初始化：换发型/换发色/训练发型服务

包含：
- hair_service_sd: 主服务（换发型/换发色/生发，端口8801）
- photo_service: LoRA调度+训练（端口32678）
- hair_grow_service: 调试测试页（端口8888，含4个测试页）
- 批量训练脚本（batch_train_hairstyles.py）
- 发际线mask自动识别（hairline_mask.py，4种方案）
- 手绘mask换发型（hair_swap_manual.py）
- 文档：README.md + LARGE_FILES.md + docs/

大文件（模型权重200G、训练数据123G）已排除，见 LARGE_FILES.md
OSS/COS密钥已脱敏为环境变量，原文件备份在本地

project/hair_service_sd/utils/model_io.py

@@ -0,0 +1,25 @@
+import os
+import torch
+
+def load_model_by_path(model_save_path, model, gpu_id = None):
+    if not os.path.exists(model_save_path): return
+    loc = 'cpu' if gpu_id is None else 'cuda:{}'.format(gpu_id)
+    pretrained_dict = torch.load(model_save_path, map_location=loc)
+    if 'state_dict' in pretrained_dict: pretrained_dict = pretrained_dict['state_dict']
+    model_dict = model.state_dict()
+    # pretrained_dict.pop('netG.model.1.weight', '404')
+    pretrained_dict_new = {k: v for k, v in pretrained_dict.items()
+                       if (k in model_dict and model_dict[k].data.shape == v.data.shape)}
+    # if 'netG.model.1.weight' not in pretrained_dict_new and 'netG.model.1.weight' in model_dict:
+    #     pretrained_dict_new['netG.model.1.weight'] = model_dict['netG.model.1.weight']
+    #     pretrained_dict_new['netG.model.1.weight'][:,:12] = pretrained_dict['netG.model.1.weight']
+
+    model_dict.update(pretrained_dict_new)
+    model.load_state_dict(model_dict)
+    print("load model: ", model_save_path)
+
+def save_model_by_path(model_save_path, model):
+    save_dir, _ = os.path.split(model_save_path)
+    if not os.path.isdir(save_dir): os.makedirs(save_dir)
+    model_dic={k.replace('.module', ''):v for k,v in model.state_dict().items()}
+    torch.save(model_dic, model_save_path)