Update app.py

app.py

@@ -67,12 +67,7 @@ latents = latents * scheduler.init_noise_sigma # Scaling (previous versions did
 with autocast("cuda"):  # will fallback to CPU if no CUDA; no autocast for MPS
     for i, t in tqdm(enumerate(scheduler.timesteps), total=len(scheduler.timesteps)):
 
-"""It's working, but that's quite a bit of code! Let's look at the components one by one.
 
-## The Autoencoder (AE)
-
-The AE can 'encode' an image into some sort of latent representation, and decode this back into an image. I've wrapped the code for this into a couple of functions here so we can see what this looks like in action:
-"""
 
 def pil_to_latent(input_im):
     # Single image -> single latent in a batch (so size 1, 4, 64, 64)