str] = Form(default=None, description="划线图片 base64"),
use_mask: bool = Form(default=True, description="是否画发际线(测试对比用)。false 时跳过划线检测、直接送划线图"),
prompt: str = Form(default="填充遮罩区域的头发", 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,皮肤加一点磨皮,再加一点美颜", description="ComfyUI 提示词,会替换工作流节点60的文本"),
):
"""接口3 已屏蔽:调用直接返回错误,不再执行生发/ComfyUI 逻辑。
保留路由(含原参数签名)避免老客户端裸 404,multipart 入参仍被接受但不处理。"""
return err(1007, "接口3/api/v1/hair/grow-b)已屏蔽,暂不提供服务")
# 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)
if e is not None:
return e
marked = cv2.imdecode(np.frombuffer(marked_raw, np.uint8), cv2.IMREAD_COLOR)
if marked 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_b
res = await run_in_threadpool(generate_grow_b, marked, use_mask, prompt)
if res["status"] == "no_face":
return err(1001, "无法识别人像")
if res["status"] == "no_line":
return err(1001, "未检测到发际线划线,请确认划线图额头有清晰的手绘发际线")
grown_b64 = _png_to_jpg_b64(res["grown_png"]) if res["grown_png"] else None # 生发图 JPG
data = {
"hair_growth_image_base64": grown_b64,
"hairline_type": "custom",
}
return ok(data)
except Exception as ex: # noqa: BLE001
logger.exception("接口3 处理异常")
return err(1007, f"处理失败:{ex}")
# ---------------------------------------------------------------------------
@@ -861,28 +924,56 @@ async def hair_grow_b(
@app.post(
"/api/v1/face/features",
summary="接口4 用户特征分析(仅网关)",
summary="接口4 用户特征分析",
tags=["人脸分析"],
description="""
**本接口不在 worker 实现。** 请调用网关(本机默认 `http://127.0.0.1:8080`)的同路径;
网关本机调火山方舟豆包视觉模型,不转发到 worker。
description=f"""
输入用户照片,返回 N 个用户面部特征字段。
直接打 worker(如 `:8187`)会返回错误,避免误用假数据。
{_image_fields_desc}
图片同时支持 `multipart/form-data` 文件上传(字段名 `image_file`)。
---
由**火山方舟 豆包视觉模型**分析,返回**固定 6 个英文字段**。
**返回格式**`data.features` 为一个 **JSON 字符串**(不是对象),需要在客户端 `JSON.parse()` 后使用。
| 字段 | 说明 |
|------|------|
| face_shape | 脸形(如"鹅蛋脸" |
| eyebrow_shape | 眉形(如"平眉" |
| facial_age | 面部年龄(区间,如"18-25岁" |
| dynamic_static_type | 动静类型("静态型"/"动态型" |
| gender | 性别(""/"" |
| gene_style | 基因风格(如"自然型" |
> 无人脸返回 `1001`。
""",
responses={
200: {
"description": "worker 不提供本接口",
"description": "成功",
"content": {
"application/json": {
"example": {
"code": 1007,
"message": "接口4 仅在网关实现,请访问网关(本机默认 :8080),worker 不提供本接口",
"code": 0,
"message": "success",
"request_id": "mock-request-id",
"data": None,
"data": {
"features": '{"face_shape":"鹅蛋脸","eyebrow_shape":"平眉","facial_age":"18-25岁","dynamic_static_type":"静态型","gender":"","gene_style":"少年型"}',
},
}
}
},
},
400: {
"description": "参数错误 / 图片识别失败",
"content": {
"application/json": {
"example": {"code": 1001, "message": "无法识别人像", "request_id": "x", "data": None}
}
},
},
},
)
async def face_features(
@@ -890,12 +981,16 @@ async def face_features(
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)"),
):
# 接口4 只在网关实现(gateway/app.py → face_features.analyze_features)。
# 不再返回 Mock 成功数据,避免本机打 :8187 时被假结果误导
return err(
1007,
"接口4 仅在网关实现,请访问网关(本机默认 :8080),worker 不提供本接口",
# ⚠️ 接口4 已迁到**网关本机**实现(直接调豆包视觉模型,见 gateway/app.py)。
# 网关不会把本接口转发到 worker,故此处仅留 Mock 占位、保持 worker 无外网依赖
features = json.dumps(
{
"face_shape": "鹅蛋脸", "eyebrow_shape": "平眉", "facial_age": "18-25岁",
"dynamic_static_type": "静态型", "gender": "", "gene_style": "少年型",
},
ensure_ascii=False,
)
return ok({"features": features})
# ---------------------------------------------------------------------------
@@ -1001,7 +1096,7 @@ async def hairline_generate(
gender: Optional[str] = Form(default=None, description="性别 male/female(必填)"),
hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔(必填,如 1,2,3)。female:1-5 male:1-4"),
use_mask: bool = Form(default=True, description="生发是否启用 inpaint 遮罩(同接口2,测试对比用)"),
prompt: str = Form(default="填充遮罩区域的头发", description="ComfyUI 提示词(同接口2),会替换工作流节点60的文本"),
prompt: str = Form(default="填充遮罩区域的头发,皮肤加一点磨皮,再加一点美颜", description="ComfyUI 提示词(同接口2),会替换工作流节点60的文本"),
generate_grow_image: bool = Form(default=True, description="是否生成生发效果图(ComfyUI 生发,最耗时)。默认 true 出图;false 时跳过生发,各发型 grown_image 恒为 null,仅返回三档发际线叠图与中心点"),
):
if gender not in ("male", "female"):
@@ -1395,7 +1490,7 @@ async def hairline_grow_v2(
inpainting_fill: int = Form(default=1, description="change_hair服务端重绘填充:0=保留原图 | 1=噪声 | 2=纯色 | 3=潜变量。默认 1"),
mask_blur: int = Form(default=11, description="change_hair服务端遮罩边缘模糊像素,默认 11"),
mask_dilate_scale: float = Form(default=1.0, description="change_hair服务端遮罩膨胀缩放,默认 1.0"),
comfyui_prompt: Optional[str] = Form(default=None, description="Flux-2 重绘提示词,None 用默认「填充遮罩区域的头发」"),
comfyui_prompt: Optional[str] = Form(default=None, description="Flux-2 重绘提示词,None 用默认「填充遮罩区域的头发,皮肤加一点磨皮,再加一点美颜"),
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"),
@@ -1561,7 +1656,7 @@ async def hairline_grow_v2_final_v2(
async def api_redraw(
image_file: UploadFile = File(..., description="人物图片(JPG/PNG"),
mask_file: UploadFile = File(..., description="遮罩图片(PNG,支持红/白/alpha 格式)"),
prompt: str = Form(default="填充遮罩区域的头发",
prompt: str = Form(default="填充遮罩区域的头发,皮肤加一点磨皮,再加一点美颜",
description="ComfyUI 提示词"),
):
image_bytes = await image_file.read()
@@ -1606,84 +1701,6 @@ async def download_hairline_log(rid: Optional[str] = None, tail: int = 500):
return PlainTextResponse("".join(lines), media_type="text/plain; charset=utf-8")
# ---------------------------------------------------------------------------
# 调试:MediaPipe 脸型分类(face/face_shape_classifier.py,非接口4 豆包)
# ---------------------------------------------------------------------------
@app.post(
"/api/v1/debug/face-shape",
summary="调试 单张脸型分类(MediaPipe)",
tags=["调试"],
description="""
离线脸型分类调试接口(`face/face_shape_classifier.py`),**不是**接口4 的豆包视觉分析。
上传正面照 → MediaPipe 468 点 → 7 类脸型评分 + 特征标注图。
""",
include_in_schema=True,
)
async def debug_face_shape(
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"),
):
raw, e = await resolve_image_bytes(image_file, image_url, image_base64)
if e:
return e
try:
nparr = np.frombuffer(raw, np.uint8)
bgr = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
if bgr is None:
return err(1008, "图片格式不支持(仅 JPG / PNG)")
except Exception: # noqa: BLE001
return err(1008, "图片格式不支持(仅 JPG / PNG)")
def _jsonable(obj):
if isinstance(obj, dict):
return {k: _jsonable(v) for k, v in obj.items()}
if isinstance(obj, (list, tuple)):
return [_jsonable(v) for v in obj]
if hasattr(obj, "item"):
return obj.item()
if isinstance(obj, (float, int, str, bool)) or obj is None:
return obj
return obj
from fastapi.concurrency import run_in_threadpool
try:
from face.face_shape_classifier import classify_from_image
result = await run_in_threadpool(
classify_from_image, bgr, True, True,
)
except ValueError as ex:
return err(1001, str(ex) or "无法识别人像")
except Exception as ex: # noqa: BLE001
return err(1007, f"脸型分类失败:{ex}")
details = result.get("details") or {}
ranked = [
{"shape": name, "score": round(float(score), 2)}
for name, score in (details.get("ranked") or [])
]
annotated = result.pop("annotated", None)
h, w = bgr.shape[:2]
data = {
"face_shape": result["face_shape"],
"display": result["display"],
"confidence": round(float(result["confidence"]), 4),
"is_mixed": bool(details.get("is_mixed")),
"second_shape": details.get("second_shape"),
"score_gap": round(float(details["score_gap"]), 2) if details.get("score_gap") is not None else None,
"ranked": ranked,
"features": _jsonable(result.get("features") or {}),
"zscores": _jsonable(details.get("zscores") or {}),
"image_size": {"width": w, "height": h},
"annotated_image_base64": _jpg_b64(annotated) if annotated is not None else None,
}
return ok(data)
# ---------------------------------------------------------------------------
# 健康检查
# ---------------------------------------------------------------------------
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@@ -1,157 +0,0 @@
#!/usr/bin/env python3
"""9B vs 4B 模型对比:3 张图 × 5 种发型 = 15 张对比图。
第一次运行PLAN_TAG=A 9B 模型需先切工作流到 .bak
第二次运行PLAN_TAG=Bplus4B 模型需切回 4B 工作流
"""
import json, os, time, base64, subprocess, urllib.request
from datetime import datetime
API_BASE = "http://127.0.0.1:8187"
TOKEN = "dev-shared-secret-2026"
IMAGES = [
"/home/ubuntu/hair/image/girl_img/girl1.jpg",
"/home/ubuntu/hair/image/girl_img/girl7.jpg",
"/home/ubuntu/hair/image/girl_img/girl13.jpg",
]
PLAN_TAG = os.getenv("PLAN_TAG", "A")
OUT_DIR = f"/home/ubuntu/hair/benchmark_out/9b_vs_4b_{PLAN_TAG}"
RESULT_JSON = os.path.join(OUT_DIR, "results.json")
REPORT_HTML = os.path.join(OUT_DIR, "report.html")
HAIR_STYLES = [(1, "椭圆"), (2, "花瓣"), (3, "心形"), (4, "直线"), (5, "波浪")]
WARMUP = os.getenv("WARMUP", "1") == "1" # 第一次调用做预热
os.makedirs(OUT_DIR, exist_ok=True)
def get_vram():
try:
out = subprocess.check_output(
["nvidia-smi", "--query-gpu=memory.used,memory.free",
"--format=csv,noheader,nounits"], text=True, timeout=5).strip()
used, free = out.split(",")
return int(used.strip()), int(free.strip())
except Exception:
return -1, -1
def _multipart(fields, files=None):
boundary = "----CmpTest" + str(int(time.time() * 1000))
parts = []
for k, v in fields.items():
parts.append(f"--{boundary}\r\n".encode())
parts.append(f'Content-Disposition: form-data; name="{k}"\r\n\r\n'.encode())
parts.append(str(v).encode())
parts.append(b"\r\n")
if files:
for field_name, (filename, data, mime) in files.items():
parts.append(f"--{boundary}\r\n".encode())
parts.append(f'Content-Disposition: form-data; name="{field_name}"; filename="{filename}"\r\n'.encode())
parts.append(f"Content-Type: {mime}\r\n\r\n".encode())
parts.append(data)
parts.append(b"\r\n")
parts.append(f"--{boundary}--\r\n".encode())
return b"".join(parts), boundary
def call_iface2(image_path, hair_style):
with open(image_path, "rb") as f:
img_data = f.read()
fields = {"gender": "female", "hair_style": str(hair_style),
"prompt": "填充遮罩区域的头发"}
body, boundary = _multipart(
fields, {"image_file": (os.path.basename(image_path), img_data, "image/jpeg")})
req = urllib.request.Request(f"{API_BASE}/api/v1/hair/grow", data=body, method="POST")
req.add_header("Content-Type", f"multipart/form-data; boundary={boundary}")
req.add_header("X-Internal-Token", TOKEN)
t0 = time.time()
try:
with urllib.request.urlopen(req, timeout=600) as resp:
data = json.loads(resp.read())
return data, time.time() - t0, None
except Exception as e:
return {}, time.time() - t0, str(e)
def extract_image(data, img_idx, hs):
"""从响应提取 grown_image_base64 并保存为 jpg。"""
if not data or not data.get("data"):
return None
items = data["data"].get("results") or []
if not items:
return None
b64 = items[0].get("grown_image_base64") or ""
if b64.startswith("data:"):
b64 = b64.split(",", 1)[1]
if not b64:
return None
fname = f"img{img_idx}_style{hs}.jpg"
path = os.path.join(OUT_DIR, fname)
with open(path, "wb") as f:
f.write(base64.b64decode(b64))
return path
def run_test():
results = []
print(f"9B vs 4B 对比测试 — Plan={PLAN_TAG}")
print(f"图片: {[os.path.basename(p) for p in IMAGES]}")
print(f"发型: {[(s,n) for s,n in HAIR_STYLES]}")
print(f"总调用: {len(IMAGES)*len(HAIR_STYLES)}\n")
# 预热请求(避免第一次冷启动计入统计)
if WARMUP:
print("[warmup] 预热请求 (girl13, style=1) ...")
t0 = time.time()
_, warmup_time, _ = call_iface2(IMAGES[2], 1)
print(f" warmup: {warmup_time:.1f}s\n")
for img_idx, img_path in enumerate(IMAGES, start=1):
img_name = os.path.basename(img_path)
for hs, hs_name in HAIR_STYLES:
print(f"[img{img_idx}/{len(IMAGES)}] {img_name} style={hs}({hs_name}) ...")
vram_before, _ = get_vram()
data, elapsed, error = call_iface2(img_path, hs)
vram_after, _ = get_vram()
code = data.get("code", -1) if data else -1
ok = (code == 0)
img_saved = extract_image(data, img_idx, hs) if ok else None
record = {
"plan": PLAN_TAG,
"image_idx": img_idx,
"image_name": img_name,
"image_path": img_path,
"hair_style": hs,
"hair_style_name": hs_name,
"timestamp": datetime.now().strftime("%H:%M:%S"),
"elapsed_s": round(elapsed, 2),
"success": ok,
"code": code,
"error": error,
"vram_before_mb": vram_before,
"vram_after_mb": vram_after,
"vram_delta_mb": vram_after - vram_before,
"saved_image_path": img_saved,
}
results.append(record)
status = "" if ok else ""
print(f"{status} code={code} time={elapsed:.1f}s "
f"vram={vram_before}{vram_after}MB (Δ{vram_after-vram_before:+d}) "
f"img={'saved' if img_saved else 'none'}")
with open(RESULT_JSON, "w") as f:
json.dump(results, f, indent=2, ensure_ascii=False)
print(f"\n✅ 完成!{RESULT_JSON}")
print(f"📊 成功: {sum(1 for r in results if r['success'])}/{len(results)}")
if any(r['success'] for r in results):
avg = sum(r['elapsed_s'] for r in results if r['success']) / sum(1 for r in results if r['success'])
print(f"⏱ 平均: {avg:.2f}s")
if __name__ == "__main__":
run_test()
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#!/usr/bin/env python3
"""基准测试:19张图片 × 5种发际线,记录每步耗时和显存变化。
用法 python3 benchmark_grow.py
输出 benchmark_results.json + benchmark_report.html
"""
import json, os, time, base64, subprocess, re, glob, shutil
from datetime import datetime
from pathlib import Path
import urllib.request, urllib.error
API_URL = "http://127.0.0.1:8187/api/v1/hair/grow"
TOKEN = "dev-shared-secret-2026"
IMG_DIR = "/home/ubuntu/hair/image/girl_img"
OUT_DIR = "/home/ubuntu/hair/benchmark_out"
RESULT_JSON = os.path.join(OUT_DIR, "benchmark_results.json")
REPORT_HTML = os.path.join(OUT_DIR, "benchmark_report.html")
HAIRLINES = [
("1", "ellipse", "椭圆形"),
("2", "flower", "花瓣形"),
("3", "heart", "心形"),
("4", "straight", "直线形"),
("5", "wave", "波浪形"),
]
os.makedirs(OUT_DIR, exist_ok=True)
# ── 工具函数 ──────────────────────────────────────────────
def get_vram():
"""返回 (used_MB, free_MB)"""
try:
out = subprocess.check_output(
["nvidia-smi", "--query-gpu=memory.used,memory.free",
"--format=csv,noheader,nounits"], text=True, timeout=5
).strip()
used, free = out.split(",")
return int(used.strip()), int(free.strip())
except Exception:
return -1, -1
def get_gpu_procs():
"""返回各进程显存占用 dict"""
try:
out = subprocess.check_output(
["nvidia-smi", "--query-compute-apps=pid,used_memory",
"--format=csv,noheader,nounits"], text=True, timeout=5
).strip()
procs = {}
for line in out.splitlines():
parts = line.split(",")
if len(parts) >= 2:
procs[parts[0].strip()] = int(parts[1].strip())
return procs
except Exception:
return {}
def read_worker_log_tail(n=50):
"""读取 worker.log 最后 n 行"""
log_path = "/home/ubuntu/hair/worker.log"
try:
result = subprocess.run(["tail", "-n", str(n), log_path],
capture_output=True, text=True, timeout=5)
return result.stdout
except Exception:
return ""
def call_api(image_path, hair_style_value):
"""调用接口2,返回 (json_dict, elapsed_sec, error_or_None)"""
import mimetypes
boundary = "----BenchmarkBoundary" + str(int(time.time()*1000))
filename = os.path.basename(image_path)
mime = mimetypes.guess_type(image_path)[0] or "image/jpeg"
with open(image_path, "rb") as f:
img_data = f.read()
body_parts = []
body_parts.append(f"--{boundary}\r\n".encode())
body_parts.append(f'Content-Disposition: form-data; name="image_file"; filename="{filename}"\r\n'.encode())
body_parts.append(f"Content-Type: {mime}\r\n\r\n".encode())
body_parts.append(img_data)
body_parts.append(f"\r\n--{boundary}\r\n".encode())
body_parts.append(b'Content-Disposition: form-data; name="gender"\r\n\r\n')
body_parts.append(b"female")
body_parts.append(f"\r\n--{boundary}\r\n".encode())
body_parts.append(b'Content-Disposition: form-data; name="hair_style"\r\n\r\n')
body_parts.append(hair_style_value.encode())
body_parts.append(f"\r\n--{boundary}\r\n".encode())
body_parts.append(b'Content-Disposition: form-data; name="use_mask"\r\n\r\n')
body_parts.append(b"0")
body_parts.append(f"\r\n--{boundary}\r\n".encode())
body_parts.append(b'Content-Disposition: form-data; name="prompt"\r\n\r\n')
body_parts.append(b"\xe5\xa1\xab\xe5\x85\x85\xe9\x81\xae\xe7\xbd\xa9\xe5\x8c\xba\xe5\x9f\x9f\xe7\x9a\x84\xe5\xa4\xb4\xe5\x8f\x91\xef\xbc\x8c\xe7\x9a\xae\xe8\x82\xa4\xe5\x8a\xa0\xe4\xb8\x80\xe7\x82\xb9\xe7\xa3\xa8\xe7\x9a\xae")
body_parts.append(f"\r\n--{boundary}--\r\n".encode())
body = b"".join(body_parts)
req = urllib.request.Request(API_URL, data=body, method="POST")
req.add_header("Content-Type", f"multipart/form-data; boundary={boundary}")
req.add_header("X-Internal-Token", TOKEN)
t0 = time.time()
try:
with urllib.request.urlopen(req, timeout=600) as resp:
raw = resp.read()
elapsed = time.time() - t0
data = json.loads(raw)
return data, elapsed, None
except urllib.error.HTTPError as e:
elapsed = time.time() - t0
try:
data = json.loads(e.read())
except Exception:
data = {"error": str(e)}
return data, elapsed, f"HTTP {e.code}"
except Exception as e:
elapsed = time.time() - t0
return {}, elapsed, str(e)
def parse_step_timing(log_text):
"""从 worker.log 文本中解析步骤耗时"""
timing = {}
for line in log_text.splitlines():
# 匹配 "步骤1 遮罩完成 耗时=949ms"
m = re.search(r"步骤(\d+)\s+\S+\s+耗时=(\d+)ms", line)
if m:
timing[f"step{m.group(1)}_ms"] = int(m.group(2))
# 匹配 "人脸检出 px_per_cm=30.669 图尺寸=813x967"
if "人脸检出" in line:
m2 = re.search(r"px_per_cm=([\d.]+)", line)
if m2:
timing["px_per_cm"] = float(m2.group(1))
# 匹配 "头发分割完成"
if "头发分割完成" in line:
timing["hair_seg"] = True
# 匹配 "换发型图失败"
if "换发型图失败" in line or "换发型服务不可达" in line:
timing["swap_error"] = line.strip()[-100:]
# 匹配 "重绘" 相关
if "重绘" in line and ("完成" in line or "失败" in line):
timing["redraw_status"] = "完成" if "完成" in line else "失败"
return timing
# ── 主流程 ────────────────────────────────────────────────
def run_benchmark():
images = sorted(glob.glob(os.path.join(IMG_DIR, "girl*.jpg")))
print(f"找到 {len(images)} 张图片")
# 加载已有结果(支持断点续跑)
results = []
if os.path.exists(RESULT_JSON):
with open(RESULT_JSON) as f:
results = json.load(f)
print(f"已有 {len(results)} 条记录,继续未完成的测试")
total_calls = len(images) * len(HAIRLINES)
done = len(results)
print(f"总计 {total_calls} 次调用,已完成 {done},剩余 {total_calls - done}")
log_offset = 0
try:
log_offset = subprocess.check_output(["wc", "-l", "/home/ubuntu/hair/worker.log"],
text=True, timeout=5).split()[0]
log_offset = int(log_offset)
except Exception:
pass
for img_idx, img_path in enumerate(images):
img_name = os.path.basename(img_path)
# 跳过已完成的图片
img_results = [r for r in results if r["image"] == img_name]
if len(img_results) >= len(HAIRLINES):
print(f"[{img_idx+1}/{len(images)}] {img_name} 已完成,跳过")
continue
for hl_id, hl_key, hl_label in HAIRLINES:
# 跳过已完成的
existing = [r for r in results if r["image"] == img_name and r["hairline_id"] == hl_id]
if existing:
continue
print(f"\n[{img_idx+1}/{len(images)}] {img_name}{hl_label}({hl_id}) ...")
# 记录 worker.log 行数
try:
log_before = int(subprocess.check_output(
["wc", "-l", "/home/ubuntu/hair/worker.log"], text=True, timeout=5).split()[0])
except Exception:
log_before = 0
# VRAM before
vram_before, vram_free_before = get_vram()
procs_before = get_gpu_procs()
t_start = time.time()
# 调用API
api_result, elapsed, error = call_api(img_path, hl_id)
t_end = time.time()
# VRAM after
vram_after, vram_free_after = get_vram()
procs_after = get_gpu_procs()
# 读取新日志
try:
log_diff = subprocess.check_output(
["tail", "-n", "+{}".format(log_before + 1), "/home/ubuntu/hair/worker.log"],
text=True, timeout=5)
except Exception:
log_diff = ""
step_timing = parse_step_timing(log_diff)
# 提取结果图片
preview_b64 = ""
grown_b64 = ""
api_code = api_result.get("code", -1)
api_msg = api_result.get("message", "")
api_results = api_result.get("data", {}).get("results", [])
if api_results:
r0 = api_results[0]
preview_b64 = r0.get("image_base64", "")
grown_b64 = r0.get("grown_image_base64", "")
# 保存缩略图
thumb_dir = os.path.join(OUT_DIR, "thumbs")
os.makedirs(thumb_dir, exist_ok=True)
if grown_b64:
grown_bytes = base64.b64decode(grown_b64)
thumb_path = os.path.join(thumb_dir, f"{img_name}_hl{hl_id}_grown.jpg")
with open(thumb_path, "wb") as f:
f.write(grown_bytes)
if preview_b64:
preview_bytes = base64.b64decode(preview_b64)
thumb_path = os.path.join(thumb_dir, f"{img_name}_hl{hl_id}_preview.png")
with open(thumb_path, "wb") as f:
f.write(preview_bytes)
record = {
"image": img_name,
"image_idx": img_idx + 1,
"hairline_id": hl_id,
"hairline_key": hl_key,
"hairline_label": hl_label,
"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
"total_time_s": round(elapsed, 2),
"api_code": api_code,
"api_msg": api_msg,
"error": error,
"has_preview": bool(preview_b64),
"has_grown": bool(grown_b64),
"preview_size": len(preview_b64),
"grown_size": len(grown_b64),
"vram_before_mb": vram_before,
"vram_after_mb": vram_after,
"vram_free_before_mb": vram_free_before,
"vram_free_after_mb": vram_free_after,
"vram_delta_mb": vram_after - vram_before,
"procs_before": procs_before,
"procs_after": procs_after,
"step_timing": step_timing,
}
results.append(record)
print(f" → HTTP code={api_code} time={elapsed:.1f}s vram={vram_before}{vram_after}MB "
f"preview={'' if preview_b64 else ''} grown={'' if grown_b64 else ''}")
# 保存中间结果
with open(RESULT_JSON, "w") as f:
json.dump(results, f, indent=2, ensure_ascii=False)
# 生成中间报告
generate_html(results)
# 最终报告
generate_html(results)
print(f"\n✅ 完成!结果: {RESULT_JSON}")
print(f"📄 报告: {REPORT_HTML}")
print(f"📊 总调用: {len(results)}/{total_calls}")
def generate_html(results):
"""生成HTML报告"""
# 统计
total = len(results)
success = sum(1 for r in results if r["has_grown"])
failed = total - success
times = [r["total_time_s"] for r in results if r["has_grown"]]
avg_time = sum(times) / len(times) if times else 0
max_time = max(times) if times else 0
min_time = min(times) if times else 0
# 按发际线类型分组统计
hl_stats = {}
for r in results:
if r["has_grown"]:
hl = r["hairline_label"]
if hl not in hl_stats:
hl_stats[hl] = {"count": 0, "times": []}
hl_stats[hl]["count"] += 1
hl_stats[hl]["times"].append(r["total_time_s"])
# 按图片分组
img_groups = {}
for r in results:
img = r["image"]
if img not in img_groups:
img_groups[img] = []
img_groups[img].append(r)
# 生成VRAM变化数据
vram_data = [(i, r["vram_after_mb"]) for i, r in enumerate(results)]
html = f"""<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<title>接口2 基准测试报告</title>
<style>
* {{ margin:0; padding:0; box-sizing:border-box; }}
body {{ font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif; background:#1a1a2e; color:#e0e0e0; padding:20px; }}
h1 {{ text-align:center; margin-bottom:20px; color:#00d4ff; }}
.subtitle {{ text-align:center; color:#888; margin-bottom:30px; font-size:14px; }}
.summary {{ display:grid; grid-template-columns:repeat(auto-fit,minmax(180px,1fr)); gap:15px; margin-bottom:30px; }}
.card {{ background:#16213e; border-radius:12px; padding:20px; text-align:center; border:1px solid #333; }}
.card .num {{ font-size:32px; font-weight:700; }}
.card .label {{ font-size:12px; color:#888; margin-top:5px; }}
.card.ok .num {{ color:#0f0; }}
.card.err .num {{ color:#f44; }}
.card.time .num {{ color:#00d4ff; }}
table {{ width:100%; border-collapse:collapse; margin-bottom:30px; background:#16213e; border-radius:12px; overflow:hidden; }}
th {{ background:#0f3460; padding:12px 8px; text-align:center; font-size:13px; color:#fff; }}
td {{ padding:8px; text-align:center; border-bottom:1px solid #222; font-size:13px; }}
tr:hover {{ background:#1a1a3e; }}
.img-cell {{ text-align:left; }}
.time-bar {{ display:inline-block; height:20px; background:linear-gradient(90deg,#0f3460,#00d4ff); border-radius:4px; vertical-align:middle; min-width:2px; }}
.hl-badge {{ display:inline-block; padding:2px 8px; border-radius:10px; font-size:11px; font-weight:600; }}
.hl-ellipse {{ background:#1b4332; color:#52b788; }}
.hl-flower {{ background:#3a0ca3; color:#c77dff; }}
.hl-heart {{ background:#6a040f; color:#ff6b6b; }}
.hl-straight {{ background:#0077b6; color:#90e0ef; }}
.hl-wave {{ background:#9d4edd; color:#e0aaff; }}
.ok {{ color:#0f0; }} .fail {{ color:#f44; }}
.vram-chart {{ margin:20px 0; }}
.vram-bars {{ display:flex; align-items:flex-end; height:120px; gap:2px; padding:10px; background:#0d1117; border-radius:8px; }}
.vram-bar {{ flex:1; background:linear-gradient(180deg,#00d4ff,#0f3460); border-radius:2px 2px 0 0; min-height:2px; position:relative; }}
.vram-bar:hover::after {{ content:attr(data-val) 'MB'; position:absolute; bottom:100%; left:50%; transform:translateX(-50%); background:#333; padding:2px 6px; border-radius:4px; font-size:10px; white-space:nowrap; }}
.section-title {{ font-size:18px; font-weight:600; margin:30px 0 15px; color:#00d4ff; border-bottom:1px solid #333; padding-bottom:10px; }}
.hl-stats {{ display:grid; grid-template-columns:repeat(5,1fr); gap:15px; margin-bottom:20px; }}
.hl-card {{ background:#16213e; border-radius:12px; padding:15px; text-align:center; }}
.hl-card .avg {{ font-size:24px; font-weight:700; color:#00d4ff; }}
.hl-card .minmax {{ font-size:11px; color:#888; margin-top:5px; }}
.thumb {{ max-width:100px; max-height:100px; border-radius:4px; cursor:pointer; }}
.thumb:hover {{ transform:scale(2); transition:transform 0.3s; }}
</style>
</head>
<body>
<h1>📊 接口2 基准测试报告</h1>
<div class="subtitle">生成时间{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} {total} 次调用</div>
<div class="summary">
<div class="card ok"><div class="num">{success}</div><div class="label">成功</div></div>
<div class="card err"><div class="num">{failed}</div><div class="label">失败</div></div>
<div class="card time"><div class="num">{avg_time:.1f}s</div><div class="label">平均耗时</div></div>
<div class="card time"><div class="num">{min_time:.1f}s</div><div class="label">最快</div></div>
<div class="card time"><div class="num">{max_time:.1f}s</div><div class="label">最慢</div></div>
</div>
"""
# 按发际线类型统计
if hl_stats:
html += '<div class="section-title">按发际线类型统计</div><div class="hl-stats">'
for hl_label in ["椭圆形","花瓣形","心形","直线形","波浪形"]:
if hl_label in hl_stats:
s = hl_stats[hl_label]
times = s["times"]
avg = sum(times) / len(times)
mn, mx = min(times), max(times)
cls = hl_label
html += f'<div class="hl-card"><div class="hl-badge hl-{{cls}}">{hl_label}</div><div class="avg">{avg:.1f}s</div><div class="minmax">{mn:.1f}~{mx:.1f}s ({s["count"]}次)</div></div>'
else:
html += f'<div class="hl-card"><div class="hl-badge">{hl_label}</div><div class="avg">-</div><div class="minmax">未完成</div></div>'
html += '</div>'
# VRAM 变化图
if vram_data:
max_vram = max(v for _, v in vram_data if v > 0) or 1
html += '<div class="section-title">显存变化</div><div class="vram-chart"><div class="vram-bars">'
for i, (_, vram) in enumerate(vram_data):
if vram > 0:
h = int(vram / max_vram * 100)
html += f'<div class="vram-bar" style="height:{h}%" data-val="{vram}" title="{i+1}"></div>'
else:
html += f'<div class="vram-bar" style="height:0%" data-val="0"></div>'
html += '</div></div>'
# 详细结果表格
html += '<div class="section-title">详细结果</div><table><thead><tr>'
html += '<th>#</th><th>图片</th><th>发际线</th><th>总耗时</th><th>遮罩步骤</th>'
html += '<th>显存前</th><th>显存后</th><th>显存变化</th>'
html += '<th>预览图</th><th>生发图</th><th>状态</th>'
html += '</tr></thead><tbody>'
for i, r in enumerate(results):
hl_cls = r["hairline_key"]
status = '<span class="ok">✓ 成功</span>' if r["has_grown"] else f'<span class="fail">✗ {r.get("error","")}</span>'
step_ms = r.get("step_timing", {}).get("step1_ms", "")
step_str = f"{step_ms}ms" if step_ms else "-"
vram_d = r["vram_delta_mb"]
vram_d_str = f'<span style="color:{"#f44" if vram_d>0 else "#0f0"}">{"+" if vram_d>=0 else ""}{vram_d}</span>'
# 缩略图
thumb_grown = ""
if r["has_grown"]:
thumb_path = f"thumbs/{r['image']}_hl{r['hairline_id']}_grown.jpg"
if os.path.exists(os.path.join(OUT_DIR, thumb_path)):
thumb_grown = f'<img class="thumb" src="{thumb_path}">'
html += f"""<tr>
<td>{i+1}</td>
<td class="img-cell">{r['image']}</td>
<td><span class="hl-badge hl-{hl_cls}">{r['hairline_label']}</span></td>
<td><div class="time-bar" style="width:{min(r['total_time_s'],300)}px"></div> {r['total_time_s']:.1f}s</td>
<td>{step_str}</td>
<td>{r['vram_before_mb']}MB</td>
<td>{r['vram_after_mb']}MB</td>
<td>{vram_d_str}</td>
<td>{'' if r['has_preview'] else ''}</td>
<td>{thumb_grown if thumb_grown else ('' if r['has_grown'] else '')}</td>
<td>{status}</td>
</tr>"""
html += '</tbody></table>'
html += '</body></html>'
with open(REPORT_HTML, "w", encoding="utf-8") as f:
f.write(html)
if __name__ == "__main__":
run_benchmark()
-201
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@@ -1,201 +0,0 @@
#!/usr/bin/env python3
"""接口2女性5种发型对比测试:Plan B vs Plan B+。
测试 hair_style=1..5椭圆/花瓣/心形/直线/波浪每种发型一次记录耗时+显存+生成图
"""
import json, os, time, base64, subprocess, urllib.request, urllib.error
from datetime import datetime
API_BASE = "http://127.0.0.1:8187"
TOKEN = "dev-shared-secret-2026"
GIRL_IMG = "/home/ubuntu/hair/image/girl_img/girl13.jpg"
PLAN_TAG = os.getenv("PLAN_TAG", "Bplus") # Bplus / B
OUT_DIR = f"/home/ubuntu/hair/benchmark_out/iface2_female_{PLAN_TAG}"
RESULT_JSON = os.path.join(OUT_DIR, "results.json")
REPORT_HTML = os.path.join(OUT_DIR, "report.html")
HAIR_STYLE_NAMES = {
"1": "椭圆", "2": "花瓣", "3": "心形", "4": "直线", "5": "波浪",
}
os.makedirs(OUT_DIR, exist_ok=True)
def get_vram():
try:
out = subprocess.check_output(
["nvidia-smi", "--query-gpu=memory.used,memory.free",
"--format=csv,noheader,nounits"], text=True, timeout=5).strip()
used, free = out.split(",")
return int(used.strip()), int(free.strip())
except Exception:
return -1, -1
def _multipart(fields, files=None):
boundary = "----If2Test" + str(int(time.time() * 1000))
parts = []
for k, v in fields.items():
parts.append(f"--{boundary}\r\n".encode())
parts.append(f'Content-Disposition: form-data; name="{k}"\r\n\r\n'.encode())
parts.append(str(v).encode())
parts.append(b"\r\n")
if files:
for field_name, (filename, data, mime) in files.items():
parts.append(f"--{boundary}\r\n".encode())
parts.append(f'Content-Disposition: form-data; name="{field_name}"; filename="{filename}"\r\n'.encode())
parts.append(f"Content-Type: {mime}\r\n\r\n".encode())
parts.append(data)
parts.append(b"\r\n")
parts.append(f"--{boundary}--\r\n".encode())
return b"".join(parts), boundary
def call_iface2_female(image_path, hair_style):
"""接口2 女性 + 指定发型(走 swapHair 路径)"""
with open(image_path, "rb") as f:
img_data = f.read()
fields = {"gender": "female", "hair_style": str(hair_style),
"prompt": "填充遮罩区域的头发"}
body, boundary = _multipart(
fields, {"image_file": (os.path.basename(image_path), img_data, "image/jpeg")})
req = urllib.request.Request(f"{API_BASE}/api/v1/hair/grow", data=body, method="POST")
req.add_header("Content-Type", f"multipart/form-data; boundary={boundary}")
req.add_header("X-Internal-Token", TOKEN)
t0 = time.time()
try:
with urllib.request.urlopen(req, timeout=600) as resp:
data = json.loads(resp.read())
return data, time.time() - t0, None
except Exception as e:
return {}, time.time() - t0, str(e)
def run_test():
results = []
print(f"接口2女性5种发型测试 — Plan={PLAN_TAG}")
print(f"图片:{GIRL_IMG}\n")
for hs in range(1, 6):
name = HAIR_STYLE_NAMES[str(hs)]
print(f"\n[{hs}/5] hair_style={hs} ({name}) ...")
vram_before, vram_free_before = get_vram()
data, elapsed, error = call_iface2_female(GIRL_IMG, hs)
vram_after, vram_free_after = get_vram()
code = data.get("code", -1) if data else -1
ok = (code == 0)
# 保存生成图(如果有)
img_path = None
if ok and data.get("data"):
try:
items = data["data"].get("results") or []
if items and isinstance(items, list):
first = items[0]
b64 = first.get("grown_image_base64") or ""
if b64.startswith("data:"):
b64 = b64.split(",", 1)[1]
if b64:
img_path = os.path.join(OUT_DIR, f"hair_style_{hs}_{name}.jpg")
with open(img_path, "wb") as f:
f.write(base64.b64decode(b64))
except Exception as e:
print(f" 保存图片失败: {e}")
record = {
"plan": PLAN_TAG,
"hair_style": hs,
"hair_style_name": name,
"timestamp": datetime.now().strftime("%H:%M:%S"),
"elapsed_s": round(elapsed, 2),
"success": ok,
"code": code,
"error": error,
"vram_before_mb": vram_before,
"vram_after_mb": vram_after,
"vram_delta_mb": vram_after - vram_before,
"image_path": img_path,
}
results.append(record)
status = "" if ok else ""
print(f"{status} code={code} time={elapsed:.1f}s "
f"vram={vram_before}{vram_after}MB (Δ{vram_after-vram_before:+d}) "
f"img={'saved' if img_path else 'none'}")
with open(RESULT_JSON, "w") as f:
json.dump(results, f, indent=2, ensure_ascii=False)
generate_html(results)
generate_html(results)
print(f"\n✅ 完成!{RESULT_JSON}")
print(f"📄 报告:{REPORT_HTML}")
def generate_html(results):
total = len(results)
success = sum(1 for r in results if r["success"])
times = [r["elapsed_s"] for r in results if r["success"]]
avg_time = sum(times) / len(times) if times else 0
html = f"""<!DOCTYPE html>
<html lang="zh-CN"><head><meta charset="UTF-8"><title>接口2女5种发型 - Plan {PLAN_TAG}</title>
<style>
*{{margin:0;padding:0;box-sizing:border-box}}
body{{font-family:sans-serif;background:#1a1a2e;color:#e0e0e0;padding:20px}}
h1{{text-align:center;color:#00d4ff;margin-bottom:10px}}
.subtitle{{text-align:center;color:#888;margin-bottom:30px;font-size:14px}}
.summary{{display:grid;grid-template-columns:repeat(auto-fit,minmax(160px,1fr));gap:15px;margin-bottom:30px}}
.card{{background:#16213e;border-radius:12px;padding:20px;text-align:center;border:1px solid #333}}
.card .num{{font-size:28px;font-weight:700}}
.card .label{{font-size:12px;color:#888;margin-top:5px}}
.card.ok .num{{color:#0f0}} .card.err .num{{color:#f44}} .card.time .num{{color:#00d4ff}}
table{{width:100%;border-collapse:collapse;margin-bottom:30px;background:#16213e;border-radius:12px;overflow:hidden}}
th{{background:#0f3460;padding:10px 8px;text-align:center;font-size:13px;color:#fff}}
td{{padding:8px;text-align:center;border-bottom:1px solid #222;font-size:13px}}
tr:hover{{background:#1a1a3e}}
.section-title{{font-size:18px;font-weight:600;margin:30px 0 15px;color:#00d4ff;border-bottom:1px solid #333;padding-bottom:10px}}
.ok{{color:#0f0}} .fail{{color:#f44}}
.time-bar{{display:inline-block;height:18px;background:linear-gradient(90deg,#0f3460,#00d4ff);border-radius:3px;vertical-align:middle;min-width:2px}}
.gallery{{display:grid;grid-template-columns:repeat(auto-fit,minmax(200px,1fr));gap:15px}}
.gallery img{{width:100%;border-radius:8px;border:1px solid #333}}
.gallery .item{{text-align:center}}
.gallery .cap{{margin-top:5px;font-size:12px;color:#888}}
</style></head><body>
<h1>📊 接口2女性5种发型测试报告</h1>
<div class="subtitle">Plan {PLAN_TAG} {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} {total} 次调用</div>
<div class="summary">
<div class="card ok"><div class="num">{success}</div><div class="label">成功</div></div>
<div class="card err"><div class="num">{total-success}</div><div class="label">失败</div></div>
<div class="card time"><div class="num">{avg_time:.1f}s</div><div class="label">平均耗时</div></div>
</div>
<div class="section-title">详细结果</div>
<table><thead><tr><th>发型</th><th>名称</th><th>耗时</th><th>显存前</th><th>显存后</th><th>Δ</th><th>状态</th></tr></thead><tbody>
"""
for r in results:
status = '<span class="ok">✓</span>' if r["success"] else f'<span class="fail">✗ {str(r.get("error",""))[:30]}</span>'
html += f'<tr><td>style {r["hair_style"]}</td><td>{r["hair_style_name"]}</td>' \
f'<td><div class="time-bar" style="width:{min(r["elapsed_s"]*15,200)}px"></div> {r["elapsed_s"]:.1f}s</td>' \
f'<td>{r["vram_before_mb"]}MB</td><td>{r["vram_after_mb"]}MB</td>' \
f'<td>{r["vram_delta_mb"]:+d}</td><td>{status}</td></tr>'
html += '</tbody></table>'
# 图片画廊
saved = [r for r in results if r.get("image_path") and os.path.isfile(r["image_path"])]
if saved:
html += '<div class="section-title">生成图片</div><div class="gallery">'
for r in saved:
rel = os.path.relpath(r["image_path"], OUT_DIR)
html += f'<div class="item"><img src="{rel}"><div class="cap">{r["hair_style_name"]} ({r["elapsed_s"]:.1f}s)</div></div>'
html += '</div>'
html += '</body></html>'
with open(REPORT_HTML, "w", encoding="utf-8") as f:
f.write(html)
if __name__ == "__main__":
run_test()
-43
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nohup: ignoring input
9B vs 4B 对比测试 — Plan=A
图片: ['girl1.jpg', 'girl7.jpg', 'girl13.jpg']
发型: [(1, '椭圆'), (2, '花瓣'), (3, '心形'), (4, '直线'), (5, '波浪')]
总调用: 15
[warmup] 预热请求 (girl13, style=1) ...
warmup: 25.7s
[img1/3] girl1.jpg style=1(椭圆) ...
→ ✓ code=0 time=9.4s vram=26207→29449MB (Δ+3242) img=saved
[img1/3] girl1.jpg style=2(花瓣) ...
→ ✓ code=0 time=10.5s vram=29449→26341MB (Δ-3108) img=saved
[img1/3] girl1.jpg style=3(心形) ...
→ ✓ code=0 time=10.5s vram=26341→26051MB (Δ-290) img=saved
[img1/3] girl1.jpg style=4(直线) ...
→ ✓ code=0 time=10.4s vram=26051→26257MB (Δ+206) img=saved
[img1/3] girl1.jpg style=5(波浪) ...
→ ✓ code=0 time=10.8s vram=26257→26319MB (Δ+62) img=saved
[img2/3] girl7.jpg style=1(椭圆) ...
→ ✓ code=0 time=7.9s vram=26319→28417MB (Δ+2098) img=saved
[img2/3] girl7.jpg style=2(花瓣) ...
→ ✓ code=0 time=7.8s vram=28417→28397MB (Δ-20) img=saved
[img2/3] girl7.jpg style=3(心形) ...
→ ✓ code=0 time=7.7s vram=28397→28409MB (Δ+12) img=saved
[img2/3] girl7.jpg style=4(直线) ...
→ ✓ code=0 time=8.2s vram=28409→26335MB (Δ-2074) img=saved
[img2/3] girl7.jpg style=5(波浪) ...
→ ✓ code=0 time=7.9s vram=26335→28419MB (Δ+2084) img=saved
[img3/3] girl13.jpg style=1(椭圆) ...
→ ✓ code=0 time=8.9s vram=28419→28835MB (Δ+416) img=saved
[img3/3] girl13.jpg style=2(花瓣) ...
→ ✓ code=0 time=9.1s vram=28835→28817MB (Δ-18) img=saved
[img3/3] girl13.jpg style=3(心形) ...
→ ✓ code=0 time=8.8s vram=28817→28795MB (Δ-22) img=saved
[img3/3] girl13.jpg style=4(直线) ...
→ ✓ code=0 time=8.8s vram=28795→28845MB (Δ+50) img=saved
[img3/3] girl13.jpg style=5(波浪) ...
→ ✓ code=0 time=9.0s vram=28845→26291MB (Δ-2554) img=saved
✅ 完成!/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/results.json
📊 成功: 15/15
⏱ 平均: 9.06s
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-257
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[
{
"plan": "A",
"image_idx": 1,
"image_name": "girl1.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl1.jpg",
"hair_style": 1,
"hair_style_name": "椭圆",
"timestamp": "14:04:32",
"elapsed_s": 9.41,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 26207,
"vram_after_mb": 29449,
"vram_delta_mb": 3242,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/img1_style1.jpg"
},
{
"plan": "A",
"image_idx": 1,
"image_name": "girl1.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl1.jpg",
"hair_style": 2,
"hair_style_name": "花瓣",
"timestamp": "14:04:42",
"elapsed_s": 10.51,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 29449,
"vram_after_mb": 26341,
"vram_delta_mb": -3108,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/img1_style2.jpg"
},
{
"plan": "A",
"image_idx": 1,
"image_name": "girl1.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl1.jpg",
"hair_style": 3,
"hair_style_name": "心形",
"timestamp": "14:04:53",
"elapsed_s": 10.49,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 26341,
"vram_after_mb": 26051,
"vram_delta_mb": -290,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/img1_style3.jpg"
},
{
"plan": "A",
"image_idx": 1,
"image_name": "girl1.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl1.jpg",
"hair_style": 4,
"hair_style_name": "直线",
"timestamp": "14:05:03",
"elapsed_s": 10.45,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 26051,
"vram_after_mb": 26257,
"vram_delta_mb": 206,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/img1_style4.jpg"
},
{
"plan": "A",
"image_idx": 1,
"image_name": "girl1.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl1.jpg",
"hair_style": 5,
"hair_style_name": "波浪",
"timestamp": "14:05:14",
"elapsed_s": 10.85,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 26257,
"vram_after_mb": 26319,
"vram_delta_mb": 62,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/img1_style5.jpg"
},
{
"plan": "A",
"image_idx": 2,
"image_name": "girl7.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl7.jpg",
"hair_style": 1,
"hair_style_name": "椭圆",
"timestamp": "14:05:22",
"elapsed_s": 7.91,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 26319,
"vram_after_mb": 28417,
"vram_delta_mb": 2098,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/img2_style1.jpg"
},
{
"plan": "A",
"image_idx": 2,
"image_name": "girl7.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl7.jpg",
"hair_style": 2,
"hair_style_name": "花瓣",
"timestamp": "14:05:30",
"elapsed_s": 7.81,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 28417,
"vram_after_mb": 28397,
"vram_delta_mb": -20,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/img2_style2.jpg"
},
{
"plan": "A",
"image_idx": 2,
"image_name": "girl7.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl7.jpg",
"hair_style": 3,
"hair_style_name": "心形",
"timestamp": "14:05:38",
"elapsed_s": 7.74,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 28397,
"vram_after_mb": 28409,
"vram_delta_mb": 12,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/img2_style3.jpg"
},
{
"plan": "A",
"image_idx": 2,
"image_name": "girl7.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl7.jpg",
"hair_style": 4,
"hair_style_name": "直线",
"timestamp": "14:05:46",
"elapsed_s": 8.17,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 28409,
"vram_after_mb": 26335,
"vram_delta_mb": -2074,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/img2_style4.jpg"
},
{
"plan": "A",
"image_idx": 2,
"image_name": "girl7.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl7.jpg",
"hair_style": 5,
"hair_style_name": "波浪",
"timestamp": "14:05:54",
"elapsed_s": 7.92,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 26335,
"vram_after_mb": 28419,
"vram_delta_mb": 2084,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/img2_style5.jpg"
},
{
"plan": "A",
"image_idx": 3,
"image_name": "girl13.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl13.jpg",
"hair_style": 1,
"hair_style_name": "椭圆",
"timestamp": "14:06:03",
"elapsed_s": 8.95,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 28419,
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"vram_delta_mb": 416,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/img3_style1.jpg"
},
{
"plan": "A",
"image_idx": 3,
"image_name": "girl13.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl13.jpg",
"hair_style": 2,
"hair_style_name": "花瓣",
"timestamp": "14:06:12",
"elapsed_s": 9.07,
"success": true,
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"vram_delta_mb": -18,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/img3_style2.jpg"
},
{
"plan": "A",
"image_idx": 3,
"image_name": "girl13.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl13.jpg",
"hair_style": 3,
"hair_style_name": "心形",
"timestamp": "14:06:21",
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"success": true,
"code": 0,
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"vram_delta_mb": -22,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/img3_style3.jpg"
},
{
"plan": "A",
"image_idx": 3,
"image_name": "girl13.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl13.jpg",
"hair_style": 4,
"hair_style_name": "直线",
"timestamp": "14:06:30",
"elapsed_s": 8.84,
"success": true,
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"vram_after_mb": 28845,
"vram_delta_mb": 50,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/img3_style4.jpg"
},
{
"plan": "A",
"image_idx": 3,
"image_name": "girl13.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl13.jpg",
"hair_style": 5,
"hair_style_name": "波浪",
"timestamp": "14:06:39",
"elapsed_s": 8.96,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 28845,
"vram_after_mb": 26291,
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"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_A/img3_style5.jpg"
}
]
-43
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@@ -1,43 +0,0 @@
nohup: ignoring input
9B vs 4B 对比测试 — Plan=Bplus
图片: ['girl1.jpg', 'girl7.jpg', 'girl13.jpg']
发型: [(1, '椭圆'), (2, '花瓣'), (3, '心形'), (4, '直线'), (5, '波浪')]
总调用: 15
[warmup] 预热请求 (girl13, style=1) ...
warmup: 12.1s
[img1/3] girl1.jpg style=1(椭圆) ...
→ ✓ code=0 time=7.7s vram=25531→28691MB (Δ+3160) img=saved
[img1/3] girl1.jpg style=2(花瓣) ...
→ ✓ code=0 time=8.8s vram=28691→28655MB (Δ-36) img=saved
[img1/3] girl1.jpg style=3(心形) ...
→ ✓ code=0 time=9.1s vram=28655→28651MB (Δ-4) img=saved
[img1/3] girl1.jpg style=4(直线) ...
→ ✓ code=0 time=8.7s vram=28651→28641MB (Δ-10) img=saved
[img1/3] girl1.jpg style=5(波浪) ...
→ ✓ code=0 time=8.8s vram=28641→28637MB (Δ-4) img=saved
[img2/3] girl7.jpg style=1(椭圆) ...
→ ✓ code=0 time=7.0s vram=28637→25539MB (Δ-3098) img=saved
[img2/3] girl7.jpg style=2(花瓣) ...
→ ✓ code=0 time=6.7s vram=25539→27599MB (Δ+2060) img=saved
[img2/3] girl7.jpg style=3(心形) ...
→ ✓ code=0 time=6.7s vram=27599→27597MB (Δ-2) img=saved
[img2/3] girl7.jpg style=4(直线) ...
→ ✓ code=0 time=7.0s vram=27597→25533MB (Δ-2064) img=saved
[img2/3] girl7.jpg style=5(波浪) ...
→ ✓ code=0 time=6.7s vram=25533→27585MB (Δ+2052) img=saved
[img3/3] girl13.jpg style=1(椭圆) ...
→ ✓ code=0 time=7.6s vram=27585→28023MB (Δ+438) img=saved
[img3/3] girl13.jpg style=2(花瓣) ...
→ ✓ code=0 time=7.8s vram=28023→27997MB (Δ-26) img=saved
[img3/3] girl13.jpg style=3(心形) ...
→ ✓ code=0 time=7.8s vram=27997→25557MB (Δ-2440) img=saved
[img3/3] girl13.jpg style=4(直线) ...
→ ✓ code=0 time=7.6s vram=25557→28011MB (Δ+2454) img=saved
[img3/3] girl13.jpg style=5(波浪) ...
→ ✓ code=0 time=7.8s vram=28011→28017MB (Δ+6) img=saved
✅ 完成!/home/ubuntu/hair/benchmark_out/9b_vs_4b_Bplus/results.json
📊 成功: 15/15
⏱ 平均: 7.72s
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-257
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[
{
"plan": "Bplus",
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"image_name": "girl1.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl1.jpg",
"hair_style": 1,
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},
{
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},
{
"plan": "Bplus",
"image_idx": 1,
"image_name": "girl1.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl1.jpg",
"hair_style": 3,
"hair_style_name": "心形",
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"vram_after_mb": 28651,
"vram_delta_mb": -4,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_Bplus/img1_style3.jpg"
},
{
"plan": "Bplus",
"image_idx": 1,
"image_name": "girl1.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl1.jpg",
"hair_style": 4,
"hair_style_name": "直线",
"timestamp": "14:08:46",
"elapsed_s": 8.73,
"success": true,
"code": 0,
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"vram_before_mb": 28651,
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"vram_delta_mb": -10,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_Bplus/img1_style4.jpg"
},
{
"plan": "Bplus",
"image_idx": 1,
"image_name": "girl1.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl1.jpg",
"hair_style": 5,
"hair_style_name": "波浪",
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"code": 0,
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"vram_before_mb": 28641,
"vram_after_mb": 28637,
"vram_delta_mb": -4,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_Bplus/img1_style5.jpg"
},
{
"plan": "Bplus",
"image_idx": 2,
"image_name": "girl7.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl7.jpg",
"hair_style": 1,
"hair_style_name": "椭圆",
"timestamp": "14:09:02",
"elapsed_s": 7.04,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 28637,
"vram_after_mb": 25539,
"vram_delta_mb": -3098,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_Bplus/img2_style1.jpg"
},
{
"plan": "Bplus",
"image_idx": 2,
"image_name": "girl7.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl7.jpg",
"hair_style": 2,
"hair_style_name": "花瓣",
"timestamp": "14:09:09",
"elapsed_s": 6.67,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 25539,
"vram_after_mb": 27599,
"vram_delta_mb": 2060,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_Bplus/img2_style2.jpg"
},
{
"plan": "Bplus",
"image_idx": 2,
"image_name": "girl7.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl7.jpg",
"hair_style": 3,
"hair_style_name": "心形",
"timestamp": "14:09:16",
"elapsed_s": 6.73,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 27599,
"vram_after_mb": 27597,
"vram_delta_mb": -2,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_Bplus/img2_style3.jpg"
},
{
"plan": "Bplus",
"image_idx": 2,
"image_name": "girl7.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl7.jpg",
"hair_style": 4,
"hair_style_name": "直线",
"timestamp": "14:09:23",
"elapsed_s": 6.96,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 27597,
"vram_after_mb": 25533,
"vram_delta_mb": -2064,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_Bplus/img2_style4.jpg"
},
{
"plan": "Bplus",
"image_idx": 2,
"image_name": "girl7.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl7.jpg",
"hair_style": 5,
"hair_style_name": "波浪",
"timestamp": "14:09:29",
"elapsed_s": 6.74,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 25533,
"vram_after_mb": 27585,
"vram_delta_mb": 2052,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_Bplus/img2_style5.jpg"
},
{
"plan": "Bplus",
"image_idx": 3,
"image_name": "girl13.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl13.jpg",
"hair_style": 1,
"hair_style_name": "椭圆",
"timestamp": "14:09:37",
"elapsed_s": 7.62,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 27585,
"vram_after_mb": 28023,
"vram_delta_mb": 438,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_Bplus/img3_style1.jpg"
},
{
"plan": "Bplus",
"image_idx": 3,
"image_name": "girl13.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl13.jpg",
"hair_style": 2,
"hair_style_name": "花瓣",
"timestamp": "14:09:45",
"elapsed_s": 7.84,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 28023,
"vram_after_mb": 27997,
"vram_delta_mb": -26,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_Bplus/img3_style2.jpg"
},
{
"plan": "Bplus",
"image_idx": 3,
"image_name": "girl13.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl13.jpg",
"hair_style": 3,
"hair_style_name": "心形",
"timestamp": "14:09:53",
"elapsed_s": 7.83,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 27997,
"vram_after_mb": 25557,
"vram_delta_mb": -2440,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_Bplus/img3_style3.jpg"
},
{
"plan": "Bplus",
"image_idx": 3,
"image_name": "girl13.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl13.jpg",
"hair_style": 4,
"hair_style_name": "直线",
"timestamp": "14:10:00",
"elapsed_s": 7.59,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 25557,
"vram_after_mb": 28011,
"vram_delta_mb": 2454,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_Bplus/img3_style4.jpg"
},
{
"plan": "Bplus",
"image_idx": 3,
"image_name": "girl13.jpg",
"image_path": "/home/ubuntu/hair/image/girl_img/girl13.jpg",
"hair_style": 5,
"hair_style_name": "波浪",
"timestamp": "14:10:08",
"elapsed_s": 7.77,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 28011,
"vram_after_mb": 28017,
"vram_delta_mb": 6,
"saved_image_path": "/home/ubuntu/hair/benchmark_out/9b_vs_4b_Bplus/img3_style5.jpg"
}
]
File diff suppressed because one or more lines are too long
-292
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@@ -1,292 +0,0 @@
nohup: ignoring input
找到 19 张图片
总计 95 次调用,已完成 0,剩余 95
[1/19] girl1.jpg → 椭圆形(1) ...
→ HTTP code=0 time=11.8s vram=22353→22353MB preview=✓ grown=✓
[1/19] girl1.jpg → 花瓣形(2) ...
→ HTTP code=0 time=13.7s vram=22353→22389MB preview=✓ grown=✓
[1/19] girl1.jpg → 心形(3) ...
→ HTTP code=0 time=13.6s vram=22389→22375MB preview=✓ grown=✓
[1/19] girl1.jpg → 直线形(4) ...
→ HTTP code=0 time=14.0s vram=22375→22359MB preview=✓ grown=✓
[1/19] girl1.jpg → 波浪形(5) ...
→ HTTP code=0 time=13.6s vram=22359→22393MB preview=✓ grown=✓
[2/19] girl10.jpg → 椭圆形(1) ...
→ HTTP code=0 time=9.6s vram=22393→22413MB preview=✓ grown=✓
[2/19] girl10.jpg → 花瓣形(2) ...
→ HTTP code=0 time=9.8s vram=22413→22397MB preview=✓ grown=✓
[2/19] girl10.jpg → 心形(3) ...
→ HTTP code=0 time=9.8s vram=22397→22391MB preview=✓ grown=✓
[2/19] girl10.jpg → 直线形(4) ...
→ HTTP code=0 time=9.8s vram=22391→22393MB preview=✓ grown=✓
[2/19] girl10.jpg → 波浪形(5) ...
→ HTTP code=0 time=9.8s vram=22393→22395MB preview=✓ grown=✓
[3/19] girl11.jpg → 椭圆形(1) ...
→ HTTP code=0 time=10.0s vram=22395→22421MB preview=✓ grown=✓
[3/19] girl11.jpg → 花瓣形(2) ...
→ HTTP code=0 time=10.3s vram=22421→22393MB preview=✓ grown=✓
[3/19] girl11.jpg → 心形(3) ...
→ HTTP code=0 time=10.4s vram=22393→22375MB preview=✓ grown=✓
[3/19] girl11.jpg → 直线形(4) ...
→ HTTP code=0 time=10.4s vram=22375→22385MB preview=✓ grown=✓
[3/19] girl11.jpg → 波浪形(5) ...
→ HTTP code=0 time=9.9s vram=22385→22389MB preview=✓ grown=✓
[4/19] girl12.jpg → 椭圆形(1) ...
→ HTTP code=0 time=13.0s vram=22389→22389MB preview=✓ grown=✓
[4/19] girl12.jpg → 花瓣形(2) ...
→ HTTP code=0 time=12.9s vram=22389→22385MB preview=✓ grown=✓
[4/19] girl12.jpg → 心形(3) ...
→ HTTP code=0 time=12.9s vram=22385→22389MB preview=✓ grown=✓
[4/19] girl12.jpg → 直线形(4) ...
→ HTTP code=0 time=13.0s vram=22389→22389MB preview=✓ grown=✓
[4/19] girl12.jpg → 波浪形(5) ...
→ HTTP code=0 time=13.3s vram=22389→22409MB preview=✓ grown=✓
[5/19] girl13.jpg → 椭圆形(1) ...
→ HTTP code=0 time=11.5s vram=22409→22365MB preview=✓ grown=✓
[5/19] girl13.jpg → 花瓣形(2) ...
→ HTTP code=0 time=11.2s vram=22365→22395MB preview=✓ grown=✓
[5/19] girl13.jpg → 心形(3) ...
→ HTTP code=0 time=11.2s vram=22395→22421MB preview=✓ grown=✓
[5/19] girl13.jpg → 直线形(4) ...
→ HTTP code=0 time=11.4s vram=22421→22409MB preview=✓ grown=✓
[5/19] girl13.jpg → 波浪形(5) ...
→ HTTP code=0 time=11.2s vram=22409→22431MB preview=✓ grown=✓
[6/19] girl14.jpg → 椭圆形(1) ...
→ HTTP code=0 time=11.5s vram=22431→22391MB preview=✓ grown=✓
[6/19] girl14.jpg → 花瓣形(2) ...
→ HTTP code=0 time=11.5s vram=22391→22375MB preview=✓ grown=✓
[6/19] girl14.jpg → 心形(3) ...
→ HTTP code=0 time=11.5s vram=22375→22409MB preview=✓ grown=✓
[6/19] girl14.jpg → 直线形(4) ...
→ HTTP code=0 time=11.5s vram=22409→22359MB preview=✓ grown=✓
[6/19] girl14.jpg → 波浪形(5) ...
→ HTTP code=0 time=11.5s vram=22359→22393MB preview=✓ grown=✓
[7/19] girl15.jpg → 椭圆形(1) ...
→ HTTP code=0 time=11.2s vram=22393→22385MB preview=✓ grown=✓
[7/19] girl15.jpg → 花瓣形(2) ...
→ HTTP code=0 time=11.3s vram=22385→22387MB preview=✓ grown=✓
[7/19] girl15.jpg → 心形(3) ...
→ HTTP code=0 time=11.4s vram=22387→22357MB preview=✓ grown=✓
[7/19] girl15.jpg → 直线形(4) ...
→ HTTP code=0 time=11.3s vram=22357→22383MB preview=✓ grown=✓
[7/19] girl15.jpg → 波浪形(5) ...
→ HTTP code=0 time=11.7s vram=22383→22387MB preview=✓ grown=✓
[8/19] girl16.jpg → 椭圆形(1) ...
→ HTTP code=0 time=11.2s vram=22387→22377MB preview=✓ grown=✓
[8/19] girl16.jpg → 花瓣形(2) ...
→ HTTP code=0 time=11.2s vram=22377→22359MB preview=✓ grown=✓
[8/19] girl16.jpg → 心形(3) ...
→ HTTP code=0 time=11.2s vram=22359→22347MB preview=✓ grown=✓
[8/19] girl16.jpg → 直线形(4) ...
→ HTTP code=0 time=11.3s vram=22347→22367MB preview=✓ grown=✓
[8/19] girl16.jpg → 波浪形(5) ...
→ HTTP code=0 time=11.4s vram=22367→22399MB preview=✓ grown=✓
[9/19] girl17.jpg → 椭圆形(1) ...
→ HTTP code=0 time=13.0s vram=22399→22403MB preview=✓ grown=✓
[9/19] girl17.jpg → 花瓣形(2) ...
→ HTTP code=0 time=13.0s vram=22403→22421MB preview=✓ grown=✓
[9/19] girl17.jpg → 心形(3) ...
→ HTTP code=0 time=13.1s vram=22421→22381MB preview=✓ grown=✓
[9/19] girl17.jpg → 直线形(4) ...
→ HTTP code=0 time=13.1s vram=22381→22419MB preview=✓ grown=✓
[9/19] girl17.jpg → 波浪形(5) ...
→ HTTP code=0 time=13.0s vram=22419→22399MB preview=✓ grown=✓
[10/19] girl18.jpg → 椭圆形(1) ...
→ HTTP code=0 time=11.6s vram=22399→22419MB preview=✓ grown=✓
[10/19] girl18.jpg → 花瓣形(2) ...
→ HTTP code=0 time=11.2s vram=22419→22383MB preview=✓ grown=✓
[10/19] girl18.jpg → 心形(3) ...
→ HTTP code=0 time=11.4s vram=22383→22409MB preview=✓ grown=✓
[10/19] girl18.jpg → 直线形(4) ...
→ HTTP code=0 time=11.4s vram=22409→22375MB preview=✓ grown=✓
[10/19] girl18.jpg → 波浪形(5) ...
→ HTTP code=0 time=11.3s vram=22375→22413MB preview=✓ grown=✓
[11/19] girl19.jpg → 椭圆形(1) ...
→ HTTP code=0 time=11.1s vram=22413→22385MB preview=✓ grown=✓
[11/19] girl19.jpg → 花瓣形(2) ...
→ HTTP code=0 time=11.1s vram=22385→22393MB preview=✓ grown=✓
[11/19] girl19.jpg → 心形(3) ...
→ HTTP code=0 time=11.3s vram=22393→22387MB preview=✓ grown=✓
[11/19] girl19.jpg → 直线形(4) ...
→ HTTP code=0 time=11.2s vram=22387→22415MB preview=✓ grown=✓
[11/19] girl19.jpg → 波浪形(5) ...
→ HTTP code=0 time=11.2s vram=22415→22405MB preview=✓ grown=✓
[12/19] girl2.jpg → 椭圆形(1) ...
→ HTTP code=0 time=13.1s vram=22405→22395MB preview=✓ grown=✓
[12/19] girl2.jpg → 花瓣形(2) ...
→ HTTP code=0 time=12.9s vram=22395→22387MB preview=✓ grown=✓
[12/19] girl2.jpg → 心形(3) ...
→ HTTP code=0 time=12.9s vram=22387→22381MB preview=✓ grown=✓
[12/19] girl2.jpg → 直线形(4) ...
→ HTTP code=0 time=12.9s vram=22381→22387MB preview=✓ grown=✓
[12/19] girl2.jpg → 波浪形(5) ...
→ HTTP code=0 time=12.9s vram=22387→22309MB preview=✓ grown=✓
[13/19] girl3.jpg → 椭圆形(1) ...
→ HTTP code=0 time=9.8s vram=22309→22381MB preview=✓ grown=✓
[13/19] girl3.jpg → 花瓣形(2) ...
→ HTTP code=0 time=9.8s vram=22381→22403MB preview=✓ grown=✓
[13/19] girl3.jpg → 心形(3) ...
→ HTTP code=0 time=9.9s vram=22403→22419MB preview=✓ grown=✓
[13/19] girl3.jpg → 直线形(4) ...
→ HTTP code=0 time=9.9s vram=22419→22401MB preview=✓ grown=✓
[13/19] girl3.jpg → 波浪形(5) ...
→ HTTP code=0 time=9.9s vram=22401→22385MB preview=✓ grown=✓
[14/19] girl4.jpg → 椭圆形(1) ...
→ HTTP code=0 time=10.2s vram=22385→22377MB preview=✓ grown=✓
[14/19] girl4.jpg → 花瓣形(2) ...
→ HTTP code=0 time=10.5s vram=22377→22387MB preview=✓ grown=✓
[14/19] girl4.jpg → 心形(3) ...
→ HTTP code=0 time=10.0s vram=22387→22365MB preview=✓ grown=✓
[14/19] girl4.jpg → 直线形(4) ...
→ HTTP code=0 time=9.9s vram=22365→22371MB preview=✓ grown=✓
[14/19] girl4.jpg → 波浪形(5) ...
→ HTTP code=0 time=10.0s vram=22371→22389MB preview=✓ grown=✓
[15/19] girl5.jpg → 椭圆形(1) ...
→ HTTP code=0 time=8.5s vram=22389→23903MB preview=✓ grown=✓
[15/19] girl5.jpg → 花瓣形(2) ...
→ HTTP code=0 time=8.6s vram=23903→23929MB preview=✓ grown=✓
[15/19] girl5.jpg → 心形(3) ...
→ HTTP code=0 time=8.6s vram=23929→23925MB preview=✓ grown=✓
[15/19] girl5.jpg → 直线形(4) ...
→ HTTP code=0 time=8.7s vram=23925→23931MB preview=✓ grown=✓
[15/19] girl5.jpg → 波浪形(5) ...
→ HTTP code=0 time=8.7s vram=23931→23951MB preview=✓ grown=✓
[16/19] girl6.jpg → 椭圆形(1) ...
→ HTTP code=0 time=8.4s vram=23951→23729MB preview=✓ grown=✓
[16/19] girl6.jpg → 花瓣形(2) ...
→ HTTP code=0 time=8.7s vram=23729→22377MB preview=✓ grown=✓
[16/19] girl6.jpg → 心形(3) ...
→ HTTP code=0 time=8.4s vram=22377→23685MB preview=✓ grown=✓
[16/19] girl6.jpg → 直线形(4) ...
→ HTTP code=0 time=8.3s vram=23685→23731MB preview=✓ grown=✓
[16/19] girl6.jpg → 波浪形(5) ...
→ HTTP code=0 time=8.4s vram=23731→23713MB preview=✓ grown=✓
[17/19] girl7.jpg → 椭圆形(1) ...
→ HTTP code=0 time=9.9s vram=23713→24481MB preview=✓ grown=✓
[17/19] girl7.jpg → 花瓣形(2) ...
→ HTTP code=0 time=10.1s vram=24481→24447MB preview=✓ grown=✓
[17/19] girl7.jpg → 心形(3) ...
→ HTTP code=0 time=10.0s vram=24447→24481MB preview=✓ grown=✓
[17/19] girl7.jpg → 直线形(4) ...
→ HTTP code=0 time=10.0s vram=24481→24469MB preview=✓ grown=✓
[17/19] girl7.jpg → 波浪形(5) ...
→ HTTP code=0 time=10.0s vram=24469→24475MB preview=✓ grown=✓
[18/19] girl8.jpg → 椭圆形(1) ...
→ HTTP code=0 time=11.4s vram=24475→24861MB preview=✓ grown=✓
[18/19] girl8.jpg → 花瓣形(2) ...
→ HTTP code=0 time=11.5s vram=24861→24823MB preview=✓ grown=✓
[18/19] girl8.jpg → 心形(3) ...
→ HTTP code=0 time=11.3s vram=24823→22393MB preview=✓ grown=✓
[18/19] girl8.jpg → 直线形(4) ...
→ HTTP code=0 time=11.1s vram=22393→22389MB preview=✓ grown=✓
[18/19] girl8.jpg → 波浪形(5) ...
→ HTTP code=0 time=11.1s vram=22389→22365MB preview=✓ grown=✓
[19/19] girl9.jpg → 椭圆形(1) ...
→ HTTP code=0 time=11.9s vram=22365→22391MB preview=✓ grown=✓
[19/19] girl9.jpg → 花瓣形(2) ...
→ HTTP code=0 time=11.9s vram=22391→22375MB preview=✓ grown=✓
[19/19] girl9.jpg → 心形(3) ...
→ HTTP code=0 time=11.9s vram=22375→22391MB preview=✓ grown=✓
[19/19] girl9.jpg → 直线形(4) ...
→ HTTP code=0 time=11.9s vram=22391→22371MB preview=✓ grown=✓
[19/19] girl9.jpg → 波浪形(5) ...
→ HTTP code=0 time=11.8s vram=22371→22373MB preview=✓ grown=✓
✅ 完成!结果: /home/ubuntu/hair/benchmark_out/benchmark_results.json
📄 报告: /home/ubuntu/hair/benchmark_out/benchmark_report.html
📊 总调用: 95/95
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接口2女性5种发型测试 — Plan=B
图片:/home/ubuntu/hair/image/girl_img/girl13.jpg
[1/5] hair_style=1 (椭圆) ...
→ ✓ code=0 time=8.7s vram=24981→25025MB (Δ+44) img=saved
[2/5] hair_style=2 (花瓣) ...
→ ✓ code=0 time=8.4s vram=25025→27451MB (Δ+2426) img=saved
[3/5] hair_style=3 (心形) ...
→ ✓ code=0 time=8.4s vram=27451→27431MB (Δ-20) img=saved
[4/5] hair_style=4 (直线) ...
→ ✓ code=0 time=8.5s vram=27431→27433MB (Δ+2) img=saved
[5/5] hair_style=5 (波浪) ...
→ ✓ code=0 time=8.4s vram=27433→27407MB (Δ-26) img=saved
✅ 完成!/home/ubuntu/hair/benchmark_out/iface2_female_B/results.json
📄 报告:/home/ubuntu/hair/benchmark_out/iface2_female_B/report.html
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<!DOCTYPE html>
<html lang="zh-CN"><head><meta charset="UTF-8"><title>接口2女5种发型 - Plan B</title>
<style>
*{margin:0;padding:0;box-sizing:border-box}
body{font-family:sans-serif;background:#1a1a2e;color:#e0e0e0;padding:20px}
h1{text-align:center;color:#00d4ff;margin-bottom:10px}
.subtitle{text-align:center;color:#888;margin-bottom:30px;font-size:14px}
.summary{display:grid;grid-template-columns:repeat(auto-fit,minmax(160px,1fr));gap:15px;margin-bottom:30px}
.card{background:#16213e;border-radius:12px;padding:20px;text-align:center;border:1px solid #333}
.card .num{font-size:28px;font-weight:700}
.card .label{font-size:12px;color:#888;margin-top:5px}
.card.ok .num{color:#0f0} .card.err .num{color:#f44} .card.time .num{color:#00d4ff}
table{width:100%;border-collapse:collapse;margin-bottom:30px;background:#16213e;border-radius:12px;overflow:hidden}
th{background:#0f3460;padding:10px 8px;text-align:center;font-size:13px;color:#fff}
td{padding:8px;text-align:center;border-bottom:1px solid #222;font-size:13px}
tr:hover{background:#1a1a3e}
.section-title{font-size:18px;font-weight:600;margin:30px 0 15px;color:#00d4ff;border-bottom:1px solid #333;padding-bottom:10px}
.ok{color:#0f0} .fail{color:#f44}
.time-bar{display:inline-block;height:18px;background:linear-gradient(90deg,#0f3460,#00d4ff);border-radius:3px;vertical-align:middle;min-width:2px}
.gallery{display:grid;grid-template-columns:repeat(auto-fit,minmax(200px,1fr));gap:15px}
.gallery img{width:100%;border-radius:8px;border:1px solid #333}
.gallery .item{text-align:center}
.gallery .cap{margin-top:5px;font-size:12px;color:#888}
</style></head><body>
<h1>📊 接口2女性5种发型测试报告</h1>
<div class="subtitle">Plan B 2026-07-18 13:52:20 5 次调用</div>
<div class="summary">
<div class="card ok"><div class="num">5</div><div class="label">成功</div></div>
<div class="card err"><div class="num">0</div><div class="label">失败</div></div>
<div class="card time"><div class="num">8.5s</div><div class="label">平均耗时</div></div>
</div>
<div class="section-title">详细结果</div>
<table><thead><tr><th>发型</th><th>名称</th><th>耗时</th><th>显存前</th><th>显存后</th><th>Δ</th><th>状态</th></tr></thead><tbody>
<tr><td>style 1</td><td>椭圆</td><td><div class="time-bar" style="width:130.65px"></div> 8.7s</td><td>24981MB</td><td>25025MB</td><td>+44</td><td><span class="ok"></span></td></tr><tr><td>style 2</td><td>花瓣</td><td><div class="time-bar" style="width:125.54999999999998px"></div> 8.4s</td><td>25025MB</td><td>27451MB</td><td>+2426</td><td><span class="ok"></span></td></tr><tr><td>style 3</td><td>心形</td><td><div class="time-bar" style="width:125.54999999999998px"></div> 8.4s</td><td>27451MB</td><td>27431MB</td><td>-20</td><td><span class="ok"></span></td></tr><tr><td>style 4</td><td>直线</td><td><div class="time-bar" style="width:127.5px"></div> 8.5s</td><td>27431MB</td><td>27433MB</td><td>+2</td><td><span class="ok"></span></td></tr><tr><td>style 5</td><td>波浪</td><td><div class="time-bar" style="width:126.0px"></div> 8.4s</td><td>27433MB</td><td>27407MB</td><td>-26</td><td><span class="ok"></span></td></tr></tbody></table><div class="section-title">生成图片</div><div class="gallery"><div class="item"><img src="hair_style_1_椭圆.jpg"><div class="cap">椭圆 (8.7s)</div></div><div class="item"><img src="hair_style_2_花瓣.jpg"><div class="cap">花瓣 (8.4s)</div></div><div class="item"><img src="hair_style_3_心形.jpg"><div class="cap">心形 (8.4s)</div></div><div class="item"><img src="hair_style_4_直线.jpg"><div class="cap">直线 (8.5s)</div></div><div class="item"><img src="hair_style_5_波浪.jpg"><div class="cap">波浪 (8.4s)</div></div></div></body></html>
@@ -1,72 +0,0 @@
[
{
"plan": "B",
"hair_style": 1,
"hair_style_name": "椭圆",
"timestamp": "13:51:46",
"elapsed_s": 8.71,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 24981,
"vram_after_mb": 25025,
"vram_delta_mb": 44,
"image_path": "/home/ubuntu/hair/benchmark_out/iface2_female_B/hair_style_1_椭圆.jpg"
},
{
"plan": "B",
"hair_style": 2,
"hair_style_name": "花瓣",
"timestamp": "13:51:55",
"elapsed_s": 8.37,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 25025,
"vram_after_mb": 27451,
"vram_delta_mb": 2426,
"image_path": "/home/ubuntu/hair/benchmark_out/iface2_female_B/hair_style_2_花瓣.jpg"
},
{
"plan": "B",
"hair_style": 3,
"hair_style_name": "心形",
"timestamp": "13:52:03",
"elapsed_s": 8.37,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 27451,
"vram_after_mb": 27431,
"vram_delta_mb": -20,
"image_path": "/home/ubuntu/hair/benchmark_out/iface2_female_B/hair_style_3_心形.jpg"
},
{
"plan": "B",
"hair_style": 4,
"hair_style_name": "直线",
"timestamp": "13:52:11",
"elapsed_s": 8.5,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 27431,
"vram_after_mb": 27433,
"vram_delta_mb": 2,
"image_path": "/home/ubuntu/hair/benchmark_out/iface2_female_B/hair_style_4_直线.jpg"
},
{
"plan": "B",
"hair_style": 5,
"hair_style_name": "波浪",
"timestamp": "13:52:20",
"elapsed_s": 8.4,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 27433,
"vram_after_mb": 27407,
"vram_delta_mb": -26,
"image_path": "/home/ubuntu/hair/benchmark_out/iface2_female_B/hair_style_5_波浪.jpg"
}
]
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nohup: ignoring input
接口2女性5种发型测试 — Plan=Bplus
图片:/home/ubuntu/hair/image/girl_img/girl13.jpg
[1/5] hair_style=1 (椭圆) ...
→ ✓ code=0 time=7.9s vram=25875→25889MB (Δ+14) img=saved
[2/5] hair_style=2 (花瓣) ...
→ ✓ code=0 time=8.0s vram=25889→28337MB (Δ+2448) img=saved
[3/5] hair_style=3 (心形) ...
→ ✓ code=0 time=7.8s vram=28337→28355MB (Δ+18) img=saved
[4/5] hair_style=4 (直线) ...
→ ✓ code=0 time=7.6s vram=28355→28333MB (Δ-22) img=saved
[5/5] hair_style=5 (波浪) ...
→ ✓ code=0 time=8.3s vram=28333→25885MB (Δ-2448) img=saved
✅ 完成!/home/ubuntu/hair/benchmark_out/iface2_female_Bplus/results.json
📄 报告:/home/ubuntu/hair/benchmark_out/iface2_female_Bplus/report.html
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<!DOCTYPE html>
<html lang="zh-CN"><head><meta charset="UTF-8"><title>接口2女5种发型 - Plan Bplus</title>
<style>
*{margin:0;padding:0;box-sizing:border-box}
body{font-family:sans-serif;background:#1a1a2e;color:#e0e0e0;padding:20px}
h1{text-align:center;color:#00d4ff;margin-bottom:10px}
.subtitle{text-align:center;color:#888;margin-bottom:30px;font-size:14px}
.summary{display:grid;grid-template-columns:repeat(auto-fit,minmax(160px,1fr));gap:15px;margin-bottom:30px}
.card{background:#16213e;border-radius:12px;padding:20px;text-align:center;border:1px solid #333}
.card .num{font-size:28px;font-weight:700}
.card .label{font-size:12px;color:#888;margin-top:5px}
.card.ok .num{color:#0f0} .card.err .num{color:#f44} .card.time .num{color:#00d4ff}
table{width:100%;border-collapse:collapse;margin-bottom:30px;background:#16213e;border-radius:12px;overflow:hidden}
th{background:#0f3460;padding:10px 8px;text-align:center;font-size:13px;color:#fff}
td{padding:8px;text-align:center;border-bottom:1px solid #222;font-size:13px}
tr:hover{background:#1a1a3e}
.section-title{font-size:18px;font-weight:600;margin:30px 0 15px;color:#00d4ff;border-bottom:1px solid #333;padding-bottom:10px}
.ok{color:#0f0} .fail{color:#f44}
.time-bar{display:inline-block;height:18px;background:linear-gradient(90deg,#0f3460,#00d4ff);border-radius:3px;vertical-align:middle;min-width:2px}
.gallery{display:grid;grid-template-columns:repeat(auto-fit,minmax(200px,1fr));gap:15px}
.gallery img{width:100%;border-radius:8px;border:1px solid #333}
.gallery .item{text-align:center}
.gallery .cap{margin-top:5px;font-size:12px;color:#888}
</style></head><body>
<h1>📊 接口2女性5种发型测试报告</h1>
<div class="subtitle">Plan Bplus 2026-07-18 13:50:43 5 次调用</div>
<div class="summary">
<div class="card ok"><div class="num">5</div><div class="label">成功</div></div>
<div class="card err"><div class="num">0</div><div class="label">失败</div></div>
<div class="card time"><div class="num">7.9s</div><div class="label">平均耗时</div></div>
</div>
<div class="section-title">详细结果</div>
<table><thead><tr><th>发型</th><th>名称</th><th>耗时</th><th>显存前</th><th>显存后</th><th>Δ</th><th>状态</th></tr></thead><tbody>
<tr><td>style 1</td><td>椭圆</td><td><div class="time-bar" style="width:119.10000000000001px"></div> 7.9s</td><td>25875MB</td><td>25889MB</td><td>+14</td><td><span class="ok"></span></td></tr><tr><td>style 2</td><td>花瓣</td><td><div class="time-bar" style="width:120.6px"></div> 8.0s</td><td>25889MB</td><td>28337MB</td><td>+2448</td><td><span class="ok"></span></td></tr><tr><td>style 3</td><td>心形</td><td><div class="time-bar" style="width:116.55px"></div> 7.8s</td><td>28337MB</td><td>28355MB</td><td>+18</td><td><span class="ok"></span></td></tr><tr><td>style 4</td><td>直线</td><td><div class="time-bar" style="width:114.6px"></div> 7.6s</td><td>28355MB</td><td>28333MB</td><td>-22</td><td><span class="ok"></span></td></tr><tr><td>style 5</td><td>波浪</td><td><div class="time-bar" style="width:123.89999999999999px"></div> 8.3s</td><td>28333MB</td><td>25885MB</td><td>-2448</td><td><span class="ok"></span></td></tr></tbody></table><div class="section-title">生成图片</div><div class="gallery"><div class="item"><img src="hair_style_1_椭圆.jpg"><div class="cap">椭圆 (7.9s)</div></div><div class="item"><img src="hair_style_2_花瓣.jpg"><div class="cap">花瓣 (8.0s)</div></div><div class="item"><img src="hair_style_3_心形.jpg"><div class="cap">心形 (7.8s)</div></div><div class="item"><img src="hair_style_4_直线.jpg"><div class="cap">直线 (7.6s)</div></div><div class="item"><img src="hair_style_5_波浪.jpg"><div class="cap">波浪 (8.3s)</div></div></div></body></html>
@@ -1,72 +0,0 @@
[
{
"plan": "Bplus",
"hair_style": 1,
"hair_style_name": "椭圆",
"timestamp": "13:50:11",
"elapsed_s": 7.94,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 25875,
"vram_after_mb": 25889,
"vram_delta_mb": 14,
"image_path": "/home/ubuntu/hair/benchmark_out/iface2_female_Bplus/hair_style_1_椭圆.jpg"
},
{
"plan": "Bplus",
"hair_style": 2,
"hair_style_name": "花瓣",
"timestamp": "13:50:19",
"elapsed_s": 8.04,
"success": true,
"code": 0,
"error": null,
"vram_before_mb": 25889,
"vram_after_mb": 28337,
"vram_delta_mb": 2448,
"image_path": "/home/ubuntu/hair/benchmark_out/iface2_female_Bplus/hair_style_2_花瓣.jpg"
},
{
"plan": "Bplus",
"hair_style": 3,
"hair_style_name": "心形",
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]
-87
View File
@@ -1,87 +0,0 @@
nohup: ignoring input
轮换测试:4 个接口 × 5 轮 = 20 次调用
测试图片:/home/ubuntu/hair/image/girl_img/girl13.jpg
============================================================
第 1/5 轮
============================================================
[1/5] 接口1-测量 ...
→ ✓ code=0 time=0.4s vram=25303→25425MB (Δ+122) ⚠模型换出!
[1/5] 接口2-女-椭圆 ...
→ ✓ code=0 time=7.5s vram=25425→25887MB (Δ+462) ⚠模型换出!
[1/5] 接口2-男-椭圆 ...
→ ✓ code=0 time=4.1s vram=25887→28383MB (Δ+2496) ⚠模型换出!
[1/5] 接口3-B端生发 ...
→ ✓ code=0 time=3.1s vram=28383→28415MB (Δ+32) ⚠模型换出!
============================================================
第 2/5 轮
============================================================
[2/5] 接口1-测量 ...
→ ✓ code=0 time=0.1s vram=28415→28415MB (Δ+0)
[2/5] 接口2-女-椭圆 ...
→ ✓ code=0 time=7.5s vram=28415→28353MB (Δ-62) ⚠模型换出!
[2/5] 接口2-男-椭圆 ...
→ ✓ code=0 time=4.0s vram=28353→28353MB (Δ+0)
[2/5] 接口3-B端生发 ...
→ ✓ code=0 time=3.3s vram=28353→28353MB (Δ+0)
============================================================
第 3/5 轮
============================================================
[3/5] 接口1-测量 ...
→ ✓ code=0 time=0.1s vram=28353→28353MB (Δ+0)
[3/5] 接口2-女-椭圆 ...
→ ✓ code=0 time=7.6s vram=28353→25855MB (Δ-2498) ⚠模型换出!
[3/5] 接口2-男-椭圆 ...
→ ✓ code=0 time=4.1s vram=25855→28351MB (Δ+2496) ⚠模型换出!
[3/5] 接口3-B端生发 ...
→ ✓ code=0 time=3.1s vram=28351→28351MB (Δ+0)
============================================================
第 4/5 轮
============================================================
[4/5] 接口1-测量 ...
→ ✓ code=0 time=0.1s vram=28351→28351MB (Δ+0)
[4/5] 接口2-女-椭圆 ...
→ ✓ code=0 time=7.3s vram=28351→28337MB (Δ-14) ⚠模型换出!
[4/5] 接口2-男-椭圆 ...
→ ✓ code=0 time=4.2s vram=28337→28337MB (Δ+0)
[4/5] 接口3-B端生发 ...
→ ✓ code=0 time=3.2s vram=28337→28337MB (Δ+0)
============================================================
第 5/5 轮
============================================================
[5/5] 接口1-测量 ...
→ ✓ code=0 time=0.1s vram=28337→28337MB (Δ+0)
[5/5] 接口2-女-椭圆 ...
→ ✓ code=0 time=7.4s vram=28337→25855MB (Δ-2482) ⚠模型换出!
[5/5] 接口2-男-椭圆 ...
→ ✓ code=0 time=4.1s vram=25855→28351MB (Δ+2496) ⚠模型换出!
[5/5] 接口3-B端生发 ...
→ ✓ code=0 time=3.1s vram=28351→28383MB (Δ+32) ⚠模型换出!
✅ 完成!/home/ubuntu/hair/benchmark_out/rotation/rotation_results.json
📄 报告:/home/ubuntu/hair/benchmark_out/rotation/rotation_report.html
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