Update app.py

app.py

@@ -102,128 +102,9 @@ def fill_image(image, model_selection):
     cnet_image.paste(image, (0, 0), mask)
 
     yield background, cnet_image
-    
-
-@spaces.GPU
-def fill_image(image, model_selection):
-    source = image
-    target_ratio=(9, 16)
-    target_height=1280
-    overlap=48
-    fade_width=24
-    max_width = 720
-    # Resize the image if it's wider than max_width
-    if source.width > max_width:
-        scale_factor = max_width / source.width
-        new_width = max_width
-        new_height = int(source.height * scale_factor)
-        source = source.resize((new_width, new_height), Image.LANCZOS)
-    
-    # Calculate the required height for 9:16 ratio
-    target_height = (source.width * target_ratio[1]) // target_ratio[0]
-    
-    # Calculate margins (only top and bottom)
-    margin_y = (target_height - source.height) // 2
-    
-    # Calculate new output size
-    output_size = (source.width, target_height)
-    
-    # Create a white background
-    background = Image.new('RGB', output_size, (255, 255, 255))
-    
-    # Calculate position to paste the original image
-    position = (0, margin_y)
-    
-    # Paste the original image onto the white background
-    background.paste(source, position)
-    
-    # Create the mask
-    mask = Image.new('L', output_size, 255)  # Start with all white
-    mask_draw = ImageDraw.Draw(mask)
-    mask_draw.rectangle([
-        (overlap, margin_y + overlap),
-        (source.width - overlap, margin_y + source.height - overlap)
-    ], fill=0)
-    
-    # Prepare the image for ControlNet
-    cnet_image = background.copy()
-    cnet_image.paste(0, (0, 0), mask)
-
-    for image in pipe(
-        prompt_embeds=prompt_embeds,
-        negative_prompt_embeds=negative_prompt_embeds,
-        pooled_prompt_embeds=pooled_prompt_embeds,
-        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-        image=cnet_image,
-    ):
-        yield image, cnet_image
-
-    image = image.convert("RGBA")
-    cnet_image.paste(image, (0, 0), mask)
-
-    yield background, cnet_image
-
-
-def fill_image(image, model_selection):
-    source = image
-    target_ratio = (16, 9)  # Set the new target ratio to 16:9
-    target_width = 1280  # Adjust target width based on desired resolution
-    overlap = 48
-    fade_width = 24
-    max_height = 720  # Adjust max height instead of width
-    
-    # Resize the image if it's taller than max_height
-    if source.height > max_height:
-        scale_factor = max_height / source.height
-        new_height = max_height
-        new_width = int(source.width * scale_factor)
-        source = source.resize((new_width, new_height), Image.LANCZOS)
-    
-    # Calculate the required width for the 16:9 ratio
-    target_width = (source.height * target_ratio[0]) // target_ratio[1]
-    
-    # Calculate margins (now left and right)
-    margin_x = (target_width - source.width) // 2
-    
-    # Calculate new output size
-    output_size = (target_width, source.height)
-    
-    # Create a white background
-    background = Image.new('RGB', output_size, (255, 255, 255))
-    
-    # Calculate position to paste the original image
-    position = (margin_x, 0)
-    
-    # Paste the original image onto the white background
-    background.paste(source, position)
-    
-    # Create the mask
-    mask = Image.new('L', output_size, 255)  # Start with all white
-    mask_draw = ImageDraw.Draw(mask)
-    mask_draw.rectangle([
-        (margin_x + overlap, overlap),
-        (margin_x + source.width - overlap, source.height - overlap)
-    ], fill=0)
-    
-    # Prepare the image for ControlNet
-    cnet_image = background.copy()
-    cnet_image.paste(0, (0, 0), mask)
-
-    for image in pipe(
-        prompt_embeds=prompt_embeds,
-        negative_prompt_embeds=negative_prompt_embeds,
-        pooled_prompt_embeds=pooled_prompt_embeds,
-        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-        image=cnet_image,
-    ):
-        yield image, cnet_image
-
-    image = image.convert("RGBA")
-    cnet_image.paste(image, (0, 0), mask)
-
-    yield background, cnet_image
 """
 
+@spaces.GPU
 def infer(image, model_selection, ratio_choice):
 
     source = image
@@ -361,40 +242,46 @@ title = """<h1 align="center">Diffusers Image Outpaint</h1>
 """
 
 with gr.Blocks(css=css) as demo:
-    gr.HTML(title)
-
-    run_button = gr.Button("Generate")
-
-    with gr.Row():
-        input_image = gr.Image(
-            type="pil",
-            label="Input Image",
-            sources=["upload"],
-        )
-
-        result = ImageSlider(
-            interactive=False,
-            label="Generated Image",
-        )
-    
-    with gr.Row():
-        ratio = gr.Radio(
-            label="Expected ratio", 
-            choices=["9:16", "16:9"],
-            value = "9:16"
-        )
-        model_selection = gr.Dropdown(
-            choices=list(MODELS.keys()),
-            value="RealVisXL V5.0 Lightning",
-            label="Model",
-        )
+    with gr.Column():
+        
+        gr.HTML(title)
+
+        with gr.Row():
+            
+            with gr.Column():
+                
+                input_image = gr.Image(
+                    type="pil",
+                    label="Input Image",
+                    sources=["upload"],
+                )
+     
+                with gr.Row():
+                    ratio = gr.Radio(
+                        label="Expected ratio", 
+                        choices=["9:16", "16:9"],
+                        value = "9:16"
+                    )
+                    model_selection = gr.Dropdown(
+                        choices=list(MODELS.keys()),
+                        value="RealVisXL V5.0 Lightning",
+                        label="Model",
+                    )
+    
+                run_button = gr.Button("Generate")
+            
+            with gr.Column():
+                result = ImageSlider(
+                    interactive=False,
+                    label="Generated Image",
+                )
 
     run_button.click(
         fn=clear_result,
         inputs=None,
         outputs=result,
     ).then(
-        fn=fill_image,
+        fn=infer,
         inputs=[input_image, model_selection, ratio],
         outputs=result,
     )