fix: 适配 ubuntu 路径并增强换发色/训练流程稳定性

将配置与训练脚本从 /home/xsl 切到本机 /home/ubuntu；换发色在 webui 增强失败或缺色板时降级返回，训练结束后自动重启 hair 服务再回调。

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

project/photo_service/lora_train_service_1.py

@@ -37,8 +37,8 @@ if version == "local":
     callback_url = 'http://service.aicloud.fit:7395/api/hair/trainCallBack'
 else:
     current_url = 'http://0.0.0.0:7393/'
-    kohya_ss_home_dir = '/home/xsl/change_hair/project/kohya_ss_home'
-    webui_lora_dir = '/home/xsl/change_hair/project/onediff/stable-diffusion-webui/models/Lora'
+    kohya_ss_home_dir = '/home/ubuntu/change_hair/project/kohya_ss_home'
+    webui_lora_dir = '/home/ubuntu/change_hair/project/onediff/stable-diffusion-webui/models/Lora'
     inference_use_onediff = False
     callback_url = 'http://0.0.0.0:8801/api/hair/trainCallBack'
 base_webui_port = '57860'
@@ -307,13 +307,13 @@ def train_thread(sq, gpu_id):
             #   3. GPU 固定 device=0(单卡)
             #   4. 去掉 tokenizer_cache_dir(改用 HF 本地缓存 + 离线模式)
             #   5. 设置 HF_HUB_OFFLINE 避免联网检查
-            kohya_python = '/home/xsl/miniconda3/envs/kohya/bin/python'
+            kohya_python = '/home/ubuntu/miniconda3/envs/my_hair/bin/python'
             kohya_workdir = os.path.join(kohya_ss_home_dir, 'kohya_ss')
-            base_model = '/home/xsl/change_hair/project/onediff/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.safetensors'
+            base_model = '/home/ubuntu/change_hair/project/onediff/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.safetensors'
             cmd_train = (
                 f'cd {kohya_workdir} && '
                 f'HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 CUDA_VISIBLE_DEVICES={device_id} '
-                f'/home/xsl/miniconda3/envs/kohya/bin/accelerate launch --num_cpu_threads_per_process=2 "./train_network.py" --enable_bucket '
+                f'/home/ubuntu/miniconda3/envs/my_hair/bin/accelerate launch --num_cpu_threads_per_process=2 "./train_network.py" --enable_bucket '
                 f'--min_bucket_reso=256 --max_bucket_reso=2048 --pretrained_model_name_or_path="{base_model}" '
                 f'--train_data_dir={images_dir} --resolution="2000,2000" '
                 f'--output_dir={model_dir} '