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>
This commit is contained in:
Cursor
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@@ -40,6 +40,18 @@ _REPO = os.path.dirname(os.path.dirname(__file__))
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_TEXTURE_DIR = os.path.join(_REPO, "hairline_texture")
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_BLACK_TEXTURE_DIR = os.path.join(_REPO, "hairline_texture_black")
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# 三接口（接口2女重绘 / 接口2男 / 接口3）统一的 ComfyUI 重绘 prompt。
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# 关键：ComfyUI 单卡显存装不下 Flux(7.7G)+qwen CLIP(3.9G) 同驻，靠缓存 CLIP 文本条件避免重载。
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# prompt 不同会使缓存失效 → 重载 CLIP 并挤出 Flux（每次 +4s）。三接口用同一字符串即可全程命中。
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# 与 app.py 接口2/接口3 的默认 prompt 保持一致；可用 REDRAW_PROMPT 覆盖。
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_REDRAW_PROMPT = os.getenv("REDRAW_PROMPT", "填充遮罩区域的头发，皮肤加一点磨皮")
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# 接口2 女重绘整条管线（swapHair + ComfyUI）送模型前限边。真实照片常达 1257x1495：
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# 全分辨率 ComfyUI 重绘要 13~21s 且激活显存把模型挤出。女性路径含 swapHair(SD WebUI ~5.3s
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# 固定地板) + ComfyUI 两段串行。用 1024 优先画质（女性总耗时 ~11~12s，偶尔踩 12s）；
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# 若要更稳的 <12s 余量可设 REDRAW_MAX_SIDE=896（ComfyUI ~4s，女性 9~11s）。
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_REDRAW_MAX_SIDE = int(os.getenv("REDRAW_MAX_SIDE", "1024"))
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def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0):
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"""直接调 ComfyUI 重绘（替代原 local_test HTTP 服务）。
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@@ -47,7 +59,27 @@ def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0):
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失败抛异常（调用方负责 try/except 跳过）。
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"""
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from .redraw import run_redraw
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return run_redraw(image_png_bytes, mask_png_bytes, timeout=timeout)
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img = cv2.imdecode(np.frombuffer(image_png_bytes, np.uint8), cv2.IMREAD_UNCHANGED)
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scale = 1.0
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orig_w = orig_h = 0
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if img is not None:
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orig_h, orig_w = img.shape[:2]
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m = max(orig_h, orig_w)
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if _REDRAW_MAX_SIDE > 0 and m > _REDRAW_MAX_SIDE:
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scale = _REDRAW_MAX_SIDE / float(m)
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nw, nh = max(1, round(orig_w * scale)), max(1, round(orig_h * scale))
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msk = cv2.imdecode(np.frombuffer(mask_png_bytes, np.uint8), cv2.IMREAD_UNCHANGED)
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img_s = cv2.resize(img, (nw, nh), interpolation=cv2.INTER_AREA)
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msk_s = cv2.resize(msk, (nw, nh), interpolation=cv2.INTER_NEAREST)
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image_png_bytes = cv2.imencode(".png", img_s)[1].tobytes()
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mask_png_bytes = cv2.imencode(".png", msk_s)[1].tobytes()
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logger.info("接口2女 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
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orig_w, orig_h, nw, nh, _REDRAW_MAX_SIDE)
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out = run_redraw(image_png_bytes, mask_png_bytes, timeout=timeout,
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prompt=_REDRAW_PROMPT)
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if scale < 1.0 and out:
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out = _upscale_png_to(out, orig_w, orig_h)
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return out
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# 发际线贴图档位：middle=默认(hairline_texture/)，high/low 各自独立文件夹。
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_TEXTURE_DIRS = {
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@@ -190,9 +222,12 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
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if not use_mask:
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try:
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h, w = image_bgr.shape[:2]
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img_s, msk_s, gsc = _prep_comfy_input(image_bgr, np.zeros((h, w), np.uint8))
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buf = io.BytesIO()
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compose_comfy_rgba(image_bgr, np.zeros((h, w), np.uint8)).save(buf, format="PNG", compress_level=1)
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compose_comfy_rgba(img_s, msk_s).save(buf, format="PNG", compress_level=1)
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shared_grown = comfyui.run(buf.getvalue(), prompt=prompt, workflow_path=workflow_path)
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if gsc < 1.0 and shared_grown:
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shared_grown = _upscale_png_to(shared_grown, w, h)
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except Exception as e: # noqa: BLE001
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logger.warning("接口2 生发图失败(无遮罩)：%s", e)
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@@ -210,9 +245,12 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
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black = load_texture_rgba(_black_texture_path(white_path))
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marked, mask = build_inpaint_mask(
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image_bgr, ctx["landmarks"], ctx["parse_map"], ctx["points"], black)
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m_s, msk_s, gsc = _prep_comfy_input(marked, mask)
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buf = io.BytesIO()
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compose_comfy_rgba(marked, mask).save(buf, format="PNG", compress_level=1)
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compose_comfy_rgba(m_s, msk_s).save(buf, format="PNG", compress_level=1)
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grown_png = comfyui.run(buf.getvalue(), prompt=prompt, workflow_path=workflow_path)
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if gsc < 1.0 and grown_png:
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grown_png = _upscale_png_to(grown_png, w, h)
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except Exception as e: # noqa: BLE001 单张失败不拖垮整请求
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logger.warning("接口2 生发图失败 type=%s：%s", key, e)
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@@ -253,6 +291,20 @@ def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | N
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results = []
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h, w = image_bgr.shape[:2]
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# 重绘管线（swapHair + ComfyUI）统一降分辨率：真实照片 swap(SD WebUI)~5s、blend、ComfyUI
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# 均随分辨率线性下降。overlay 预览仍用全分辨率；grown_png 最后放大回原尺寸。
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redraw_img = image_bgr
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hair_mask_redraw = hair_mask_reuse
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if _REDRAW_MAX_SIDE > 0 and max(h, w) > _REDRAW_MAX_SIDE:
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redraw_img, _rs = _downscale_max_side(image_bgr, _REDRAW_MAX_SIDE)
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_nh, _nw = redraw_img.shape[:2]
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if hair_mask_redraw is not None:
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hair_mask_redraw = cv2.resize(hair_mask_reuse.astype(np.uint8), (_nw, _nh),
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interpolation=cv2.INTER_NEAREST).astype(bool)
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logger.info("接口2女 管线降分辨率: %dx%d → %dx%d (max_side=%d)",
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w, h, _nw, _nh, _REDRAW_MAX_SIDE)
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for order, (key, white_path) in items:
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white = load_texture_rgba(white_path)
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overlay = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
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@@ -263,7 +315,10 @@ def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | N
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logger.warning("接口2 换发型：female key=%s 无对应 chang_id，跳过生发图", key)
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else:
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try:
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data = generate_hairline_redraw(image_bgr, chang_id, hair_mask=hair_mask_reuse, **redraw_defaults)
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import time as _t
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_ts0 = _t.perf_counter()
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data = generate_hairline_redraw(redraw_img, chang_id, hair_mask=hair_mask_redraw, **redraw_defaults)
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_ts1 = _t.perf_counter()
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steps = data.get("steps") or {}
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# ④ final（接缝融合基底）+ ⑤-② 纯红遮罩 PNG
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final_b64 = steps.get("final_base64") or ""
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@@ -280,9 +335,19 @@ def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | N
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final_bytes = base64.b64decode(final_b64)
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mask_bytes = base64.b64decode(mask_b64)
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# 后端直接调 ComfyUI 重绘，返回重绘后的 PNG
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_tr0 = _t.perf_counter()
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grown_png = _call_local_redraw(final_bytes, mask_bytes)
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_tr1 = _t.perf_counter()
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_tm = data.get("timings_ms") or {}
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logger.info("接口2女 分段计时 type=%s: swapHair管线=%.2fs (mask=%dms swap=%dms blend=%dms), ComfyUI重绘=%.2fs",
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key, _ts1 - _ts0,
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_tm.get("mask", 0), _tm.get("swap", 0), _tm.get("blend", 0),
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_tr1 - _tr0)
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if grown_png is None:
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logger.warning("接口2 换发型：type=%s 重绘结果为空", key)
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elif redraw_img is not image_bgr:
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# 管线在降分辨率图上跑，结果放大回原尺寸
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grown_png = _upscale_png_to(grown_png, w, h)
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except NoFaceError:
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logger.warning("接口2 换发型：type=%s 未检出人脸", key)
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except Exception as e: # noqa: BLE001 单张失败不拖垮整请求
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@@ -377,18 +442,73 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
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return {"images": images, "best_centers": best_centers}
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# 接口3 送 ComfyUI 前限边，降低峰值显存，避免与接口2 切换时把 Flux 挤出。
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# 统一 prompt 后 Flux 不再被 CLIP 挤出，接口3 可用较高分辨率。可用 GROW_B_MAX_SIDE 覆盖。
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_GROW_B_MAX_SIDE = int(os.getenv("GROW_B_MAX_SIDE", "1024"))
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def _downscale_max_side(img_bgr: np.ndarray, max_side: int) -> tuple[np.ndarray, float]:
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"""长边超过 max_side 时等比例缩小；返回 (图, scale)，scale=新/旧。"""
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h, w = img_bgr.shape[:2]
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m = max(h, w)
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if max_side <= 0 or m <= max_side:
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return img_bgr, 1.0
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scale = max_side / float(m)
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nw = max(1, int(round(w * scale)))
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nh = max(1, int(round(h * scale)))
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out = cv2.resize(img_bgr, (nw, nh), interpolation=cv2.INTER_AREA)
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return out, scale
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def _upscale_png_to(png_bytes: bytes, out_w: int, out_h: int) -> bytes:
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"""把 Comfy 输出 PNG 双线性拉回原图尺寸（仅展示对齐，不增加推理细节）。"""
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arr = np.frombuffer(png_bytes, np.uint8)
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img = cv2.imdecode(arr, cv2.IMREAD_UNCHANGED)
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if img is None:
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return png_bytes
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if img.shape[1] == out_w and img.shape[0] == out_h:
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return png_bytes
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resized = cv2.resize(img, (out_w, out_h), interpolation=cv2.INTER_LINEAR)
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ok, buf = cv2.imencode(".png", resized)
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return buf.tobytes() if ok else png_bytes
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def _prep_comfy_input(img_bgr: np.ndarray, mask: np.ndarray) -> tuple[np.ndarray, np.ndarray, float]:
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"""单段 ComfyUI 生发（接口2男 / 接口3）送图前限边到 GROW_B_MAX_SIDE。
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返回 (缩后图, 缩后遮罩, scale)；scale<1 时调用方需把结果放大回原尺寸。"""
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h, w = img_bgr.shape[:2]
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if _GROW_B_MAX_SIDE <= 0 or max(h, w) <= _GROW_B_MAX_SIDE:
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return img_bgr, mask, 1.0
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out, scale = _downscale_max_side(img_bgr, _GROW_B_MAX_SIDE)
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nh, nw = out.shape[:2]
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msk = cv2.resize(mask, (nw, nh), interpolation=cv2.INTER_NEAREST)
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logger.info("接口2男/接口3 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
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w, h, nw, nh, _GROW_B_MAX_SIDE)
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return out, msk, scale
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def generate_grow_b(marked_bgr: np.ndarray, use_mask: bool = True, prompt: str = None):
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"""接口3：检测医生手绘发际线 → 遮罩 → 送 ComfyUI 生发（仅需划线图一张）。
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检测路径只用来**建遮罩**；ComfyUI 输入图用 **marked 原图**（含医生手绘线，
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工作流提示词会清除黑线再生发）。
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进 Comfy 前若长边 > GROW_B_MAX_SIDE（默认 896）会先等比例缩小，降低峰值显存；
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输出再拉回原图尺寸。
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use_mask（默认 True）：是否启用自动检测的遮罩，用于测试对比。
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- True：检测手绘线 → 建遮罩 → alpha=255−mask（透明区=重绘区，节点44 画黄色参考区）。
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- False：跳过检测，直接送划线图，alpha 全 255（空遮罩，节点26 mask 为空），
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模型仅凭医生黑线参考生发。无需改工作流，唯一变量是遮罩。
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Returns: {"grown_png": bytes 或 None, "status": "ok"|"no_face"|"no_line"}。
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"""
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orig_h, orig_w = marked_bgr.shape[:2]
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marked_bgr, _scale = _downscale_max_side(marked_bgr, _GROW_B_MAX_SIDE)
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if _scale < 1.0:
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logger.info(
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"接口3 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
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orig_w, orig_h, marked_bgr.shape[1], marked_bgr.shape[0], _GROW_B_MAX_SIDE,
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)
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h, w = marked_bgr.shape[:2]
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if use_mask:
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rgb = cv2.cvtColor(marked_bgr, cv2.COLOR_BGR2RGB)
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@@ -406,8 +526,10 @@ def generate_grow_b(marked_bgr: np.ndarray, use_mask: bool = True, prompt: str =
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mask = np.zeros((h, w), np.uint8) # 空遮罩：alpha 全 255，跳过检测
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buf = io.BytesIO()
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compose_comfy_rgba(marked_bgr, mask).save(buf, format="PNG", compress_level=1) # marked 原图 + 遮罩
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compose_comfy_rgba(marked_bgr, mask).save(buf, format="PNG", compress_level=1) # marked + 遮罩
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grown_png = comfyui.run(buf.getvalue(), prompt=prompt)
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if _scale < 1.0 and grown_png:
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grown_png = _upscale_png_to(grown_png, orig_w, orig_h)
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return {"grown_png": grown_png, "status": "ok"}
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