Update app.py

app.py

@@ -54,12 +54,12 @@ def resize_image(input_path, output_path, target_height):
 
     return output_path
 
-def infer(image_in, prompt):
-    prompt = 'Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see a big rain approaching. text "InstantX" on image'
+def infer(image_in, prompt, inference_steps, guidance_scale, control_weight):
+    
     n_prompt = 'NSFW, nude, naked, porn, ugly'
 
+    # Canny preprocessing
     image_to_canny = load_image(image_in)
-
     image_to_canny = np.array(image_to_canny)
     image_to_canny = cv2.Canny(image_to_canny, 100, 200)
     image_to_canny = image_to_canny[:, :, None]
@@ -71,38 +71,53 @@ def infer(image_in, prompt):
         dict(
             control_index=0,
             control_image=image_to_canny,
-            control_weight=0.7,
+            control_weight=control_weight,
             control_pooled_projections='zeros'
         )
     ]
+    
     # infer
     image = pipe(
         prompt=prompt,
         negative_prompt=n_prompt,
         controlnet_conditioning=controlnet_conditioning,
-        num_inference_steps=28,
-        guidance_scale=7.0,
-        height=1024,
-        width=1024,
+        num_inference_steps=inference_steps,
+        guidance_scale=guidance_scale,
     ).images[0]
 
-    return image
-
+    return image, image_to_canny
 
-with gr.Blocks() as demo:
-    with gr.Column():
+css="""
+#col-container{
+    margin: 0 auto;
+    max-width: 1080px;
+}
+"""
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
         gr.Markdown("""
         # SD3 ControlNet
         """)
-        image_in = gr.Image(label="Image reference", sources=["upload"], type="filepath")
-        prompt = gr.Textbox(label="Prompt")
-        submit_btn = gr.Button("Submit")
-        result = gr.Image(label="Result")
+        with gr.Row():
+            with gr.Column():
+                image_in = gr.Image(label="Image reference", sources=["upload"], type="filepath")
+                prompt = gr.Textbox(label="Prompt")
+                with gr.Accordion("Advanced settings", open=False):
+                    with gr.Column():
+                        with gr.Row():
+                            inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=50, step=1, value=25)
+                            guidance_scale = gr.Slider(label="Guidance scale", minimum=1.0, maximum=10.0, step=0.1, value=7.0)
+                        control_weight = gr.Slider(label="Control Weight", minimum=0.0, maximum=1.0, step=0.01, value=0.7)
+                    
+                submit_btn = gr.Button("Submit")
+            with gr.Column():
+                result = gr.Image(label="Result")
+                canny_used = gr.Image(label="Preprocessed Canny")
     
     submit_btn.click(
         fn = infer,
-        inputs = [image_in, prompt],
-        outputs = [result],
+        inputs = [image_in, prompt, inference_steps, guidance_scale, control_weight],
+        outputs = [result, canny_used],
         show_api=False
     )
 demo.queue().launch()