The final step is to use the `vae` to decode the latent representation into an image and get the decoded output with `sample`:  
```py
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
with torch.no_grad():
image = vae.decode(latents).sample
```  
Lastly, convert the image to a `PIL.Image` to see your generated image!  
```py
>>> image = (image / 2 + 0.5).clamp(0, 1).squeeze()
>>> image = (image.permute(1, 2, 0) * 255).to(torch.uint8).cpu().numpy()