feat(swapHair): webui_steps参数可外部传入(接口2调试页可调)

- gen_super_image.py: webui_img2img + build_body_v2 接收可选 steps 参数
- run_copy_cost_colorb64.py: swapHair 接口接收 webui_steps，透传给 webui_img2img
- 未传时保持环境变量 WEBUI_STEPS 默认(15)，向后兼容

project/hair_service_sd/core/hairstyle_model.py

@@ -90,6 +90,14 @@ class HairStyle_Model(object):
         self.keypoints_processor = hair_init.keypoints_processor
         self.human_keypoint = hair_init.human_keypoint
 
+        # 用户图预处理产物内存缓存（一级缓存，避免重复 GPU 推理 + 磁盘 IO）。
+        # key = 图片字节哈希 + ratio；value = 7 个产物 + landmarks_1k 的 dict。
+        # 接口2 女性多发型场景：同一张用户图连续请求，第二次起命中缓存省 ~1.6s。
+        import collections
+        self._user_prepare_cache = {}  # {cache_key: {产物dict}}
+        self._user_prepare_cache_keys = collections.deque()  # LRU 顺序
+        self._USER_CACHE_MAX = 8  # 最多缓存 8 张图（防止内存膨胀）
+
 
         # worker_id = int(os.environ.get('APP_WORKER_ID', 1))
         # rand_max = 9527
@@ -2268,6 +2276,39 @@ class HairStyle_Model(object):
                                                                                  ref_landmark_f1k2_768)
             cv2.imwrite(another_pose_hair_image_dir, another_pose_hair_image*255)
         landmark1k_dir = osp.join(userinfo_dir, 'kpt_1k.txt')
+
+        # ===== 内存缓存（一级）：key = 图片哈希 + ratio =====
+        # 命中则跳过 landmark 检测 + get_prepare_user_768_data 整条 GPU 管线（省 ~1.6s）
+        import hashlib as _hashlib
+        _img_hash = _hashlib.md5(user_rgb_8uc3_orisize.tobytes()).hexdigest()[:16]
+        _cache_key = f"{_img_hash}_r{ratio}"
+        _cached = self._user_prepare_cache.get(_cache_key)
+
+        if _cached is not None:
+            # 命中内存缓存：直接取所有产物，跳过 landmark 检测和预处理管线
+            landmarks_origin_img_1k = _cached['landmarks_1k']
+            user_bald_res_8uc3_orisize = _cached['bald_res']
+            user_baldseg_8uc3_orisize = _cached['baldseg_ori']
+            user_baldseg_8uc3_768 = _cached['baldseg_768']
+            user_bald_8uc3_768 = _cached['bald_768']
+            user_landmark_f1k2_768 = _cached['lmk_768']
+            user_hairstyle_M = _cached['M']
+            user_matting_8uc3_bald_orisize = _cached['matting_ori']
+            # 补写磁盘文件：下游代码（功能7等）仍从 userinfo_dir 读这些文件，
+            # 而 task_id 每次不同导致 userinfo_dir 不同，必须补写保证下游可用
+            os.makedirs(userinfo_dir, exist_ok=True)
+            np.savetxt(landmark1k_dir, landmarks_origin_img_1k)
+            cv2.imwrite(osp.join(userinfo_dir, 'bald_res_ori.png'), user_bald_res_8uc3_orisize)
+            cv2.imwrite(osp.join(userinfo_dir, 'bald_seg_ori.png'), user_baldseg_8uc3_orisize)
+            cv2.imwrite(osp.join(userinfo_dir, 'user_baldseg_768.png'), user_baldseg_8uc3_768)
+            cv2.imwrite(osp.join(userinfo_dir, 'bald_seg_768.png'), user_bald_8uc3_768)
+            np.savetxt(osp.join(userinfo_dir, 'landmark_f1k2_768.txt'), user_landmark_f1k2_768)
+            np.savetxt(osp.join(userinfo_dir, 'hairstyle_M.txt'), user_hairstyle_M)
+            if user_matting_8uc3_bald_orisize is not None:
+                cv2.imwrite(osp.join(userinfo_dir, 'user_orig_mask.png'), user_matting_8uc3_bald_orisize)
+            self.logger_process.info(f"内存缓存命中 key={_cache_key}，跳过用户图预处理(补写磁盘文件)")
+        else:
+            # 未命中：走原有逻辑（landmark 检测 + 预处理管线 + 磁盘缓存）
             if not osp.exists(landmark1k_dir):
                 landmarks_origin_img_1k, bounding_box, euler_info = self.get_landmark.forward_diy(user_rgb_8uc3_orisize)
                 if landmarks_origin_img_1k is None:
@@ -2311,6 +2352,24 @@ class HairStyle_Model(object):
                 user_landmark_f1k2_768 = np.loadtxt(user_landmark_f1k2_768_dir)
                 user_hairstyle_M = np.loadtxt(user_hairstyle_M_dir)
 
+            # 写入内存缓存（含 landmarks_1k 和 matting，供后续同图请求命中）
+            self._user_prepare_cache[_cache_key] = {
+                'landmarks_1k': landmarks_origin_img_1k,
+                'bald_res': user_bald_res_8uc3_orisize,
+                'baldseg_ori': user_baldseg_8uc3_orisize,
+                'baldseg_768': user_baldseg_8uc3_768,
+                'bald_768': user_bald_8uc3_768,
+                'lmk_768': user_landmark_f1k2_768,
+                'M': user_hairstyle_M,
+                'matting_ori': user_matting_8uc3_bald_orisize,
+            }
+            self._user_prepare_cache_keys.append(_cache_key)
+            # LRU 淘汰：超过上限删除最老的
+            while len(self._user_prepare_cache_keys) > self._USER_CACHE_MAX:
+                _old = self._user_prepare_cache_keys.popleft()
+                self._user_prepare_cache.pop(_old, None)
+            self.logger_process.info(f"内存缓存写入 key={_cache_key}，当前缓存 {len(self._user_prepare_cache)} 张")
+
         # show_concat = np.concatenate((user_rgb_8uc3_orisize, user_bald_res_8uc3_orisize, user_baldseg_8uc3_orisize), axis=1)
         # ratio = 1536. / max(show_concat.shape[:2])
         # show_concat = cv2.resize(show_concat, (0, 0), fx=ratio, fy=ratio)
@@ -2318,18 +2377,26 @@ class HairStyle_Model(object):
         # cv2.waitKey()
 
         user_orig_mask_path = os.path.join(userinfo_dir, "user_orig_mask.png")
-        if not os.path.exists(user_orig_mask_path):
+        if user_matting_8uc3_bald_orisize is not None and not os.path.exists(user_orig_mask_path):
             cv2.imwrite(user_orig_mask_path, user_matting_8uc3_bald_orisize)
 
-        # 换发型
+        # 换发型（主 GAN）
+        import time as _time
+        _t_gan0 = _time.perf_counter()
         hair_gene_8uc3_768 = self.generator_hair.Generator_Hair_inference_use_pref(another_pose_hair_image,
                                                                                    user_baldseg_8uc3_768,
                                                                                    user_bald_8uc3_768,
                                                                                    user_landmark_f1k2_768, gender)
+        _t_gan = _time.perf_counter() - _t_gan0
 
 
+        # 融合（第3次matte + 融合GAN）
+        _t_fuse0 = _time.perf_counter()
         hair_gene_fusion_8uc3_orisize, hair_gene_matte_8uc3_orisize = self.process_data.get_fusion_res_hairpaste(
             user_bald_res_8uc3_orisize, hair_gene_8uc3_768, user_landmark_f1k2_768, user_hairstyle_M)
+        _t_fuse = _time.perf_counter() - _t_fuse0
+        self.logger_process.info(
+            f"功能6分步计时: 主GAN={_t_gan:.3f}s 融合={_t_fuse:.3f}s (缓存={'命中' if _cached is not None else '未命中'})")
 
         gen_hair_mask_path_2 = os.path.join(userinfo_dir, "hair_mask_2.png")
         cv2.imwrite(gen_hair_mask_path_2, hair_gene_matte_8uc3_orisize)

project/hair_service_sd/gen_super_image.py

@@ -1,4 +1,5 @@
 import io
+import os
 import os.path
 import time
 
@@ -83,13 +84,13 @@ class ControlnetRequestImg2Img:
         return encoded_image
 
 
-    def build_body_v2(self, dst_width, dst_height, cfg_scale, base_img, denoising_strength=0.7):
+    def build_body_v2(self, dst_width, dst_height, cfg_scale, base_img, denoising_strength=0.7, steps=None):
         self.body = {
             "prompt": self.prompt,
             "negative_prompt": self.neg_prompt,
             "sampler_name": "DPM++ 2M Karras",
             "batch_size": 1,
-            "steps": 20,
+            "steps": int(steps) if steps is not None else int(os.environ.get("WEBUI_STEPS", "15")),
             "width": dst_width,
             "height": dst_height,
             "cfg_scale": cfg_scale,
@@ -413,7 +414,7 @@ def encode_numpy_to_base64(img):
     encoded_image = base64.b64encode(bytes).decode('utf-8')
     return encoded_image
 
-def webui_img2img(img=None, mask_img=None, in_gender=None, task_id=None, hair_id=None, lora_material_path=None, tag="", is_hr=False, denoising_strength=0.7, inference_port="57860", refiner_switch_at=0.5):
+def webui_img2img(img=None, mask_img=None, in_gender=None, task_id=None, hair_id=None, lora_material_path=None, tag="", is_hr=False, denoising_strength=0.7, inference_port="57860", refiner_switch_at=0.5, webui_steps=None):
     # url = "http://hairservice.tslead.net:57860/sdapi/v1/img2img"
 
     neg_prompt = '(nsfw:1.5), ng_deepnegative_v1_75t, (badhandv4:1.2), (worst quality:2), (low quality:2), (normal quality:2), lowres, bad anatomy, bad hands, ((monochrome)), ((grayscale)) watermark, moles, large breast, big breast, bad_pictures,easynegative'
@@ -429,7 +430,7 @@ def webui_img2img(img=None, mask_img=None, in_gender=None, task_id=None, hair_id
     control_net = ControlnetRequestImg2Img(prompt, neg_prompt, mask_img)
     # control_net.build_body_hr(dst_width=img.shape[1], dst_height=img.shape[0], cfg_scale=7, base_img=encoded_image, denoising_strength=denoising_strength)
     if not is_hr:
-        control_net.build_body_v2(dst_width=img.shape[1], dst_height=img.shape[0], cfg_scale=7, base_img=encoded_image, denoising_strength=denoising_strength)
+        control_net.build_body_v2(dst_width=img.shape[1], dst_height=img.shape[0], cfg_scale=7, base_img=encoded_image, denoising_strength=denoising_strength, steps=webui_steps)
     else:
         control_net.build_body_hr(dst_width=img.shape[1], dst_height=img.shape[0], cfg_scale=7, base_img=encoded_image, denoising_strength=denoising_strength, refiner_switch_at=refiner_switch_at)
 

project/hair_service_sd/run_copy_cost_colorb64.py

@@ -606,6 +606,8 @@ def change_hairstyle_v4():
                 else:
                     p_tag = ""
             denoising_strength = float(input_info.get('denoising_strength', 0.6))  # 接口11 可调；默认 0.6
+            webui_steps_raw = input_info.get('webui_steps', None)  # 可选：webui img2img 步数，None用服务端默认
+            webui_steps = int(webui_steps_raw) if webui_steps_raw is not None else None
             print(f"功能8：处理发型区域，耗时：{time.time() - start_time:.3f}s")
             # cv2.imwrite(f"{task_id}_mask_dilate.jpg", mask_dilate)
             # cv2.imwrite(f"{task_id}_final_img.jpg", final_img)
@@ -619,7 +621,7 @@ def change_hairstyle_v4():
             # mask_dilate = cv2.imread("/root/project/hair_service_sd/gt_mask_dilate.jpg")
             sd_result = webui_img2img(img=final_img, mask_img=mask_dilate, in_gender=in_gender, task_id=task_id,
                                       hair_id=hair_id, lora_material_path=hair_material_dir, tag=p_tag, is_hr=is_hr,
-                                      denoising_strength=denoising_strength, inference_port="57860")
+                                      denoising_strength=denoising_strength, inference_port="57860", webui_steps=webui_steps)
             print(f"功能：webui，耗时：{time.time() - start_time:.3f}s")
 
             # 功能：后处理并上传结果