device fix

utils.py

@@ -21,7 +21,7 @@ def pil_to_latent(input_im):
         latent = vae.encode(tfms.ToTensor()(input_im).unsqueeze(0).to(torch_device)*2-1) # Note scaling
     return 0.18215 * latent.latent_dist.sample()
 
-def latents_to_pil(latents, torch_device="mps:0"):
+def latents_to_pil(latents, torch_device=torch_device):
     # bath of latents -> list of images
     latents = (1 / 0.18215) * latents
     with torch.no_grad():