Update app.py

app.py

@@ -16,6 +16,7 @@ image_pipe = DDPMPipeline.from_pretrained(pipeline_name).to(device)
 scheduler = DDIMScheduler.from_pretrained(pipeline_name)
 scheduler.set_timesteps(num_inference_steps=20)
 
+# The guidance function 
 def color_loss(images, target_color=(0.1, 0.9, 0.5)):
     """Given a target color (R, G, B) return a loss for how far away on average 
     the images' pixels are from that color. Defaults to a light teal: (0.1, 0.9, 0.5) """
@@ -24,7 +25,7 @@ def color_loss(images, target_color=(0.1, 0.9, 0.5)):
     error = torch.abs(images - target).mean() # Mean absolute difference between the image pixels and the target color
     return error
 
-
+# And the core function to generate an image given the relevant inputs
 def generate(color, guidance_loss_scale):
   target_color = ImageColor.getcolor(color, "RGB") # Target color as RGB
   target_color = [a/255 for a in target_color] # Rescale from (0, 255) to (0, 1)
@@ -45,20 +46,23 @@ def generate(color, guidance_loss_scale):
   im.save('test.jpeg')
   return im
 
+# See the gradio docs for the types of inputs and outputs available
 inputs = [
     gr.ColorPicker(label="color", value='55FFAA'), # Add any inputs you need here
     gr.Slider(label="guidance_scale", minimum=0, maximum=30, value=3)
 ]
 outputs = gr.Image(label="result")
 
+# Setting up a minimal interface to our function:
 demo = gr.Interface(
     fn=generate,
     inputs=inputs,
     outputs=outputs,
     examples=[
-        ["#BB2266"],["#44CCAA"] # You can provide some example inputs to get people started
+        ["#BB2266", 3],["#44CCAA", 5] # You can provide some example inputs to get people started
     ],
 )
 
+# And launching
 if __name__ == "__main__":
     demo.launch(enable_queue=True)