Update app.py

app.py

@@ -16,31 +16,25 @@ sd_2_1_4x = StableDiffusionUpscalePipeline.from_pretrained(model_4x, torch_dtype
 # Define the function that will be called when the interface is used
 
 def upscale_image(model, input_image):
-    # Convert the image to a PyTorch tensor
+    # Convert the image 
     generator = torch.manual_seed(999999)
     input_image = Image.open(input_image).convert("RGB")
-    
-    
     # Upscale the image using the selected model
     if model == "SD 2.0 2x Latent Upscaler":
         upscaled_image = sd_2_0_2x(prompt="", image=input_image, num_inference_steps=5).images[0]
     else:
         low_res_img = input_image.resize((128, 128))
         upscaled_image = sd_2_1_4x(prompt="", image=low_res_img, num_inference_steps=5).images[0]
-    
-    # Convert the upscaled tensor back to a PIL image
-     
-    
     # Return the upscaled image
     return upscaled_image
 
 # Define the Gradio interface
 iface = gr.Interface(
     fn=upscale_image,
-    inputs=[gr.Radio(["SD 2.0 2x Latent Upscaler", "SD 2.1 4x Upscaler"]), gr.Image(type="filepath")],
+    inputs=[gr.Radio(["SD 2.0 2x Latent Upscaler", "SD 2.1 4x Upscaler"], label="Models:"), gr.Image(type="filepath")],
     outputs=gr.Image(type="filepath"),
-    title="Image Upscaler",
-    description="Upscale an image using either the SD 2.0 2x Latent Upscaler or the SD 2.1 4x Upscaler."
+    title="SD Image Upscaler",
+    description="Upscale an image using either the SD 2.0 2x Latent Upscaler or the SD 2.1 4x Upscaler. Use the 4x Upscaler for images lower than 512x512. Use the 2x Upscaler for images 512x512 to 768x768"
 )
 
 # Launch the interface