Update app.py

app.py

@@ -41,12 +41,27 @@ def generate_i2i(input_image, prompt, strength, num_inference_steps, seed, rando
     if randomize_seed:
         seed = torch.randint(0, 2**32 - 1, (1,)).item()
     generator = torch.Generator("cuda").manual_seed(int(seed))
+    
+    # Preserve original aspect ratio
+    orig_w, orig_h = input_image.size
+    # Round to nearest multiple of 64
+    new_w = round(orig_w / 64) * 64
+    new_h = round(orig_h / 64) * 64
+    # Cap at 1024 on longest side
+    scale = min(1024 / new_w, 1024 / new_h)
+    if scale < 1:
+        new_w = round(new_w * scale / 64) * 64
+        new_h = round(new_h * scale / 64) * 64
+    input_image = input_image.resize((new_w, new_h))
+    
     image = pipe_i2i(
         prompt=prompt,
         image=input_image,
         strength=float(strength),
         num_inference_steps=int(num_inference_steps),
         generator=generator,
+        width=new_w,
+        height=new_h,
     ).images[0]
     return image, seed
 
@@ -127,7 +142,7 @@ with gr.Blocks(fill_height=True) as demo:
                         lines=4,
                     )
                     with gr.Accordion("⚙️ Advanced Settings", open=False):
-                        i2i_strength = gr.Slider(0.1, 1.0, value=0.75, step=0.05, label="Strength", info="Higher = more change")
+                        i2i_strength = gr.Slider(0.1, 1.0, value=0.85, step=0.05, label="Strength", info="Higher = more change")
                         i2i_steps = gr.Slider(1, 8, value=4, step=1, label="Inference Steps")
                         with gr.Row():
                             i2i_randomize = gr.Checkbox(label="🎲 Random Seed", value=True)