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
2026-07-07 13:53:52 +08:00
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+# liveme_photo_service
+
+liveme数字写真线上服务代码。依赖webui……
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+import os
+import sys
+from webui_im2im import ControlnetRequestImg2Img
+import numpy as np
+import base64
+import cv2
+import os,sys
+from gevent import pywsgi, monkey
+
+monkey.patch_all()
+
+# 将当前工作目录切换到当前目录
+project_dir = os.path.dirname(os.path.abspath(__file__))
+os.chdir(project_dir)
+sys.path.append(project_dir)
+
+from flask import Flask, request, jsonify
+import global_variable as global_var
+import json
+
+app = Flask(__name__)
+
+
+@app.route('/template/list', methods=['POST'])
+def get_template_list():
+    import os
+    import json
+    try:
+        # 读取data/template.json文件，并返回
+        with open('data/template.json', 'r') as f:
+            template_list = json.load(f)
+        ret_dict = dict(code=0, message='success', data=template_list)
+        # 返回结果作为 JSON 响应
+        return jsonify(ret_dict)
+
+    except Exception as e:
+        ret_dict = dict(code=-1, message=str(e), data=[])
+        return jsonify(ret_dict)
+
+
+@app.route('/user/list', methods=['POST'])
+def get_user_list():
+    try:
+        # 获取用户文件夹路径
+        user_list_dir = os.path.join(global_var.service_data_dir, 'user_data')
+
+        ret_user_list = []
+
+        # 遍历查找user_list_dir目录下所有的cfg.json文件
+        for root, dirs, files in os.walk(user_list_dir):
+            for file in files:
+                if file != 'cfg.json': continue
+                json_path = os.path.join(root, file)
+                lora_path = os.path.join(root, 'lora.safetensors')
+                if not os.path.exists(lora_path): continue
+                # 读取cfg.json文件
+                with open(json_path, 'r') as f:
+                    user_info = json.load(f)
+                user_dict = dict(user_id=user_info['user_id'], face_img_url=user_info['face_img_url'])
+                ret_user_list.append(user_dict)
+
+        ret_dict = dict(code=0, message='success', data=ret_user_list)
+        return jsonify(ret_dict)
+
+    except Exception as e:
+        ret_dict = dict(code=-1, message='error', data=[])
+        return jsonify(ret_dict)
+
+
+@app.route('/user/generate', methods=['POST'])
+def generate_photo():
+    try:
+        # 获取请求参数
+        request_data = request.get_json()
+        #判断'user_id'和'base_img'是否在请求参数中
+        assert 'user_id' in request_data and 'base_img' in request_data, 'user_id and base_img is required'
+        user_id = request_data['user_id']
+        base_img_b64 = request_data['base_img']
+
+        user_path = os.path.join(global_var.service_data_dir, 'user_data', user_id)
+        assert os.path.isdir(user_path), 'user_id not exist'
+        user_lora = os.path.join(user_path, 'lora.safetensors')
+        usr_config_path = os.path.join(user_path, 'cfg.json')
+        assert os.path.exists(user_lora) and os.path.exists(usr_config_path), 'lora file or config not exist'
+
+        # 读取用户配置文件
+        with open(usr_config_path, 'r') as f:
+            user_info = json.load(f)
+            lora_md5 = user_info['lora_md5']
+        dst_lora_path = os.path.join(global_var.webui_lora_dir, lora_md5 + '.safetensors')
+        if not os.path.exists(dst_lora_path):
+            os.system(f'cp {user_lora} {dst_lora_path}')
+
+        #将模板图像转化为numpy数组
+        prompt = f'<lora:{lora_md5}:0.8>,easyphoto_face, easyphoto, 1person,face,suit'
+        neg_prompt = '(worst quality:2),(low quality:2),(normal quality:2),lowres,watermark'
+
+        image_array = np.frombuffer(base64.b64decode(base_img_b64), np.uint8)
+        base_img = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+        # base_img = cv2.resize(base_img, (512, 512))
+        # cv2.imshow('image', base_img)
+        # cv2.waitKey()
+
+        # 生成图片
+        control_net = ControlnetRequestImg2Img(prompt, neg_prompt)
+        control_net.build_body(dst_width=base_img.shape[1], dst_height=base_img.shape[0], cfg_scale=3.5, base_img=base_img)
+        output = control_net.send_request()
+        generate_photo = output['images'][0]
+
+        # 清理硬盘空间
+        os.remove(dst_lora_path)
+
+        # # 将生成的图片转化为base64编码
+        # retval, bytes = cv2.imencode('.png', generate_photo)
+        # generate_photo = base64.b64encode(bytes).decode('utf-8')
+        return jsonify(dict(code=0, message='success', generate_photo_b64=generate_photo))
+
+    except Exception as e:
+        ret_dict = dict(code=-1, message=str(e), data=[])
+        return jsonify(ret_dict)
+
+
+def webd_service():
+    # 用于启动webd的后台服务
+    current_file_dir = os.path.dirname(os.path.abspath(__file__))
+    webd_path = os.path.join(current_file_dir, 'webd', 'webd')
+
+    print('webd server started...')
+    cmd = f"{webd_path} -w {global_var.service_data_dir} -g rlT -l 10219"
+    os.system(cmd)
+
+
+if __name__ == '__main__':
+    # 服务启动的数据目录
+    global_var.service_data_dir = sys.argv[1]
+
+    # 本地webui的lora存储目录
+    global_var.webui_lora_dir = sys.argv[2]
+
+    # webui_server_port
+    global_var.webui_server_port = int(sys.argv[3])
+
+    # server_port
+    global_var.server_port = int(sys.argv[4])
+
+    # 检查webui_lora_dir目录是否存在
+    assert os.path.isdir(global_var.webui_lora_dir), 'webui_lora_dir should be a directory'
+
+    # 检查service_data_dir目录是否存在
+    if not os.path.exists(global_var.service_data_dir):
+        os.makedirs(global_var.service_data_dir, exist_ok=True)
+    else:
+        assert os.path.isdir(global_var.service_data_dir), 'service_data_dir should be a directory'
+
+    # 启动服务
+    # app.run(debug=False, port=global_var.server_port, host='0.0.0.0')
+
+    server = pywsgi.WSGIServer(('0.0.0.0', global_var.server_port), app)  # test port
+    server.serve_forever()
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+import os
+
+if __name__ == '__main__':
+    in_dir = "/mnt/database2/jiangqian/0808/online_orig_train_datas_2"
+    out_dir = "/mnt/database2/jiangqian/0808/online_train_datas_2"
+
+    dirs = os.listdir(in_dir)
+
+    for single_dir in dirs:
+        if "traindata" in single_dir:
+            in_single_dir = os.path.join(in_dir, single_dir)
+            out_single_dir = os.path.join(out_dir, single_dir)
+
+            if not os.path.exists(out_single_dir):
+                os.makedirs(out_single_dir)
+
+            images_dir = os.path.join(out_single_dir, "images")
+            if not os.path.exists(images_dir):
+                os.makedirs(images_dir)
+
+            hairstyle_dir = os.path.join(images_dir, "1_hairstyle")
+            if not os.path.exists(hairstyle_dir):
+                os.makedirs(hairstyle_dir)
+
+            for root, dirs, files in os.walk(in_single_dir):
+                for file in files:
+                    in_file = os.path.join(root, file)
+                    out_file = os.path.join(hairstyle_dir, file)
+                    os.system("cp %s %s" % (in_file, out_file))
+                    print("copy %s to %s" % (in_file, out_file))
+
+
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+import cv2
+import os
+import numpy as np
+import tqdm
+
+from utils import landmark_processor
+import base64
+import requests
+from PIL import Image
+import io
+
+def encode_numpy_to_base64(img):
+    retval, bytes = cv2.imencode('.png', img)
+    encoded_image = base64.b64encode(bytes).decode('utf-8')
+    return encoded_image
+
+
+def webui_img2img(img, mask, prompt=''):
+    url = "http://127.0.0.1:57860/sdapi/v1/img2img"
+    request_dict = {
+            "prompt": prompt,
+            "negative_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, large breast, big breast, bad_pictures,easynegative, faceless, no human, white background, simple background, ',
+            "sampler_name": "DPM++ 2M Karras",
+            "batch_size": 1,
+            "steps": 20,
+            "width": img.shape[1],
+            "height": img.shape[0],
+            "cfg_scale": 7.0,
+            "seed": 123456789,
+            "mask_blur": 11,
+            "init_images": [
+                encode_numpy_to_base64(img)
+            ],
+            "inpaint_full_res": False,
+            "inpainting_fill": 1,
+            "inpainting_mask_invert": 0,
+            "mask": encode_numpy_to_base64(mask),
+            # "refiner_checkpoint":"majicmixRealistic_v7.safetensors",
+            # "refiner_switch_at": 0.4,
+            "denoising_strength": 0.7,
+            "alwayson_scripts": {
+                # "controlnet": {
+                #     "args": [
+                #         {
+                #             "enabled": True,
+                #             "module": "openpose_full",
+                #             "model": "openpose",
+                #             "weight": 1.0,
+                #             # "image": self.read_image(),
+                #             "resize_mode": "Crop and Resize",
+                #             "low_vram": False,
+                #             "processor_res": 512,
+                #             "guidance_start": 0.0,
+                #             "guidance_end": 1.0,
+                #             "control_mode": "Balanced",
+                #             "pixel_perfect": False
+                #         }
+                #     ]
+                # }
+            }
+        }
+    response = requests.post(url=url, json=request_dict)
+    ret_json = response.json()
+    result = ret_json['images'][0]
+    img = cv2.imdecode(np.frombuffer(base64.b64decode(result.split(",", 1)[0]), np.uint8), cv2.IMREAD_COLOR)
+    return img
+
+def webui_super_res_img(img, ratio):
+    url = "http://127.0.0.1:57860/sdapi/v1/extra-single-image"
+    request_dict = {
+                      "resize_mode": 0,
+                      "show_extras_results": False,
+                      "gfpgan_visibility": 0,
+                      "codeformer_visibility": 1,
+                      "codeformer_weight": 1,
+                      "upscaling_resize": ratio,
+                      "upscaler_1": "8x_NMKD-Superscale_150000_G",
+                      "upscale_first": False,
+                      "image": encode_numpy_to_base64(img)
+                    }
+    response = requests.post(url=url, json=request_dict)
+    ret_json = response.json()
+    result = ret_json['image']
+    img = cv2.imdecode(np.frombuffer(base64.b64decode(result), np.uint8), cv2.IMREAD_COLOR)
+    return img
+
+
+def webui_tag_by_clip(img):
+    url = "http://127.0.0.1:57860/sdapi/v1/interrogate"
+    request_dict = {
+                      "image": encode_numpy_to_base64(img),
+                      "model": "clip"
+                    }
+    response = requests.post(url=url, json=request_dict)
+    ret_json = response.json()
+    return ret_json['caption']
+
+
+
+
+if __name__ == '__main__':
+    hair_dir = '/mnt/database2/jiangqian/0808/exp2-data-zrn-0808/1816523294655647746'
+
+    data2process_list = []
+
+    # 遍历查找hair_dir下的所有npy文件
+    for root, dirs, files in os.walk(hair_dir):
+        for file in files:
+            if file.endswith('.npy'):
+                # 关键点路径
+                pt1k_path = os.path.join(root, file)
+                origin_img_path = pt1k_path[:-4] + '.png'
+                origin_matting_path = pt1k_path[:-4] + '_origin_matting.png'
+                new_matting_path = pt1k_path[:-4] + '_new_matting.png'
+                result_img_path = pt1k_path[:-4] + '_res.png'
+                # 判断上面的文件是否存在
+                if (not os.path.exists(origin_img_path) or not os.path.exists(origin_matting_path)
+                        or not os.path.exists(new_matting_path) or not os.path.exists(result_img_path)):
+                    continue
+
+                ref_hair_path = pt1k_path[:-4] + '_orig_hair.png'
+                # if not os.path.exists(ref_hair_path):
+                #     continue
+                lora_model_path = pt1k_path[:-4] + '_hairstyle_lora.safetensors'
+                # if not os.path.exists(lora_model_path):
+                #     continue
+                data2process_list.append([pt1k_path, origin_img_path, origin_matting_path,
+                                          new_matting_path, result_img_path, ref_hair_path, lora_model_path])
+
+    crop_size = 768
+
+    webui_lora_dir = '/home/student/Documents/workspace_cxt_tianjing_hair/miaoya/webui_home/stable-diffusion-webui/models/Lora'
+    for pt1k_path, origin_img_path, origin_matting_path, new_matting_path, result_img_path, ref_hair_path, lora_model_path in tqdm.tqdm(data2process_list):
+        # if '508417f3-2c71-45cf-a75b-969b27ec7d8f' not in pt1k_path: continue
+        # 读取关键点
+        pt1k = np.load(pt1k_path)
+        # 读取原图
+        origin_img = cv2.imread(origin_img_path)
+        tmp_scale = 1920 / max(origin_img.shape[0], origin_img.shape[1])
+        if tmp_scale < 1.0:
+            origin_img = cv2.resize(origin_img, (0, 0), fx=tmp_scale, fy=tmp_scale, interpolation=cv2.INTER_LANCZOS4)
+
+        print("origin_img shape:", origin_img.shape)
+        # cv2.imshow("origin_img", origin_img)
+
+        # 读取原图抠图
+        origin_matting = cv2.imread(origin_matting_path, cv2.IMREAD_GRAYSCALE)
+        # 读取新图抠图
+        new_matting = cv2.imread(new_matting_path, cv2.IMREAD_GRAYSCALE)
+        # 读取结果图
+        result_img = cv2.imread(result_img_path)
+        print("result_img shape:", result_img.shape)
+        # cv2.imshow("result_img", result_img)
+        # # 读取参考头发
+        # ref_hair = cv2.imread(ref_hair_path)
+        # if max(ref_hair.shape[:2]) < 300: continue
+
+
+        # 如何图像不清晰，进行超分辨率处理
+        # if max(origin_img.shape[:2]) < 1500:
+        #     scale_ratio = 2000 / max(origin_img.shape[:2])
+        #     result_img = webui_super_res_img(result_img, scale_ratio)
+        #     origin_img = cv2.resize(origin_img, (result_img.shape[1], result_img.shape[0]),
+        #                             interpolation=cv2.INTER_LANCZOS4)
+        #     origin_matting = cv2.resize(origin_matting, (result_img.shape[1], result_img.shape[0]))
+        #     new_matting = cv2.resize(new_matting, (result_img.shape[1], result_img.shape[0]))
+        #     pt1k = pt1k * scale_ratio
+
+        # 获取头发处理的局部区域图像
+        # M = landmark_processor.get_transform_mat_hair_ratio_v1(pt1k, crop_size, ratio=0.30, h_offset=0.32)
+
+        scale = 768 / max(origin_img.shape[0], origin_img.shape[1])
+        M = cv2.getRotationMatrix2D((0, 0), 0, scale)
+
+        dst_size = (int(origin_img.shape[1] * scale), int(origin_img.shape[0] * scale))
+
+        # 高质量的从原图中截取头发区域
+        crop_origin = landmark_processor.high_quality_warpAffine(origin_img, M, dst_size)
+        cv2.imwrite("./crop_origin.png", crop_origin)
+
+        crop_result = landmark_processor.high_quality_warpAffine(result_img, M, dst_size)
+        cv2.imwrite("./crop_result.png", crop_result)
+        # tmp = cv2.warpAffine(origin_img, M, dst_size, flags=cv2.INTER_AREA)
+
+        # 构造重绘的mask
+        matting_merge = np.concatenate([origin_matting[:,:, np.newaxis], new_matting[:,:, np.newaxis]], axis=2)
+        matting_merge = np.max(matting_merge, axis=2)
+        # matting_merge = new_matting
+        crop_matting = cv2.warpAffine(matting_merge, M, dst_size)
+        mask = (crop_matting > 10).astype(np.float32)
+        mask_dilate = cv2.dilate(mask, np.ones((3, 11), np.uint8))
+        final_img = crop_result
+
+        mask_dilate = np.clip(mask_dilate * 255, 0, 255).astype(np.uint8)
+
+        # file_name = os.path.basename(pt1k_path)[:-4]
+        # save_dir = '/home/chinatszrn/Downloads/abc/ref_hair/dst_res/style3_tmp'
+        # cv2.imwrite(os.path.join(save_dir, file_name + '.png'), final_img)
+        # cv2.imwrite(os.path.join(save_dir, file_name + '_mask.png'), mask_dilate)
+        # # cv2.imwrite(os.path.join(save_dir, file_name + '_ref_hair.png'), ref_hair)
+        # continue
+
+        # # 拷贝lora模型
+        # os.system('cp {} {}'.format(lora_model_path, webui_lora_dir))
+        # # 构建prompt提示词
+        # lora_model_name = os.path.basename(pt1k_path)[:-4]
+
+        # 对final_img进行打标
+        # tag_result = webui_tag_by_clip(final_img)
+        tag_result = ''
+
+        # 开始重绘
+        prompt = f'<lora:1816523294655647746_hairstyle_lora:1> titor hairstyle, easyphoto, ' + tag_result
+        # 对发型进行重绘
+
+        # cv2.imshow("final_img_0", final_img)
+        # cv2.imshow("mask_dilate", mask_dilate)
+        # cv2.waitKey(100)
+
+        sd_result = webui_img2img(final_img, mask_dilate, prompt)
+
+        final_img = origin_img.copy()
+        # 将重绘结果恢复到原图
+        M_inv = cv2.invertAffineTransform(M)
+        cv2.warpAffine(sd_result, M_inv, (final_img.shape[1], final_img.shape[0]), dst=final_img,
+                       borderMode=cv2.BORDER_TRANSPARENT, flags=cv2.INTER_LANCZOS4)
+
+        # cv2.imshow("final_img", final_img)
+        # cv2.waitKey(0)
+        cv2.imwrite(result_img_path[:-4] + '_sd.png', final_img)
+
+        # ref_hair_scale = final_img.shape[0] / ref_hair.shape[0]
+        # ref_hair = cv2.resize(ref_hair, (0, 0), fx=ref_hair_scale, fy=ref_hair_scale, interpolation=cv2.INTER_LANCZOS4)
+        # img2show = np.concatenate([origin_img, ref_hair, final_img], axis=1)
+        #
+        # # 显示结果
+        # save_dir = '/home/chinatszrn/Downloads/exp'
+        # cv2.imwrite(os.path.join(save_dir, lora_model_name + '.png'), img2show)
+        # # cv2.imshow("origin_img", cv2.resize(img2show, (0, 0), fx=0.3, fy=0.3, interpolation=cv2.INTER_AREA))
+        # cv2.imshow("sd_result", cv2.resize(sd_result, (0, 0), fx=0.3, fy=0.3, interpolation=cv2.INTER_AREA))
+        # cv2.waitKey(1000)
+
+
+
+
+
@@ -0,0 +1,34 @@
+# 服务数据目录
+service_data_dir = None
+
+# 生成视频的thread_num
+video_generation_thread_num = None
+
+# avatar训练的thread_num
+avatar_train_thread_num = None
+
+# 视频生成的任务队列
+video_generation_task_sq = None
+
+# avatar训练的任务队列
+avatar_train_task_sq = None
+
+# webui的lora存储目录
+webui_lora_dir = None
+
+# webui_server_port
+webui_server_port = None
+
+# server ip address
+server_ip = None
+
+# service port
+server_port = 10239
+
+# 是否启动视频的硬件编码
+video_hardware_encode = True
+
+#----------------------------------------------
+
+# 发型lora训练队列
+hair_style_lora_train_task_sq = None
@@ -0,0 +1,68 @@
+import base64
+import time
+
+import requests
+import cv2
+import json
+import re
+import os
+import tqdm
+
+# OpenAI API Key
+api_key = "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"
+
+# Function to encode the image
+def encode_image(image_path):
+  img = cv2.imread(image_path)
+  scale = 500.0 / min(img.shape[:2])
+  img = cv2.resize(img, (0, 0), fx=scale, fy=scale)
+  scaled_path = '/tmp/scaled_image.jpg'
+  cv2.imwrite(scaled_path, img)
+  with open(scaled_path, "rb") as image_file:
+    return base64.b64encode(image_file.read()).decode('utf-8')
+
+def caption_image(image_path):
+  # Getting the base64 string
+  base64_image = encode_image(image_path)
+
+  headers = {
+    "Content-Type": "application/json",
+    "Authorization": f"Bearer {api_key}"
+  }
+
+  payload = {
+    "model": "gpt-4-vision-preview",
+    "messages": [
+      {
+        "role": "user",
+        "content": [
+          {
+            "type": "text",
+            "text": "As an AI image tagging expert, please provide precise tags for the hairstyle in the image, To enhance CLIP model's understanding of the content. Please provide a detailed description of the hairstyle in the image, including but not limited to the color, style, length, curliness, hairline, highlights, gradients,  etc. Your tags should be accurate, non-duplicative, and within a 10-20 word count range. Tags should be comma-separated. No need to provide any safety statements or precautions."
+          },
+          {
+            "type": "image_url",
+            "image_url": {
+              "url": f"data:image/jpeg;base64,{base64_image}",
+              "detail": "low"
+            }
+          }
+        ]
+      }
+    ],
+    "max_tokens": 300
+  }
+
+  try:
+    response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
+    tmp_json = response.json()
+    return tmp_json['choices'][0]['message']['content']
+  except Exception as e:
+    print(e)
+    return ""
+
+
+if __name__ == '__main__':
+  prompt = caption_image('/home/chinatszrn/Downloads/abc/train_data/style1/07ebac82-4c0f-4dd2-84bd-bc34a059bd9b.png')
+  print(prompt)
+
@@ -0,0 +1,169 @@
+import gradio as gr
+import os,re
+import numpy as np
+import requests
+import cv2
+import base64
+import json
+from io import BytesIO
+from PIL import Image
+
+api_service_url = 'http://127.0.0.1:1234'
+# api_service_url = 'http://i-2.gpushare.com:53412'
+
+class ChangeFaceGui():
+    def __init__(self):
+        self.template_list = self.get_template_list()
+        self.user_dict = self.get_user_dict()
+        # self.tmp_dir = './tmp'
+        # if not os.path.exists(self.tmp_dir): os.makedirs(self.tmp_dir)
+        self.user_img_list = []
+        # 获取户图片
+        for item in self.user_dict:
+            response = requests.get(item['face_img_url'])
+            image_data = BytesIO(response.content)
+            user_face_img = cv2.imdecode(np.frombuffer(image_data.read(), np.uint8), cv2.IMREAD_COLOR)
+            self.user_img_list.append(user_face_img)
+            item['face_img'] = user_face_img
+
+            # img_path = f'{self.tmp_dir}/{item["user_id"]}.jpg'
+            # if not os.path.exists(img_path):
+            #     cv2.imwrite(f'{self.tmp_dir}/{item["user_id"]}.jpg', user_face_img)
+
+
+    #请求得到模板图片的url
+    def get_template_list(self):
+        url = f"{api_service_url}/template/list"
+        payload = {}
+        headers = {}
+        response = requests.request("POST", url, headers=headers, data=payload)
+        print('请求模板照片成功！！')
+        return(response.json()['data'])
+
+
+    #请求得到用户图片及ID
+    def get_user_dict(self):
+        url = f"{api_service_url}/user/list"
+        payload = {}
+        headers = {}
+        response = requests.request("POST", url, headers=headers, data=payload)
+        user_dict = response.json()['data']
+        print('请求用户照片成功！！')
+        return(user_dict)
+
+
+    #获取用户ID
+    def get_user_id(self, user_img):
+        user_small_img = cv2.resize(user_img, (100, 100))
+        mse = []
+        for item in self.user_dict:
+            user_face_img = item['face_img']
+            # cv2.imwrite(f'{self.tmp_dir}/{item["user_id"]}_list.jpg', user_face_img)
+            user_face_img = cv2.resize(user_face_img, (100, 100))
+            # 计算均方误差
+            mse.append(np.mean((user_small_img - user_face_img) ** 2))
+
+        # 找到均方差最小值对应的ID
+        user_id = mse.index(min(mse))
+        user_id = self.user_dict[user_id]['user_id']
+        print('用户ID:', user_id)
+        return user_id
+
+
+    #虚拟试穿模块，输入是模特图和衣服图，输出是虚拟试穿的结果
+    def take_photo(self, template_img, user_img):
+        # if not os.path.exists(self.tmp_dir):os.makedirs(self.tmp_dir)
+
+        if template_img is None or user_img is None:
+            return None
+
+        # 使用 Pillow 加载图像
+        template_img = Image.open(template_img)
+        user_img = Image.open(user_img)
+
+        # 将 Pillow 图像转换为 OpenCV 格式（BGR）
+        template_img = cv2.cvtColor(np.array(template_img), cv2.COLOR_RGB2BGR)
+        user_img = cv2.cvtColor(np.array(user_img), cv2.COLOR_RGB2BGR)
+
+        # 将模板图片转换为base64格式
+        retval, template_bytes = cv2.imencode('.jpg', template_img)
+        encoded_image = base64.b64encode(template_bytes).decode('utf-8')
+        user_id = self.get_user_id(user_img)
+        url = f"{api_service_url}/user/generate"
+        payload = json.dumps({
+            "user_id": user_id,
+            "base_img": encoded_image
+        })
+        headers = {
+            'Content-Type': 'application/json'
+        }
+        print('请求api_service.py发送请求！！')
+        response = requests.request("POST", url, headers=headers, data=payload)
+        print('请求api_service.py发送请求成功！！')
+        if response.status_code != 200:
+            raise RuntimeError(f"Failed to send request to API service. Status code: {response.status_code}")
+
+        ret_image_b64 = response.json().get('generate_photo_b64')
+
+        if ret_image_b64 is None:
+            raise RuntimeError(f"ret image failed!")
+
+        image_array = np.frombuffer(base64.b64decode(ret_image_b64), np.uint8)
+        result_image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+        result_image = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
+        return result_image
+
+
+    def start_gui(self):
+        user_img_list = []
+        for item in self.user_img_list:
+            img = cv2.cvtColor(item, cv2.COLOR_BGR2RGB)
+            user_img_list.append(img)
+
+        with gr.Blocks() as demo:
+            with gr.Row():
+                gr.Markdown("# 数字力场效果展示")
+
+            # 换脸
+            with gr.Tab("数字写真"):
+                with gr.Row():
+                    gr.Markdown("# 数字写真")
+                with gr.Row():
+                    with gr.Column():
+                        #选择模板
+                        template_img = gr.Image(label="模版", sources='upload', min_width=384, width=384, height=384, type="filepath", value=self.template_list[0],interactive=True)
+                        example_template = gr.Examples(
+                            inputs=template_img,
+                            examples_per_page=12,
+                            examples= self.template_list)
+                    with gr.Column():
+                        # 选择用户
+                        user_img = gr.Image(label="用户", sources='upload', min_width=384, width=384, height=384, type="filepath", value= user_img_list[0],interactive=False)
+                        example_user = gr.Examples(
+                            inputs=user_img,
+                            examples_per_page=12,
+                            examples=user_img_list)
+
+                    with gr.Column():
+                        output_img = gr.Image(label="结果展示", type="numpy", height=576, width=384)
+                with gr.Column():
+                    run_button = gr.Button(value="提交")
+                    run_button.click(self.take_photo, inputs=[template_img, user_img], outputs=[output_img])
+
+            #             #换衣服
+            # with gr.Tab("换衣"):
+            #     with gr.Row():
+            #         gr.Markdown("# 换衣")
+            #     text_button = gr.Button("提交")
+            # # 换发型
+            # with gr.Tab("换发型"):
+            #     with gr.Row():
+            #         gr.Markdown("# 换发型")
+            #     text_button = gr.Button("提交")
+
+        demo.launch(server_name='0.0.0.0', server_port=8080)
+
+
+if __name__ == '__main__':
+    demo = ChangeFaceGui()
+    demo.start_gui()
@@ -0,0 +1,102 @@
+import gradio as gr
+import os,re
+import numpy as np
+import requests
+import cv2
+import base64
+import json
+from io import BytesIO
+from PIL import Image
+
+# api_service_url = 'http://127.0.0.1:1234'
+# api_service_url = 'http://i-2.gpushare.com:53412'
+api_service_url = 'http://service.aicloud.fit:7393/api/hairStyle/v1'
+
+class ChangeHairGui():
+    # def __init__(self):
+    #     a = 1
+    def change_hair(self, user_img, hair_img):
+
+        if user_img is None or user_img is None:
+            return None
+        #
+        # if user_img.shape != hair_img.shape:
+        #     hair_img = cv2.resize(hair_img, (user_img.shape[1], user_img.shape[0]))
+        #
+        #     alpha = 0.5  # 图像1的权重
+        #     beta = 0.5  # 图像2的权重
+        #     gamma = 0  # 亮度调整常量（通常为0）
+        #
+        #     result_image = cv2.addWeighted(user_img, alpha, hair_img, beta, gamma)
+
+
+        # 将 Pillow 图像转换为 OpenCV 格式（BGR）
+        user_img = cv2.cvtColor(np.array(user_img), cv2.COLOR_RGB2BGR)
+        hair_img = cv2.cvtColor(np.array(hair_img), cv2.COLOR_RGB2BGR)
+
+        # 将模板图片转换为base64格式
+        retval, user_bytes = cv2.imencode('.jpg', user_img)
+        encoded_user_image = base64.b64encode(user_bytes).decode('utf-8')
+        retval, hair_bytes = cv2.imencode('.jpg', hair_img)
+        encoded_hair_image = base64.b64encode(hair_bytes).decode('utf-8')
+
+        url = api_service_url
+
+        # 请求换发型接口
+        payload = json.dumps({
+            "user_img_base64": encoded_user_image,
+            "hair_ref_img_base64": encoded_hair_image
+        })
+        headers = {
+            'Content-Type': 'application/json'
+        }
+        print('请求api_service.py发送请求！！')
+        response = requests.request("POST", url, headers=headers, data=payload)
+        print('请求api_service.py发送请求成功！！')
+        if response.status_code != 200:
+            raise RuntimeError(f"Failed to send request to API service. Status code: {response.status_code}")
+
+        ret_image_b64 = response.json().get('result')
+
+        if ret_image_b64 is None:
+            raise RuntimeError(f"ret image failed!")
+
+        image_array = np.frombuffer(base64.b64decode(ret_image_b64), np.uint8)
+        result_image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+        result_image = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
+
+        dst_size = max(result_image.shape[0], result_image.shape[1])
+        M = cv2.getRotationMatrix2D((result_image.shape[1] / 2, result_image.shape[0] / 2), 0, 1)
+        M[:, 2] += np.float32([dst_size / 2 - result_image.shape[1] / 2, dst_size / 2 - result_image.shape[0] / 2])
+        result_image = cv2.warpAffine(result_image, M, (dst_size, dst_size), borderValue=(255, 255, 255))
+
+        return result_image
+
+
+
+    def start_gui(self):
+        with gr.Blocks() as demo:
+            with gr.Row():
+                gr.Markdown("# 数字力场换发型效果展示")
+
+            # 换脸
+            with gr.Tab("换发型"):
+                with gr.Row():
+                    gr.Markdown("#换发型")
+                with gr.Row():
+                    with gr.Column():
+                        user_img = gr.Image(label="请上传用户图片", type="numpy", height=384, width=384)
+                    with gr.Column():
+                        hair_img = gr.Image(label="请上传发型图片", type="numpy", height=384, width=384)
+                    with gr.Column():
+                        output_img = gr.Image(label="结果展示", type="numpy", height=384, width=384, format='png')
+                with gr.Column():
+                    run_button = gr.Button(value="提交")
+                    run_button.click(self.change_hair, inputs=[user_img, hair_img], outputs=[output_img])
+
+        demo.launch(server_name='0.0.0.0', server_port=8080)
+
+if __name__ == '__main__':
+
+    demo = ChangeHairGui()
+    demo.start_gui()
@@ -0,0 +1,120 @@
+import os
+import sys
+import uuid
+
+from webui_im2im import ControlnetRequestImg2Img
+import numpy as np
+import base64
+import cv2
+import os, sys
+from gevent import pywsgi, monkey
+from multiprocessing import Process, Queue
+import glob
+
+# from gpt4v_caption import caption_image
+
+# monkey.patch_all()
+# sys.setrecursionlimit(20000)
+
+# 将当前工作目录切换到当前目录
+project_dir = os.path.dirname(os.path.abspath(__file__))
+os.chdir(project_dir)
+sys.path.append(project_dir)
+
+
+import global_variable as global_var
+import json
+
+
+
+def train_hair_lora():
+    import os
+    import json
+
+    try:
+        hair_train_dir = "/mnt/database2/jiangqian/0808/online_train_datas_2"
+        hair_material_dir_list = os.listdir(hair_train_dir)
+
+        for single_hair_material in hair_material_dir_list:
+            # 获取请求参数
+            task_id = str(uuid.uuid4())
+
+            request_data = {}
+            hair_material_dir = os.path.join(hair_train_dir, single_hair_material)
+            print("--------------------hair_material_dir:", hair_material_dir)
+            request_data['hair_material_dir'] = hair_material_dir
+
+            img_dir = os.path.join(hair_material_dir, 'images')
+
+            model_dir = os.path.join(hair_material_dir, 'model')
+            if not os.path.exists(model_dir):
+                os.makedirs(model_dir)
+
+            #判断img_dir下面是否只有一个文件夹
+            img_dir_list = os.listdir(img_dir)
+            train_image_dir = os.path.join(img_dir, img_dir_list[0])
+
+            #判断文件夹下面是否有图片
+            request_data['train_image_dir'] = train_image_dir
+
+            # 将请求数据放入队列
+            train_thread(request_data)
+
+            # 返回结果
+            print("头发lora训练开始")
+            print("\n\n\n\n")
+
+    except Exception as e:
+        print(e)
+
+def train_thread(task_dict):
+    try:
+        hair_material_dir = task_dict['hair_material_dir']
+        images_dir = os.path.join(hair_material_dir, 'images')
+        model_dir = os.path.join(hair_material_dir, 'model')
+        train_image_dir = task_dict['train_image_dir']
+        tag = ""
+
+        sample_dir = os.path.join(model_dir, 'sample')
+        if not os.path.exists(sample_dir):
+            os.makedirs(sample_dir)
+        sample_txt = os.path.join(sample_dir, 'prompt.txt')
+        with open(sample_txt, 'w') as f_s:
+            f_s.write('titor hairstyle, easyphoto, faceless, no human, white background, simple background, '
+                      + tag
+                      + ' --n low quality, worst quality, bad anatomy, bad composition, poor, low effort --h 512 '
+                        '--w 512 --s 30 --l 7')
+
+        #训练头发lora
+        cmd_train = (
+            'docker run --rm --gpus all -v /home/student/Documents/workspace_cxt_tianjing_hair/miaoya/kohya_ss_home:/home/chinatszrn -v '
+            '/mnt:/mnt -e PATH=/home/chinatszrn/.local/bin -w /home/chinatszrn/kohya_ss '
+            '--net=host chinatszrn/ubuntu:kohya_ss accelerate launch --num_cpu_threads_per_process=2 "./train_network.py" --enable_bucket '
+            '--min_bucket_reso=256 --max_bucket_reso=1800 --pretrained_model_name_or_path="/mnt/nas_hdd/米亚像馆/models/Stable-diffusion/majicmixRealistic_v7.safetensors" '
+            f'--train_data_dir={images_dir} --resolution="768,768" '
+            f'--output_dir={model_dir} '
+            '--network_alpha="64" --save_model_as=safetensors --network_module=networks.lora --text_encoder_lr=5e-05 '
+            '--unet_lr=0.0001 --network_dim=128 --output_name="hairstyle_lora" --lr_scheduler_num_cycles="20" '
+            '--no_half_vae --learning_rate="0.0001" --lr_scheduler="cosine" --lr_warmup_steps="650" --train_batch_size="1" '
+            '--max_train_steps="2000" --save_every_n_epochs="100" --mixed_precision="fp16" --save_precision="fp16" '
+            '--caption_extension=".txt" --sample_sampler=ddim '
+            f'--sample_prompts={sample_txt} --sample_every_n_epochs="1" '
+            '--seed="1234" --cache_latents --optimizer_type="AdamW8bit" --max_data_loader_n_workers="0" --bucket_reso_steps=64 '
+            '--xformers --bucket_no_upscale --noise_offset=0.0 --tokenizer_cache_dir="/home/chinatszrn/.cache/clip"')
+        print("cmd_train:", cmd_train)
+        os.system(cmd_train)
+
+        lora_path = os.path.join(model_dir, 'hairstyle_lora.safetensors')
+
+    except Exception as e:
+        print(e)
+        return
+
+
+if __name__ == '__main__':
+    global_var.webui_lora_dir = '/home/student/Documents/workspace_cxt_tianjing_hair/miaoya/webui_home/stable-diffusion-webui/models/Lora'
+
+    train_hair_lora()
+
+
+
@@ -0,0 +1,315 @@
+import os
+import sys
+import uuid
+
+from webui_im2im import ControlnetRequestImg2Img
+import numpy as np
+import base64
+import cv2
+import os, sys
+from gevent import pywsgi, monkey
+from multiprocessing import Process, Queue
+import glob
+
+# from gpt4v_caption import caption_image
+
+# monkey.patch_all()
+# sys.setrecursionlimit(20000)
+
+# 将当前工作目录切换到当前目录
+project_dir = os.path.dirname(os.path.abspath(__file__))
+os.chdir(project_dir)
+sys.path.append(project_dir)
+
+from flask import Flask, request, jsonify
+import global_variable as global_var
+import json
+
+app = Flask(__name__)
+
+
+def send_request(url, state, task_dict):
+    import requests
+    import json
+
+    msg = '头发lora训练失败' if state == -1 else '头发lora训练成功'
+    payload = json.dumps({
+        "task_id": task_dict['task_id'] if task_dict is not None else '',
+        "hair_id": task_dict['hair_id'] if task_dict is not None else '',
+        "state": state,
+        "msg": msg,
+        "is_tj": task_dict['is_tj']
+    })
+
+    print("payload:", payload)
+
+    headers = {
+        'Content-Type': 'application/json'
+    }
+
+    requests.request("POST", url, headers=headers, data=payload)
+
+
+@app.route('/api/hair/train', methods=['POST'])
+def train_hair_lora():
+    import os
+    import json
+    task_id = ''
+    try:
+        # 获取请求参数
+        request_data = request.get_json()
+        assert 'task_id' in request_data, 'task_id is required'
+        task_id = request_data['task_id']
+        assert 'hair_id' in request_data and 'hair_material_dir' in request_data, 'hair_id and hair_material_dir is required'
+        hair_material_dir = request_data['hair_material_dir']
+        assert os.path.exists(hair_material_dir), f'{hair_material_dir} not exists'
+        img_dir = os.path.join(hair_material_dir, 'images')
+        assert os.path.exists(img_dir), f'{img_dir} not exists'
+        assert os.path.exists(
+            os.path.join(hair_material_dir, 'model')), f'{os.path.join(hair_material_dir, "model")} not exists'
+
+        #判断img_dir下面是否只有一个文件夹
+        img_dir_list = os.listdir(img_dir)
+        assert len(img_dir_list) == 1, f'{img_dir}下面只能有一个文件夹'
+        train_image_dir = os.path.join(img_dir, img_dir_list[0])
+        assert os.path.isdir(train_image_dir), f'{train_image_dir}不是文件夹'
+        # 检查该文件夹是否以数字加下划线开头
+        assert img_dir_list[0].split('_')[0].isdigit(), f'{img_dir_list[0]}不是数字开头'
+
+        #判断文件夹下面是否有图片
+        img_list = glob.glob(train_image_dir + '/*.png')
+        assert len(img_list) > 10, f'{os.path.join(img_dir, img_dir_list[0])}下面图片数量小于10张'
+        request_data['train_image_dir'] = train_image_dir
+
+        # is tianjin company
+        try:
+            request_data['is_tj'] = request_data['is_tj']
+        except Exception as e:
+            print(e)
+            request_data['is_tj'] = '0'
+
+        # 将请求数据放入队列
+        global_var.hair_style_lora_train_task_sq.put(request_data)
+
+        # 返回结果
+        ret_dict = dict(state=0, msg='头发lora训练开始', task_id=task_id)
+        return jsonify(ret_dict)
+    except Exception as e:
+        ret_dict = dict(state=-1, msg=str(e), task_id=task_id)
+        return jsonify(ret_dict)
+
+
+@app.route('/api/hair/inference', methods=['POST'])
+def inference_webui():
+    import os
+    import requests
+    import time
+    url = "http://127.0.0.1:57860/sdapi/v1/img2img"
+    onediff_url = "http://127.0.0.1:9038/sdapi/v1/img2img"
+
+    try:
+        t1 = time.time()
+        # 获取请求参数
+        request_data = request.get_json()
+        # with open('request_data.json', 'w') as f:
+        #     json.dump(request_data, f)
+        request_json = request_data.get('request_json')
+        # hd_version_flag = request_data.get('hd_version_flag', False)
+        hair_id = request_data['hair_id']
+        selected_url = url
+        if 'refiner_checkpoint' not in request_json:
+            selected_url = onediff_url
+            request_json['script_name'] = 'onediff_diffusion_model'
+            print('user onediff')
+
+
+
+        # if hd_version_flag:
+        #     print('user hd version!!!!!')
+        #     lora_file_name = 'hairstyle_hd_lora.safetensors'
+        # else:
+        #     lora_file_name = 'hairstyle_lora.safetensors'
+
+        hair_material_dir = request_data.get('hair_material_dir')
+        print(hair_material_dir)
+        assert os.path.exists(hair_material_dir), f'{hair_material_dir} not exists'
+
+        hd_version_flag = True
+        lora_file_name = 'hairstyle_hd_lora.safetensors'
+        request_lora_path = os.path.join(hair_material_dir, 'model', lora_file_name)
+        if not os.path.exists(request_lora_path):
+            print('use low resolution lora')
+            hd_version_flag = False
+            lora_file_name = 'hairstyle_lora.safetensors'
+            request_lora_path = os.path.join(hair_material_dir, 'model', lora_file_name)
+
+        assert os.path.exists(request_lora_path), f'{request_lora_path} not exists'
+
+        if hd_version_flag:
+            tmp_lora_name = f'{hair_id}_hd'
+        else:
+            tmp_lora_name = f'{hair_id}'
+        lora_dst_path = os.path.join(global_var.webui_lora_dir, f'{tmp_lora_name}.safetensors')
+        if not os.path.exists(lora_dst_path):
+            os.system('cp {} {}'.format(request_lora_path, lora_dst_path))
+        request_json['prompt'] = f'<lora:{tmp_lora_name}:1.0>, titor hairstyle, ' + request_json['prompt']
+        request_json['negative_prompt'] = request_json['negative_prompt'] + ', faceless, no human'
+        print('pre_stage cost:', time.time() - t1)
+
+        start_time = time.time()
+        response = requests.post(url=selected_url, json=request_json)
+        print('inference time:', time.time() - start_time)
+
+        ret_json = response.json()
+        # if os.path.exists(lora_dst_path):
+        #     os.remove(lora_dst_path)
+        # 返回结果
+        return jsonify(ret_json)
+    except Exception as e:
+        ret_dict = dict(state=-1, msg=str(e))
+        return jsonify(ret_dict)
+
+
+@app.route('/api/hair/inference_diy', methods=['POST'])
+def inference_diy_webui():
+    import os
+    import requests
+    import time
+    url = "http://127.0.0.1:57860/sdapi/v1/img2img"
+    onediff_url = "http://127.0.0.1:9038/sdapi/v1/img2img"
+
+    try:
+        t1 = time.time()
+        # 获取请求参数
+        request_data = request.get_json()
+        # with open('request_data.json', 'w') as f:
+        #     json.dump(request_data, f)
+        request_json = request_data.get('request_json')
+
+        selected_url = url
+        if 'refiner_checkpoint' not in request_json:
+            selected_url = onediff_url
+            request_json['script_name'] = 'onediff_diffusion_model'
+            print('user onediff')
+
+        request_json['prompt'] = f'titor hairstyle, ' + request_json['prompt']
+        request_json['negative_prompt'] = request_json['negative_prompt'] + ', faceless, no human'
+        print('pre_stage cost:', time.time() - t1)
+
+        start_time = time.time()
+        response = requests.post(url=selected_url, json=request_json)
+        print('inference time:', time.time() - start_time)
+
+        ret_json = response.json()
+        # if os.path.exists(lora_dst_path):
+        #     os.remove(lora_dst_path)
+        # 返回结果
+        return jsonify(ret_json)
+    except Exception as e:
+        ret_dict = dict(state=-1, msg=str(e))
+        return jsonify(ret_dict)
+
+def train_thread(sq, gpu_id):
+    while True:
+        url = 'http://service.aicloud.fit:7393/api/hair/trainCallBack'
+        task_dict = None
+        try:
+            task_dict = sq.get()
+            hair_material_dir = task_dict['hair_material_dir']
+            images_dir = os.path.join(hair_material_dir, 'images')
+            model_dir = os.path.join(hair_material_dir, 'model')
+            train_image_dir = task_dict['train_image_dir']
+            # tag = task_dict['tag']
+            tag = ""
+            is_tj = task_dict['is_tj']
+
+            sample_dir = os.path.join(model_dir, 'sample')
+            if not os.path.exists(sample_dir):
+                os.makedirs(sample_dir)
+            sample_txt = os.path.join(sample_dir, 'prompt.txt')
+            with open(sample_txt, 'w') as f_s:
+                f_s.write('titor hairstyle, easyphoto, faceless, no human, white background, simple background, '
+                          + tag
+                          + ' --n low quality, worst quality, bad anatomy, bad composition, poor, low effort --h 768 '
+                            '--w 768 --s 30 --l 7')
+
+            # 给训练图片打标签
+            # cmd_caption = ('docker run --rm --gpus all -v /home/chinatszrn/Documents/miaoya/kohya_ss_home:/home/chinatszrn '
+            #                '-v /mnt:/mnt -e PATH=/home/chinatszrn/.local/bin -w /home/chinatszrn/kohya_ss '
+            #                '--net=host chinatszrn/ubuntu:kohya_ss accelerate '
+            #                'launch "./finetune/tag_images_by_wd14_tagger.py" --batch_size=2 '
+            #                '--general_threshold=0.5 --character_threshold=0.5 --caption_extension=".txt" '
+            #                '--model="SmilingWolf/wd-v1-4-convnextv2-tagger-v2" --max_data_loader_n_workers="2" '
+            #                '--debug --remove_underscore --frequency_tags --undesired_tags="1girl, 1boy" '
+            #                f'"{train_image_dir}"')
+            # os.system(cmd_caption)
+
+            img_path_list = glob.glob(train_image_dir + '/*.png')
+            # 给训练图片打标签, gpt
+            for img_path in img_path_list:
+                # print(img_path)
+                txt_path = img_path[:-4] + '.txt'
+
+                print("tag img_path: ", img_path)
+                # tags = caption_image(img_path)
+                tags = tag
+                with open(txt_path, 'w') as f:
+                    f.write('titor hairstyle, easyphoto, faceless, no human, white background, simple background, ' + tags)
+
+            #判断是否每个训练图片都有标签文件
+            # for img_path in img_path_list:
+            #     assert os.path.exists(img_path[:-4]+'.txt'), f'{img_path}没有对应的标签文件'
+            #     with open(img_path[:-4]+'.txt', 'r') as f:
+            #         tags = f.readline()
+            #     with open(img_path[:-4]+'.txt', 'w') as f:
+            #         f.write('titor hairstyle, faceless, no human, gray background, simple background, ' + tags)
+
+            #训练头发lora
+            cmd_train = (
+                'docker run --rm --gpus "device=0" -v /home/student/Documents/workspace_cxt_tianjing_hair/miaoya/kohya_ss_home:/home/chinatszrn -v '
+                '/mnt:/mnt -e PATH=/home/chinatszrn/.local/bin -w /home/chinatszrn/kohya_ss '
+                '--net=host chinatszrn/ubuntu:kohya_ss accelerate launch --num_cpu_threads_per_process=2 "./train_network.py" --enable_bucket '
+                '--min_bucket_reso=256 --max_bucket_reso=2048 --pretrained_model_name_or_path="/mnt/nas_hdd/米亚像馆/models/Stable-diffusion/majicmixRealistic_v7.safetensors" '
+                f'--train_data_dir={images_dir} --resolution="2000,2000" '
+                f'--output_dir={model_dir} '
+                '--network_alpha="64" --save_model_as=safetensors --network_module=networks.lora --text_encoder_lr=5e-05 '
+                '--unet_lr=0.0001 --network_dim=128 --output_name="hairstyle_hd_lora" --lr_scheduler_num_cycles="20" '
+                '--no_half_vae --learning_rate="0.0001" --lr_scheduler="cosine" --lr_warmup_steps="650" --train_batch_size="1" '
+                '--max_train_steps="1500" --save_every_n_epochs="100" --mixed_precision="fp16" --save_precision="fp16" '
+                '--caption_extension=".txt" --sample_sampler=ddim '
+                f'--sample_prompts={sample_txt} --sample_every_n_epochs="1000" '
+                '--seed="1234" --cache_latents --optimizer_type="AdamW8bit" --max_data_loader_n_workers="0" --bucket_reso_steps=64 '
+                '--xformers --bucket_no_upscale --noise_offset=0.0 --tokenizer_cache_dir="/home/chinatszrn/.cache/clip"')
+            print("cmd_train:", cmd_train)
+            os.system(cmd_train)
+
+            lora_path = os.path.join(model_dir, 'hairstyle_hd_lora.safetensors')
+
+            # tianjin callback
+            if is_tj == "1":
+                url = 'http://service.aicloud.fit:7395/api/hair/trainCallBack'
+
+            if not os.path.exists(lora_path):
+                send_request(url, -1, task_dict)
+            else:
+                send_request(url, 0, task_dict)
+
+        except Exception as e:
+            send_request(url, -1, None)
+            continue
+
+
+if __name__ == '__main__':
+    global_var.webui_lora_dir = '/gz-fs/models/Lora'
+
+    # avatar训练的任务队列
+    global_var.hair_style_lora_train_task_sq = Queue()
+    p = Process(target=train_thread, args=(
+        global_var.hair_style_lora_train_task_sq, 0))
+    p.start()
+
+    # # 启动服务
+    # app.run(debug=True, port=32678, host='0.0.0.0')
+
+    server = pywsgi.WSGIServer(('0.0.0.0', 32678), app)  # test port
+    server.serve_forever()
@@ -0,0 +1,362 @@
+# -*- coding: utf-8 -*-
+
+import os
+import sys
+import uuid
+
+from webui_im2im import ControlnetRequestImg2Img
+import numpy as np
+import base64
+import cv2
+import os, sys
+from gevent import pywsgi, monkey
+from multiprocessing import Process, Queue
+import glob
+import datetime
+# from gpt4v_caption import caption_image
+
+# monkey.patch_all()
+# sys.setrecursionlimit(20000)
+
+# 将当前工作目录切换到当前目录
+project_dir = os.path.dirname(os.path.abspath(__file__))
+os.chdir(project_dir)
+sys.path.append(project_dir)
+
+from flask import Flask, request, jsonify
+import global_variable as global_var
+import json
+#from common.logger import config
+
+version = "online"
+if version == "local":
+    current_url = 'http://service.aicloud.fit:7393/'
+    kohya_ss_home_dir = '/home/chinatszrn/Documents/miaoya/kohya_ss_home'
+    webui_lora_dir = '/home/chinatszrn/Documents/miaoya/webui_home/stable-diffusion-webui/models/Lora'
+    inference_use_onediff = False
+    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'
+    inference_use_onediff = False
+    callback_url = 'http://0.0.0.0:8801/api/hair/trainCallBack'
+base_webui_port = '57860'
+base_onediff_port = '9038'
+base_webui_url = "http://127.0.0.1:57860/sdapi/v1/img2img"
+
+
+app = Flask(__name__)
+
+
+def send_request(url, state, task_dict):
+    import requests
+    import json
+
+    msg = '头发lora训练失败' if state == -1 else '头发lora训练成功'
+    payload = json.dumps({
+        "task_id": task_dict['task_id'] if task_dict is not None else '',
+        "hair_id": task_dict['hair_id'] if task_dict is not None else '',
+        "state": state,
+        "msg": msg,
+        "is_tj": task_dict['is_tj'] if task_dict is not None else "0"
+    })
+
+    print("payload:", payload)
+
+    headers = {
+        'Content-Type': 'application/json'
+    }
+
+    requests.request("POST", url, headers=headers, data=payload)
+
+
+@app.route('/api/hair/train', methods=['POST'])
+def train_hair_lora():
+    import os
+    import json
+    task_id = ''
+    try:
+        # 获取请求参数
+        request_data = request.get_json()
+        assert 'task_id' in request_data, 'task_id is required'
+        task_id = request_data['task_id']
+        assert 'hair_id' in request_data and 'hair_material_dir' in request_data, 'hair_id and hair_material_dir is required'
+        hair_material_dir = request_data['hair_material_dir']
+        assert os.path.exists(hair_material_dir), f'{hair_material_dir} not exists'
+        img_dir = os.path.join(hair_material_dir, 'images')
+        assert os.path.exists(img_dir), f'{img_dir} not exists'
+        assert os.path.exists(
+            os.path.join(hair_material_dir, 'model')), f'{os.path.join(hair_material_dir, "model")} not exists'
+
+        #判断img_dir下面是否只有一个文件夹
+        img_dir_list = os.listdir(img_dir)
+        assert len(img_dir_list) == 1, f'{img_dir}下面只能有一个文件夹'
+        train_image_dir = os.path.join(img_dir, img_dir_list[0])
+        assert os.path.isdir(train_image_dir), f'{train_image_dir}不是文件夹'
+        # 检查该文件夹是否以数字加下划线开头
+        assert img_dir_list[0].split('_')[0].isdigit(), f'{img_dir_list[0]}不是数字开头'
+
+        #判断文件夹下面是否有图片
+        img_list = glob.glob(train_image_dir + '/*.png')
+        assert len(img_list) > 10, f'{os.path.join(img_dir, img_dir_list[0])}下面图片数量小于10张'
+        request_data['train_image_dir'] = train_image_dir
+
+        # is tianjin company
+        try:
+            request_data['is_tj'] = request_data['is_tj']
+            request_data['device_id'] = request_data['device_id']
+        except Exception as e:
+            print(e)
+            request_data['is_tj'] = '0'
+            request_data['device_id'] = '1'
+
+        # 将请求数据放入队列
+        global_var.hair_style_lora_train_task_sq.put(request_data)
+
+        # 返回结果
+        ret_dict = dict(state=0, msg='头发lora训练开始', task_id=task_id)
+        return jsonify(ret_dict)
+    except Exception as e:
+        ret_dict = dict(state=-1, msg=str(e), task_id=task_id)
+        return jsonify(ret_dict)
+
+
+@app.route('/api/hair/inference', methods=['POST'])
+def inference_webui():
+    import os
+    import requests
+    import time
+
+
+    try:
+        t1 = time.time()
+        # 获取请求参数
+        request_data = request.get_json()
+        # with open('request_data.json', 'w') as f:
+        #     json.dump(request_data, f)
+        request_json = request_data.get('request_json')
+        # hd_version_flag = request_data.get('hd_version_flag', False)
+        hair_id = request_data['hair_id']
+        inference_port = request_data['inference_port']
+
+        if inference_use_onediff and 'refiner_checkpoint' not in request_json:
+            selected_url = base_webui_url.replace(base_webui_port, base_onediff_port)
+            request_json['script_name'] = 'onediff_diffusion_model'
+            print('user onediff')
+        else:
+            selected_url = base_webui_url.replace(base_webui_port, inference_port)
+
+
+
+        # if hd_version_flag:
+        #     print('user hd version!!!!!')
+        #     lora_file_name = 'hairstyle_hd_lora.safetensors'
+        # else:
+        #     lora_file_name = 'hairstyle_lora.safetensors'
+
+        hair_material_dir = request_data.get('hair_material_dir')
+        print(hair_material_dir)
+        assert os.path.exists(hair_material_dir), f'{hair_material_dir} not exists'
+
+        hd_version_flag = True
+        lora_file_name = 'hairstyle_hd_lora.safetensors'
+        request_lora_path = os.path.join(hair_material_dir, 'model', lora_file_name)
+        if not os.path.exists(request_lora_path):
+            print('use low resolution lora')
+            hd_version_flag = False
+            lora_file_name = 'hairstyle_lora.safetensors'
+            request_lora_path = os.path.join(hair_material_dir, 'model', lora_file_name)
+
+        assert os.path.exists(request_lora_path), f'{request_lora_path} not exists'
+
+        if hd_version_flag:
+            tmp_lora_name = f'{hair_id}_hd'
+        else:
+            tmp_lora_name = f'{hair_id}'
+        lora_dst_path = os.path.join(global_var.webui_lora_dir, f'{tmp_lora_name}.safetensors')
+        if not os.path.exists(lora_dst_path):
+            os.system('cp {} {}'.format(request_lora_path, lora_dst_path))
+        request_json['prompt'] = f'<lora:{tmp_lora_name}:1.0>, titor hairstyle, ' + request_json['prompt']
+        request_json['negative_prompt'] = request_json['negative_prompt'] + ', faceless, no human'
+        print('pre_stage cost:', time.time() - t1)
+
+        start_time = time.time()
+        current_time = datetime.datetime.now()
+        print("*********cxt log1, infer***************, {}:{}:{}, {}".format(current_time.hour, current_time.minute,
+                                                                  current_time.second, selected_url))
+        response = requests.post(url=selected_url, json=request_json)
+        print('inference time:', time.time() - start_time)
+
+        ret_json = response.json()
+        # if os.path.exists(lora_dst_path):
+        #     os.remove(lora_dst_path)
+        # 返回结果
+        return jsonify(ret_json)
+    except Exception as e:
+        ret_dict = dict(state=-1, msg=str(e))
+        return jsonify(ret_dict)
+
+
+@app.route('/api/hair/inference_diy', methods=['POST'])
+def inference_diy_webui():
+    import os
+    import requests
+    import time
+
+    try:
+        t1 = time.time()
+        # 获取请求参数
+        request_data = request.get_json()
+        # with open('request_data.json', 'w') as f:
+        #     json.dump(request_data, f)
+        request_json = request_data.get('request_json')
+        inference_port = request_data['inference_port']
+
+        if inference_use_onediff and 'refiner_checkpoint' not in request_json:
+            selected_url = base_webui_url.replace(base_webui_port, base_onediff_port)
+            request_json['script_name'] = 'onediff_diffusion_model'
+            print('user onediff')
+        else:
+            selected_url = base_webui_url.replace(base_webui_port, inference_port)
+
+        request_json['prompt'] = f'titor hairstyle, ' + request_json['prompt']
+        request_json['negative_prompt'] = request_json['negative_prompt'] + ', faceless, no human'
+        print('pre_stage cost:', time.time() - t1)
+
+        start_time = time.time()
+        current_time = datetime.datetime.now()
+        print("*********cxt log1, diy***************, {}:{}:{}, {}".format(current_time.hour, current_time.minute,
+                                                                  current_time.second, selected_url))
+        response = requests.post(url=selected_url, json=request_json)
+        print('inference time:', time.time() - start_time)
+
+        ret_json = response.json()
+        # if os.path.exists(lora_dst_path):
+        #     os.remove(lora_dst_path)
+        # 返回结果
+        return jsonify(ret_json)
+    except Exception as e:
+        ret_dict = dict(state=-1, msg=str(e))
+        return jsonify(ret_dict)
+
+def train_thread(sq, gpu_id):
+    while True:
+        url = f'{callback_url}'
+        task_dict = None
+        try:
+            task_dict = sq.get()
+            hair_material_dir = task_dict['hair_material_dir']
+            images_dir = os.path.join(hair_material_dir, 'images')
+            model_dir = os.path.join(hair_material_dir, 'model')
+            train_image_dir = task_dict['train_image_dir']
+            # tag = task_dict['tag']
+            tag = ""
+            is_tj = task_dict['is_tj']
+            device_id = task_dict['device_id']
+
+            sample_dir = os.path.join(model_dir, 'sample')
+            if not os.path.exists(sample_dir):
+                os.makedirs(sample_dir)
+            sample_txt = os.path.join(sample_dir, 'prompt.txt')
+            with open(sample_txt, 'w') as f_s:
+                f_s.write('titor hairstyle, easyphoto, faceless, no human, white background, simple background, '
+                          + tag
+                          + ' --n low quality, worst quality, bad anatomy, bad composition, poor, low effort --h 768 '
+                            '--w 768 --s 30 --l 7')
+
+            # 给训练图片打标签
+            # cmd_caption = ('docker run --rm --gpus all -v /home/chinatszrn/Documents/miaoya/kohya_ss_home:/home/chinatszrn '
+            #                '-v /mnt:/mnt -e PATH=/home/chinatszrn/.local/bin -w /home/chinatszrn/kohya_ss '
+            #                '--net=host chinatszrn/ubuntu:kohya_ss accelerate '
+            #                'launch "./finetune/tag_images_by_wd14_tagger.py" --batch_size=2 '
+            #                '--general_threshold=0.5 --character_threshold=0.5 --caption_extension=".txt" '
+            #                '--model="SmilingWolf/wd-v1-4-convnextv2-tagger-v2" --max_data_loader_n_workers="2" '
+            #                '--debug --remove_underscore --frequency_tags --undesired_tags="1girl, 1boy" '
+            #                f'"{train_image_dir}"')
+            # os.system(cmd_caption)
+
+            img_path_list = glob.glob(train_image_dir + '/*.png')
+            # 给训练图片打标签, gpt
+            for img_path in img_path_list:
+                # print(img_path)
+                txt_path = img_path[:-4] + '.txt'
+
+                print("tag img_path: ", img_path)
+                # tags = caption_image(img_path)
+                tags = tag
+                with open(txt_path, 'w') as f:
+                    f.write('titor hairstyle, easyphoto, faceless, no human, white background, simple background, ' + tags)
+
+            #判断是否每个训练图片都有标签文件
+            # for img_path in img_path_list:
+            #     assert os.path.exists(img_path[:-4]+'.txt'), f'{img_path}没有对应的标签文件'
+            #     with open(img_path[:-4]+'.txt', 'r') as f:
+            #         tags = f.readline()
+            #     with open(img_path[:-4]+'.txt', 'w') as f:
+            #         f.write('titor hairstyle, faceless, no human, gray background, simple background, ' + tags)
+            
+            os.system(f'chmod 777 {model_dir}')
+            #训练头发lora
+            # 原始 Docker 命令(已弃用，改为本地 kohya 环境直接执行):
+            #   sudo docker run --rm --privileged=true --gpus "device=1" -v {kohya_ss_home_dir}:/home/chinatszrn ...
+            #   chinatszrn/ubuntu:kohya_ss accelerate launch ./train_network.py ...
+            # 本地化改动：
+            #   1. 去掉 docker 外壳，用 kohya conda 环境的 accelerate 直接跑
+            #   2. 基础模型 majicmixRealistic_v7 不存在，改用本机 v1-5-pruned-emaonly
+            #   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_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'
+            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'--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} '
+                '--network_alpha="64" --save_model_as=safetensors --network_module=networks.lora --text_encoder_lr=5e-05 '
+                '--unet_lr=0.0001 --network_dim=128 --output_name="hairstyle_hd_lora" --lr_scheduler_num_cycles="20" '
+                '--no_half_vae --learning_rate="0.0001" --lr_scheduler="cosine" --lr_warmup_steps="650" --train_batch_size="1" '
+                '--max_train_steps="1500" --save_every_n_epochs="100" --mixed_precision="fp16" --save_precision="fp16" '
+                '--caption_extension=".txt" --sample_sampler=ddim '
+                f'--sample_prompts={sample_txt} --sample_every_n_epochs="1000" '
+                '--seed="1234" --cache_latents --optimizer_type="AdamW" --max_data_loader_n_workers="0" --bucket_reso_steps=64 '
+                '--xformers --bucket_no_upscale --noise_offset=0.0')
+            print("cmd_train:", cmd_train)
+            os.system(cmd_train)
+
+            lora_path = os.path.join(model_dir, 'hairstyle_hd_lora.safetensors')
+
+            # tianjin callback
+            if is_tj == "1":
+                url = f'{callback_url}'
+
+            if not os.path.exists(lora_path):
+                send_request(url, -1, task_dict)
+            else:
+                send_request(url, 0, task_dict)
+
+        except Exception as e:
+            send_request(url, -1, None)
+            continue
+
+
+if __name__ == '__main__':
+    global_var.webui_lora_dir = webui_lora_dir
+
+    # avatar训练的任务队列
+    global_var.hair_style_lora_train_task_sq = Queue()
+    p = Process(target=train_thread, args=(
+        global_var.hair_style_lora_train_task_sq, 0))
+    p.start()
+
+    # # 启动服务
+    # app.run(debug=True, port=32678, host='0.0.0.0')
+
+    server = pywsgi.WSGIServer(('0.0.0.0', 32678), app)  # test port
+    server.serve_forever()
+
+    p.join()
@@ -0,0 +1,190 @@
+import os
+import sys
+import uuid
+
+from webui_im2im import ControlnetRequestImg2Img
+import numpy as np
+import base64
+import cv2
+import os,sys
+from gevent import pywsgi, monkey
+from multiprocessing import Process, Queue
+import glob
+from gpt4v_caption import caption_image
+
+
+# 将当前工作目录切换到当前目录
+project_dir = os.path.dirname(os.path.abspath(__file__))
+os.chdir(project_dir)
+sys.path.append(project_dir)
+
+import json
+
+def send_request(url, state, task_dict):
+    import requests
+    import json
+
+    msg = '头发lora训练失败' if state == -1 else '头发lora训练成功'
+    payload = json.dumps({
+        "task_id": task_dict['task_id'] if task_dict is not None else '',
+        "hair_id": task_dict['hair_id'] if task_dict is not None else '',
+        "state": state,
+        "msg": msg
+    })
+    headers = {
+        'Content-Type': 'application/json'
+    }
+
+    requests.request("POST", url, headers=headers, data=payload)
+
+
+
+def train_hair_lora():
+    import os
+    import json
+    task_id = ''
+    try:
+        # 获取请求参数
+        request_data = ""
+        assert 'task_id' in request_data, 'task_id is required'
+        task_id = request_data['task_id']
+        assert 'hair_id' in request_data and 'hair_material_dir' in request_data, 'hair_id and hair_material_dir is required'
+        hair_material_dir = request_data['hair_material_dir']
+        assert os.path.exists(hair_material_dir), f'{hair_material_dir} not exists'
+        img_dir = os.path.join(hair_material_dir,'images')
+        assert os.path.exists(img_dir), f'{img_dir} not exists'
+        assert os.path.exists(os.path.join(hair_material_dir, 'model')), f'{os.path.join(hair_material_dir, "model")} not exists'
+
+        #判断img_dir下面是否只有一个文件夹
+        img_dir_list = os.listdir(img_dir)
+        assert len(img_dir_list) == 1, f'{img_dir}下面只能有一个文件夹'
+        train_image_dir = os.path.join(img_dir,img_dir_list[0])
+        assert os.path.isdir(train_image_dir), f'{train_image_dir}不是文件夹'
+        # 检查该文件夹是否以数字加下划线开头
+        assert img_dir_list[0].split('_')[0].isdigit(), f'{img_dir_list[0]}不是数字开头'
+
+        #判断文件夹下面是否有图片
+        img_list = glob.glob(train_image_dir + '/*.png')
+        assert len(img_list) > 10, f'{os.path.join(img_dir,img_dir_list[0])}下面图片数量小于10张'
+        request_data['train_image_dir'] = train_image_dir
+    except Exception as e:
+        print(e)
+
+
+def inference_webui():
+    import os
+    import requests
+    url = "http://127.0.0.1:57860/sdapi/v1/img2img"
+    try:
+        # 获取请求参数
+        request_data = ""
+        # with open('request_data.json', 'w') as f:
+        #     json.dump(request_data, f)
+        request_json = request_data.get('request_json')
+        hair_material_dir = request_data.get('hair_material_dir')
+        assert os.path.exists(hair_material_dir), f'{hair_material_dir} not exists'
+        request_lora_path = os.path.join(hair_material_dir, 'model', 'hairstyle_lora.safetensors')
+        assert os.path.exists(request_lora_path), f'{request_lora_path} not exists'
+
+        tmp_lora_name = f'{uuid.uuid4()}'
+        lora_dst_path = ""
+        os.system('cp {} {}'.format(request_lora_path, lora_dst_path))
+        request_json['prompt'] = f'<lora:{tmp_lora_name}:1.0>, titor hairstyle, ' + request_json['prompt']
+        request_json['negative_prompt'] = request_json['negative_prompt'] + ', faceless, no human'
+        response = requests.post(url=url, json=request_json)
+        ret_json = response.json()
+
+        if os.path.exists(lora_dst_path):
+            os.remove(lora_dst_path)
+
+    except Exception as e:
+        print(e)
+
+
+def tag(train_image_dir):
+    img_path_list = glob.glob(train_image_dir + '/*.png')
+
+    # 给训练图片打标签, gpt
+    for img_path in img_path_list:
+        print(img_path)
+        txt_path = img_path[:-4] + '.txt'
+        # if not os.path.exists(txt_path):
+        tags = caption_image(img_path)
+        with open(txt_path, 'w') as f:
+            f.write('titor hairstyle, faceless, no human, gray background, simple background, ' + tags)
+        # else:
+        #     continue
+
+
+def train_thread(sq, gpu_id):
+    while True:
+        url = 'http://service.aicloud.fit:7393/api/hair/trainCallBack'
+        task_dict = None
+        try:
+            task_dict = sq.get()
+            hair_material_dir = task_dict['hair_material_dir']
+            images_dir = os.path.join(hair_material_dir, 'images')
+            model_dir =  os.path.join(hair_material_dir, 'model')
+            train_image_dir = task_dict['train_image_dir']
+            # 给训练图片打标签
+            # cmd_caption = ('docker run --rm --gpus all -v /home/chinatszrn/Documents/miaoya/kohya_ss_home:/home/chinatszrn '
+            #                '-v /mnt:/mnt -e PATH=/home/chinatszrn/.local/bin -w /home/chinatszrn/kohya_ss '
+            #                '--net=host chinatszrn/ubuntu:kohya_ss accelerate '
+            #                'launch "./finetune/tag_images_by_wd14_tagger.py" --batch_size=2 '
+            #                '--general_threshold=0.5 --character_threshold=0.5 --caption_extension=".txt" '
+            #                '--model="SmilingWolf/wd-v1-4-convnextv2-tagger-v2" --max_data_loader_n_workers="2" '
+            #                '--debug --remove_underscore --frequency_tags --undesired_tags="1girl, 1boy" '
+            #                f'"{train_image_dir}"')
+            # os.system(cmd_caption)
+
+            img_path_list = glob.glob(train_image_dir + '/*.png')
+            # 给训练图片打标签, gpt
+            for img_path in img_path_list:
+                tags = caption_image(img_path)
+                txt_path = img_path[:-4]+'.txt'
+                if not os.path.exists(txt_path):
+                    with open(txt_path, 'w') as f:
+                        f.write('titor hairstyle, faceless, no human, gray background, simple background, ' + tags)
+                else:
+                    continue
+
+
+            #判断是否每个训练图片都有标签文件
+            # for img_path in img_path_list:
+            #     assert os.path.exists(img_path[:-4]+'.txt'), f'{img_path}没有对应的标签文件'
+            #     with open(img_path[:-4]+'.txt', 'r') as f:
+            #         tags = f.readline()
+            #     with open(img_path[:-4]+'.txt', 'w') as f:
+            #         f.write('titor hairstyle, faceless, no human, gray background, simple background, ' + tags)
+
+            #训练头发lora
+            cmd_train = ('docker run --rm --gpus all -v /home/chinatszrn/Documents/miaoya/kohya_ss_home:/home/chinatszrn -v '
+                         '/mnt:/mnt -e PATH=/home/chinatszrn/.local/bin -w /home/chinatszrn/kohya_ss '
+                         '--net=host chinatszrn/ubuntu:kohya_ss accelerate launch --num_cpu_threads_per_process=2 "./train_network.py" --enable_bucket '
+                         '--min_bucket_reso=512 --max_bucket_reso=1800 --pretrained_model_name_or_path="/mnt/nas_hdd/米亚像馆/models/Stable-diffusion/majicmixRealistic_v7.safetensors" '
+                         f'--train_data_dir={images_dir} --resolution="1800,1800" '
+                         f'--output_dir={model_dir} '
+                         '--network_alpha="64" --save_model_as=safetensors --network_module=networks.lora --text_encoder_lr=5e-05 '
+                         '--unet_lr=0.0001 --network_dim=128 --output_name="hairstyle_lora" --lr_scheduler_num_cycles="12" '
+                         '--no_half_vae --learning_rate="0.0001" --lr_scheduler="cosine" --lr_warmup_steps="96" --train_batch_size="1" '
+                         '--max_train_steps="4000" --save_every_n_epochs="100" --mixed_precision="fp16" --save_precision="fp16" '
+                         '--seed="1234" --cache_latents --optimizer_type="AdamW8bit" --max_data_loader_n_workers="0" --bucket_reso_steps=64 '
+                         '--xformers --bucket_no_upscale --noise_offset=0.0 --tokenizer_cache_dir="/home/chinatszrn/.cache/clip"')
+            os.system(cmd_train)
+
+            lora_path = os.path.join(model_dir, 'hairstyle_lora.safetensors')
+
+
+            if not os.path.exists(lora_path):
+                send_request(url, -1, task_dict)
+            else:
+                send_request(url, 0, task_dict)
+
+        except Exception as e:
+            send_request(url, -1, None)
+            continue
+
+
+if __name__ == '__main__':
+
+    tag("/mnt/database2/online-server/hair-online/hair_lora_train_material/AAVWGW1NN-0KC-33B-24-/images/1_hairstyle")
@@ -0,0 +1,37 @@
+import requests
+import cv2
+import numpy as np
+import base64
+import sys
+import json
+
+if __name__ == '__main__':
+    path = './data/template01.png'
+    img = cv2.imread(path)
+    retval, bytes = cv2.imencode('.jpg', img)
+    encoded_image = base64.b64encode(bytes).decode('utf-8')
+    user_id = '61AB9097'
+
+    #给api_service.py发送请求
+    url = "http://i-2.gpushare.com:53412/user/generate"
+    payload = json.dumps({
+        "user_id": "61AB9097",
+        "base_img": encoded_image
+    })
+    headers = {
+        'Content-Type': 'application/json'
+    }
+    response = requests.request("POST", url, headers=headers, data=payload)
+
+    if response.status_code != 200:
+        raise RuntimeError(f"Failed to send request to API service. Status code: {response.status_code}")
+
+    ret_image_b64 = response.json().get('generate_photo_b64')
+
+    if ret_image_b64 is None:
+        raise RuntimeError(f"ret image failed!")
+
+    image_array = np.frombuffer(base64.b64decode(ret_image_b64), np.uint8)
+    image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+    cv2.imshow('image', cv2.resize(image, (0, 0), fx=0.5, fy=0.5))
+    cv2.waitKey()
@@ -0,0 +1 @@
+python api_service.py /hy-tmp/photo_service/service_data /hy-tmp/stable-diffusion-webui/models/Lora 7860 1234
@@ -0,0 +1,18 @@
+import cv2
+import os
+import json
+import base64
+from PIL import Image
+import io
+
+if __name__ == '__main__':
+    with open('request_json.json', 'r') as f:
+        request_data = json.load(f)
+    request_json = request_data
+    img_base64 = request_json['init_images'][0]
+    mask_base64 = request_json['mask']
+    image = Image.open(io.BytesIO(base64.b64decode(img_base64)))
+    image.save('image.png')
+    mask = Image.open(io.BytesIO(base64.b64decode(mask_base64)))
+    mask.save('mask.png')
+
@@ -0,0 +1,106 @@
+import io
+import cv2
+import base64
+import requests
+from PIL import Image
+import numpy as np
+
+"""
+    To use this example make sure you've done the following steps before executing:
+    1. Ensure automatic1111 is running in api mode with the controlnet extension. 
+       Use the following command in your terminal to activate:
+            ./webui.sh --no-half --api
+    2. Validate python environment meet package dependencies.
+       If running in a local repo you'll likely need to pip install cv2, requests and PIL 
+"""
+
+
+class ControlnetRequestImg2Img:
+    def __init__(self, prompt, net_prompt):
+        self.url = "http://127.0.0.1:7860/sdapi/v1/img2img"
+        self.prompt = prompt
+        self.neg_prompt = net_prompt
+        self.body = None
+
+    def build_body(self, dst_width, dst_height, cfg_scale, base_img):
+
+        self.body = {
+            "prompt": self.prompt,
+            "negative_prompt": self.neg_prompt,
+            "sampler_name": "Restart",
+            "batch_size": 1,
+            "steps": 30,
+            "width": dst_width,
+            "height": dst_height,
+            "cfg_scale": cfg_scale,
+            "seed": -1,
+            "init_images": [
+                self.encode_image_to_base64(base_img)
+            ],
+            "denoising_strength": 0.4,
+            "alwayson_scripts": {
+                "controlnet": {
+                    "args": [
+                        {
+                            "enabled": True,
+                            "module": "openpose_full",
+                            "model": "openpose",
+                            "weight": 1.0,
+                            # "image": self.read_image(),
+                            "resize_mode": "Crop and Resize",
+                            "low_vram": False,
+                            "processor_res": 512,
+                            "guidance_start": 0.0,
+                            "guidance_end": 1.0,
+                            "control_mode": "Balanced",
+                            "pixel_perfect": True
+                        }
+                    ]
+                }
+            }
+        }
+
+    def send_request(self):
+        response = requests.post(url=self.url, json=self.body)
+        return response.json()
+
+    def encode_image_to_base64(self, img):
+        retval, bytes = cv2.imencode('.png', img)
+        encoded_image = base64.b64encode(bytes).decode('utf-8')
+        return encoded_image
+
+    def read_image(self):
+        img = cv2.imread(self.img_path)
+        retval, bytes = cv2.imencode('.png', img)
+        encoded_image = base64.b64encode(bytes).decode('utf-8')
+        return encoded_image
+    
+    def read_mask(self):
+        img = cv2.imread(self.mask)
+        retval, bytes = cv2.imencode('.png', img)
+        encoded_image = base64.b64encode(bytes).decode('utf-8')
+        return encoded_image
+
+def encode_image_to_base64(img):
+    retval, bytes = cv2.imencode('.jpg', img)
+    encoded_image = base64.b64encode(bytes).decode('utf-8')
+    return encoded_image
+
+
+
+if __name__ == '__main__':
+    path = '/home/chinatszrn/Downloads/photo_service/service_data/template_data/template01.png'
+    img = cv2.imread(path)
+    prompt = '<lora:5b05d5eeee0188f436d7131c4f0ff52b:0.8>,easyphoto_face, easyphoto, 1person,face,suit'
+    neg_prompt = '(worst quality:2),(low quality:2),(normal quality:2),lowres,watermark'
+
+
+    control_net = ControlnetRequestImg2Img(prompt, neg_prompt)
+    control_net.build_body(dst_width=img.shape[1], dst_height=img.shape[0], cfg_scale=3.5, base_img=img)
+    output = control_net.send_request()
+    result = output['images'][0]
+
+    image_array = np.frombuffer(base64.b64decode(result.split(",", 1)[0]), np.uint8)
+    image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+    cv2.imshow('image', image)
+    cv2.waitKey()
@@ -0,0 +1,86 @@
+import io
+import cv2
+import base64
+import requests
+from PIL import Image
+
+class ControlnetRequestImg2Img:
+    def __init__(self, prompt, net_prompt, path, mask):
+        self.url = "http://127.0.0.1:57860/sdapi/v1/img2img"
+        self.prompt = prompt
+        self.neg_prompt = net_prompt
+        self.img_path = path
+        self.mask = mask
+        self.body = None
+
+    def build_body(self):
+        img = cv2.imread(self.img_path)
+        self.body = {
+            "prompt": self.prompt,
+            "negative_prompt": self.neg_prompt,
+            "sampler_name": "DPM++ 2M Karras",
+            "batch_size": 1,
+            "steps": 30,
+            "width": img.shape[1],
+            "height": img.shape[0],
+            "cfg_scale": 7,
+            "seed": -1,
+            "mask_blur": 15,
+            "init_images": [
+                self.read_image()
+            ],
+            "inpaint_full_res": True,
+            "inpainting_fill": 1,
+            "inpainting_mask_invert": 1,
+            "mask": self.read_mask(),
+            "denoising_strength": 0.4,
+            "alwayson_scripts": {
+                "controlnet": {
+                    "args": [
+                        {
+                            "enabled": True,
+                            "module": "openpose_full",
+                            "model": "openpose",
+                            "weight": 1.0,
+                            "resize_mode": 1,
+                            "lowvram": False,
+                            "processor_res": 512,
+                            "guidance_start": 0.0,
+                            "guidance_end": 1.0,
+                            "control_mode": 0,
+                            "pixel_perfect": True
+                        },
+                    ]
+                },
+            }
+        }
+
+    def send_request(self):
+        response = requests.post(url=self.url, json=self.body)
+        return response.json()
+
+    def read_image(self):
+        img = cv2.imread(self.img_path)
+        retval, bytes = cv2.imencode('.png', img)
+        encoded_image = base64.b64encode(bytes).decode('utf-8')
+        return encoded_image
+
+    def read_mask(self):
+        img = cv2.imread(self.mask)
+        retval, bytes = cv2.imencode('.png', img)
+        encoded_image = base64.b64encode(bytes).decode('utf-8')
+        return encoded_image
+
+
+if __name__ == '__main__':
+    path = '/home/chinatszrn/Downloads/user1_hr.png'
+    mask_path = '/home/chinatszrn/Downloads/user1_hr_mask.png'
+    prompt = 'a woman with long blonde hair and a blue shirt on a gray background with a gray background and a gray background, lyco art, An Gyeon, realistic face, a character portrait'
+    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'
+
+    control_net = ControlnetRequestImg2Img(prompt, neg_prompt, path, mask_path)
+    control_net.build_body()
+    output = control_net.send_request()
+    result = output['images'][0]
+    image = Image.open(io.BytesIO(base64.b64decode(result.split(",", 1)[0])))
+    image.save('save2.png')
				`@@ -0,0 +1 @@`
				`python api_service.py /hy-tmp/photo_service/service_data /hy-tmp/stable-diffusion-webui/models/Lora 7860 1234`