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app.py

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+#pip install "modelscope[cv]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
+#pip install gradio
+#pip install tensorflow
+
+from tqdm import tqdm
+from skimage import io
+import datetime
+import os
+import gradio as gr
+from PIL import Image
+from translate import Translator
+from gradio_client import Client
+import json
+
+# 初始化Translator对象,指定源语言和目标语言
+translator = Translator(from_lang="zh", to_lang="en")
+
+#获取当前北京时间
+utc_dt = datetime.datetime.utcnow()
+beijing_dt = utc_dt.astimezone(datetime.timezone(datetime.timedelta(hours=8)))
+formatted = beijing_dt.strftime("%Y-%m-%d_%H")
+print(f"北京时间: {beijing_dt.year}年{beijing_dt.month}月{beijing_dt.day}日 "
+      f"{beijing_dt.hour}时{beijing_dt.minute}分{beijing_dt.second}秒")
+#创建作品存放目录
+works_path = 'works_text_image_api/' + formatted
+if not os.path.exists(works_path):
+  os.makedirs(works_path)
+print('作品目录：' + works_path)
+#创建用户上传图片存放目录
+user_upload_path = 'user_upload/' + formatted
+if not os.path.exists(user_upload_path):
+  os.makedirs(user_upload_path)
+print('用户图片目录：' + user_upload_path)
+
+def get_size(h, w, max = 720):
+    if min(h, w) > max:
+        if h > w:
+            h, w = int(max * h / w), max
+        else:
+            h, w = max, int(max * w / h)
+    
+    return h, w
+        
+def inference(original_prompt: str, image: Image) -> Image:
+  #调整图片尺寸，避免过大导致处理耗时过久
+  w, h = image.size
+  print(f'原图片宽：{w}，高：{h}')
+  h, w = get_size(h, w, 720)
+  image = image.resize((w, h))
+  print(f'调整尺寸后图片宽：{w}，高：{h}')
+
+  print('图片描述：' + original_prompt)
+  translate_prompt = translator.translate(original_prompt) #翻译为英文
+  print('translate_prompt：' + translate_prompt)
+    
+  utc_dt = datetime.datetime.utcnow()
+  beijing_dt = utc_dt.astimezone(datetime.timezone(datetime.timedelta(hours=8)))
+  formatted = beijing_dt.strftime("%Y-%m-%d_%H-%M-%S.%f")
+  image_path = user_upload_path + '/' + formatted + '.png'
+  print('用户图片：' + image_path)
+  image.save(image_path)
+  
+  # https://huggingface.co/spaces/hysts/ControlNet-v1-1
+  client = Client("https://hysts-controlnet-v1-1.hf.space/")
+  result = client.predict(
+        image_path,    # str (filepath or URL to image) in 'parameter_98' Image component
+        translate_prompt,    # str  in 'Prompt' Textbox component
+        "masterpiece, best quality, extremely detailed",    # str  in 'Additional prompt' Textbox component
+        "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",    # str  in 'Negative prompt' Textbox component
+        1,    # int | float (numeric value between 1 and 1) in 'Number of images' Slider component
+        512,    # int | float (numeric value between 256 and 512) in 'Image resolution' Slider component
+        512,    # int | float (numeric value between 128 and 512) in 'Preprocess resolution' Slider component
+        20,    # int | float (numeric value between 1 and 100) in 'Number of steps' Slider component
+        9.0,    # int | float (numeric value between 0.1 and 30.0) in 'Guidance scale' Slider component
+        706138,    # int | float (numeric value between 0 and 1000000) in 'Seed' Slider component
+        "HED",    # str  in 'Preprocessor' Radio component
+        api_name="/softedge"
+  )
+
+  print(result)
+  result += '/captions.json'
+
+  with open(result) as f:
+    data = json.load(f)
+
+  # data 为Python对象
+  print(data)
+  
+  i = 0
+  for key in data.keys():
+    if i == 1:
+        result_path = key
+        break
+    
+    i += 1
+
+  res_img = Image.open(result_path)
+  print('作品：' + result_path)
+
+  return res_img
+
+
+css_style = "#fixed_size_img {height: 240px;} "
+
+title = "人像创作 by宁侠"
+description = '''
+我们提供的服务能够快速高效地将您提供的人像图片转化为栩栩如生的肖像图，您只需简单地输入图片描述，我们的服务便会根据您的要求对图片进行处理，让您获得一张高质量的肖像图。我们期待着为您提供最好的服务，并让您的体验更加愉快。
+'''
+examples_path = 'examples/'
+examples = [[examples_path + 'input1.png'], [examples_path + 'input2.png'], [examples_path + 'input3.png'], [examples_path + 'input4.png']]
+
+with gr.Blocks(title=title, css=css_style) as demo:
+    gr.HTML('''
+      <div style="text-align: center; max-width: 720px; margin: 0 auto;">
+                  <div
+                    style="
+                      display: inline-flex;
+                      align-items: center;
+                      gap: 0.8rem;
+                      font-size: 1.75rem;
+                    "
+                  >
+                    <h1 style="font-family: PingFangSC; font-weight: 500; line-height: 1.5em; font-size: 32px; margin-bottom: 7px;">
+                      人像创作
+                    </h1>
+                    <h1 style="font-family: PingFangSC; font-weight: 500; line-height: 1.5em; font-size: 16px; margin-bottom: 7px;">
+                      by宁侠
+                    </h1>
+                  </div>
+                  <img id="overview" alt="overview" src="https://github.com/lllyasviel/ControlNet/raw/main/github_page/p5.png" />
+                </div>
+      ''')
+
+    gr.Markdown(description)
+    with gr.Row():
+        original_prompt = gr.Textbox(label="请输入图片描述", value="英俊青年的油画，杰作")
+    with gr.Row():
+        img_input = gr.Image(label="图片", type="pil", elem_id="fixed_size_img")
+        img_output = gr.Image(label="作品", type="pil", elem_id="fixed_size_img")
+    with gr.Row():
+        btn_submit = gr.Button(value="一键创作", elem_id="blue_btn")
+        # btn_clear = gr.Button(value="清除")
+
+    examples = gr.Examples(examples=examples, inputs=[img_input], outputs=img_output)
+    btn_submit.click(inference, inputs=[original_prompt, img_input], outputs=img_output)
+    # btn_clear清除画布
+
+demo.queue(api_open=False).launch(debug=True)

requirements.txt

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+translate