Upload app.py

app.py

@@ -4,10 +4,8 @@ import numpy as np
 import torch
 from PIL import Image
 import gradio as gr
-from gradio_imageslider import ImageSlider
 
 # 延迟 CUDA 初始化
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 weight_dtype = torch.float32
 
 # 加载模型组件
@@ -19,6 +17,7 @@ from transformers import CLIPTextModel, AutoTokenizer
 
 pretrained_model_name_or_path = "sjtu-deepvision/dereflection-any-image-v0"
 pretrained_model_name_or_path2 = "stabilityai/stable-diffusion-2-1"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # 加载模型
 controlnet = ControlNetVAEModel.from_pretrained(pretrained_model_name_or_path, subfolder="controlnet", torch_dtype=weight_dtype).to(device)
@@ -41,7 +40,6 @@ pipe = DAIPipeline(
     t_start=0,
 ).to(device)
 
-# 使用 spaces.GPU 包装推理函数
 @spaces.GPU
 def process_image(input_image):
     # 将 Gradio 输入转换为 PIL 图像
@@ -60,8 +58,7 @@ def process_image(input_image):
     processed_frame = (processed_frame[0] * 255).astype(np.uint8)
     processed_frame = Image.fromarray(processed_frame)
 
-    # 返回输入图像和处理后的图像
-    return input_image, processed_frame
+    return processed_frame
 
 # 创建 Gradio 界面
 def create_gradio_interface():
@@ -77,18 +74,13 @@ def create_gradio_interface():
                 input_image = gr.Image(label="Input Image", type="numpy")
                 submit_btn = gr.Button("Remove Reflection", variant="primary")
             with gr.Column():
-                # 使用 ImageSlider 显示前后对比
-                output_slider = ImageSlider(
-                    label="Before & After",
-                    show_download_button=True,
-                    show_share_button=True,
-                )
+                output_image = gr.Image(label="Processed Image")
 
         # 添加示例
         gr.Examples(
             examples=example_images,
             inputs=input_image,
-            outputs=output_slider,
+            outputs=output_image,
             fn=process_image,
             cache_examples=False,  # 缓存结果以加快加载速度
             label="Example Images",
@@ -98,7 +90,7 @@ def create_gradio_interface():
         submit_btn.click(
             fn=process_image,
             inputs=input_image,
-            outputs=output_slider,
+            outputs=output_image,
         )
 
     return demo
@@ -106,7 +98,7 @@ def create_gradio_interface():
 # 主函数
 def main():
     demo = create_gradio_interface()
-    demo.queue().launch(show_api=False)
+    demo.launch(server_name="0.0.0.0", server_port=7860)
 
 if __name__ == "__main__":
     main()