de

app.py

@@ -107,29 +107,6 @@ def predict_image(cond_image, prompt, negative_prompt, controlnet_conditioning_s
   
   return image
 
-# block = gr.Blocks().queue()
-
-# with block:
-#     with gr.Row():
-#         with gr.Column():
-#             input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
-#             prompt = gr.Textbox(label="Prompt")
-#             negative_prompt = gr.Textbox(label="Negative prompt", value="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers")
-#             num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1)
-#             controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05)
-#             seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,)
-#             run_button = gr.Button(value="Run")
-                        
-#         with gr.Column():
-#             with gr.Row():
-#                 pose_image_output = gr.Image(label="Pose Image", type="pil", interactive=False)
-#                 generated_image_output = gr.Image(label="Generated Image", type="pil", interactive=False)
-
-#     ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed]
-#     run_button.click(fn=process, inputs=ips, outputs=[pose_image_output, generated_image_output])
-
-
-# block.launch(debug = True)
 
 def convert_pil_to_opencv(pil_image):
   return np.array(pil_image)
@@ -243,8 +220,27 @@ def outpaint_image(image):
   
   return image
 
-# Gradioアプリケーション
-with gr.Blocks() as demo:
+block = gr.Blocks().queue()
+
+with block:
+  with gr.Row():
+    with gr.Column():
+      input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
+      prompt = gr.Textbox(label="Prompt")
+      negative_prompt = gr.Textbox(label="Negative prompt", value="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers")
+      num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1)
+      controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05)
+      seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,)
+      run_button = gr.Button(value="Run")
+                    
+    with gr.Column():
+      with gr.Row():
+        pose_image_output = gr.Image(label="Pose Image", type="pil", interactive=False)
+        generated_image_output = gr.Image(label="Generated Image", type="pil", interactive=False)
+
+  ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed]
+  run_button.click(fn=process, inputs=ips, outputs=[pose_image_output, generated_image_output])
+
   gr.Markdown("## Position Map Visualizer")
   
   with gr.Row():
@@ -271,4 +267,4 @@ with gr.Blocks() as demo:
   predict_map_btn.click(predict_image, inputs=[img1, prompt, negative_prompt, controlnet_conditioning_scale], outputs=img2)
   visualize_3d_btn.click(visualize_3d, inputs=[img2, img1], outputs=reconstruction_output)
 
-demo.launch()
+block.launch(debug = True)