Update app.py

app.py

@@ -65,11 +65,16 @@ def get_normal_map(image):
 
 
 @spaces.GPU
-def generate_(prompt, negative_prompt, canny_image, num_steps, controlnet_conditioning_scale, seed):
+def generate_(prompt, negative_prompt, normal_image, num_steps, controlnet_conditioning_scale, seed):
     generator = torch.Generator("cuda").manual_seed(seed)    
     images = pipe(
-    prompt, negative_prompt=negative_prompt, image=canny_image, num_inference_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale),
-    generator=generator,
+        prompt, 
+        negative_prompt=negative_prompt, 
+        image=normal_image, 
+        num_inference_steps=num_steps, 
+        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
+        num_images_per_prompt=4,
+        generator=generator,
     ).images
     return images
 
@@ -80,7 +85,7 @@ def process(normal_image, prompt, negative_prompt, num_steps, controlnet_conditi
     # depth_image = get_depth_map(input_image)
     images = generate_(prompt, negative_prompt, normal_image, num_steps, controlnet_conditioning_scale, seed)
 
-    return [normal_image, images[0]]
+    return [images[0], images[1], images[2], images[3]]
 
 
 def run_demo():
@@ -111,12 +116,12 @@ def run_demo():
                 prompt = gr.Textbox(label="Prompt")
                 negative_prompt = gr.Textbox(visible=False, 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)
+                controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=1.0, value=0.95, 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():
-                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[2], height='auto')
+                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[2], rows=[2], height='auto')
         ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed]
         
         run_button.click(fn=process, inputs=ips, outputs=[result_gallery])