Update app.py

app.py

@@ -118,8 +118,11 @@ def estimate_depth(pil_image: Image.Image) ->Image.Image:
 
 
 def generate_image_for_gradio(
+    prompt: str,
     input_image_for_depth: Image.Image,
-    prompt: str
+    num_inference_step: int,
+    guidance_scale: float,
+    
 ) -> Image.Image:
 
     global pipeline
@@ -152,49 +155,26 @@ def generate_image_for_gradio(
         generated_images = pipeline(
             prompt,
             image=input_image_for_pipeline,
-            num_inference_steps=25,
-            guidance_scale=8.0,
+            num_inference_steps=num_inference_step,
+            guidance_scale = guidance_scale,
             generator=generator,
         ).images
 
-    # with torch.no_grad():
-    #     generated_images = pipeline(
-    #         prompt,
-    #         negative_prompt,
-    #         image=input_image_for_pipeline,
-    #         num_inference_steps=25,
-    #         # guidance_scale=8.0,
-    #         strength = 0.85,
-    #         generator=generator,
-    #     ).images
-
+    
     print(f"Image generation complete (seed: {seed}).")
     return generated_images[0]
 
 
 
-# iface = gr.Interface(
-#     fn=generate_image_for_gradio,
-#     inputs=[
-#         gr.Textbox(label="Prompt", value="a high-quality photo of a modern interior design"),
-#         gr.Image(type="pil", label="Input Image (for Depth Estimation)"),
-#         gr.Slider(minimum=10, maximum=100, value=25, step=1, label="Inference Steps"),
-#         gr.Slider(minimum=1.0, maximum=20.0, value=8.0, step=0.5, label="Guidance Scale"),
-#         gr.Number(label="Seed (optional, leave blank for random)", value=None),
-#         gr.Number(label="Resolution", value=512, interactive=False)
-#     ],
-#     outputs=gr.Image(type="pil", label="Generated Image"),
-#     title="Stable Diffusion ControlNet Depth Demo (with Depth Estimation)",
-#     description="Upload an input image, and the app will estimate its depth map, then use it with your prompt to generate a new image. This allows for structural guidance from your input photo.",
-#     allow_flagging="never",
-#     live=False,
-#     theme=Soft(),
-
 iface = gr.Interface(
     fn=generate_image_for_gradio,
     inputs=[
-        gr.Image(type="pil", label="Input Image (for Depth Estimation)"),
         gr.Textbox(label="Prompt", value="a high-quality photo of a modern interior design"),
+        gr.Image(type="pil", label="Input Image (for Depth Estimation)"),
+        gr.Slider(minimum=10, maximum=100, value=25, step=1, label="Inference Steps"),
+        gr.Slider(minimum=1.0, maximum=20.0, value=8.0, step=0.5, label="Guidance Scale"),
+        # gr.Number(label="Seed (optional, leave blank for random)", value=None),
+        # gr.Number(label="Resolution", value=512, interactive=False)
     ],
     outputs=gr.Image(type="pil", label="Generated Image"),
     title="Stable Diffusion ControlNet Depth Demo (with Depth Estimation)",