Remove device initialization and logging from load_transformer function; set device parameter to None for flexibility

app.py

@@ -310,8 +310,6 @@ def load_visual_tokenizer(args):
     return vae
 
 def load_transformer(vae, args):
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    print(f"Device: {device}")
     model_path = args.model_path
     if args.checkpoint_type == 'torch': 
         # copy large model to local; save slim to local; and copy slim to nas; load local slim model
@@ -368,7 +366,7 @@ def load_transformer(vae, args):
         model_path=slim_model_path, 
         scale_schedule=None, 
         vae=vae, 
-        device=device, 
+        device=None,
         model_kwargs=kwargs_model,
         text_channels=args.text_channels,
         apply_spatial_patchify=args.apply_spatial_patchify,