Update app.py

app.py

@@ -189,7 +189,7 @@ def esrgan_upscale(image, model, device='cuda', upscale_factor=4):
     if low_res_w < 1 or low_res_h < 1:
         raise ValueError("Upscale factor too small for image size")
     
-    low_res_image = image.resize((low_res_w, low_res_h), Image.LANCZOS)
+    low_res_image = image.resize((low_res_w, low_res_h), Image.BICUBIC)  # Changed to BICUBIC for better match to training degradation
     
     # Prepare image
     img_np = np.array(low_res_image).astype(np.float32) / 255.
@@ -210,7 +210,7 @@ def esrgan_upscale(image, model, device='cuda', upscale_factor=4):
     target_w = int(orig_w * upscale_factor)
     target_h = int(orig_h * upscale_factor)
     if upscaled.size != (target_w, target_h):
-        upscaled = upscaled.resize((target_w, target_h), Image.LANCZOS)
+        upscaled = upscaled.resize((target_w, target_h), Image.BICUBIC)  # Changed to BICUBIC
     
     return upscaled
 
@@ -346,7 +346,7 @@ with gr.Blocks(css=css) as demo:
                 maximum=4,
                 step=1,
                 value=4,
-                info="Choose upscale factor (2x, 3x, 4x)"
+                info="Choose upscale factor (2x, 3x, 4x). Use 4x for best results; lower may cause color artifacts."
             )
             
             num_inference_steps = gr.Slider(