Update app.py

app.py

@@ -142,31 +142,6 @@ def latents_to_pil(latents):
     pil_images = [Image.fromarray(image) for image in images]
     return pil_images
 
-"""We'll use a pic from the web here, but you can load your own instead by uploading it and editing the filename in the next cell."""
-
-
-# Setting the number of sampling steps:
-set_timesteps(scheduler, 15)
-
-"""You can see how our new set of steps corresponds to those used in training:"""
-
-# See these in terms of the original 1000 steps used for training:
-print(scheduler.timesteps)
-
-"""And how much noise is present at each:"""
-
-# Look at the equivalent noise levels:
-print(scheduler.sigmas)
-
-# TODO maybe show timestep as well
-
-"""This 'sigma' is the amount of noise added to the latent representation. Let's visualize what this looks like by adding a bit of noise to our encoded image and then decoding this noised version:"""
-
-noise = torch.randn_like(encoded) # Random noise
-sampling_step = 10 # Equivalent to step 10 out of 15 in the schedule above
-# encoded_and_noised = scheduler.add_noise(encoded, noise, timestep) # Diffusers 0.3 and below
-encoded_and_noised = scheduler.add_noise(encoded, noise, timesteps=torch.tensor([scheduler.timesteps[sampling_step]]))
-latents_to_pil(encoded_and_noised.float())[0] # Display
 
 """What does this look like at different timesteps? Experiment and see for yourself!