Removed all GPU-requiring operations from the app.py

app.py

@@ -13,7 +13,6 @@ import torch
 
 model = DiffusionModule("./diffusion_configs.yaml")
 model.load_ckpt("./data/model.ckpt")
-model.cuda().half()
 model.eval();
 
 # Loading a baseline noise for making predictions
@@ -21,10 +20,8 @@ model.eval();
 seed = 3407
 np.random.seed(seed)
 torch.random.manual_seed(seed)
-torch.cuda.manual_seed(seed)
-torch.cuda.manual_seed_all(seed)
 torch.backends.cudnn.deterministic = True
-BASELINE_NOISE = torch.randn(1, 1, 256, 256).cuda().half()
+BASELINE_NOISE = torch.randn(1, 1, 256, 256).half()
 
 # Model helper functions
 
@@ -88,13 +85,13 @@ def make_predictions(img_path, angles=None, cls_batch=None, rotate_to_standard=F
             cls_value = torch.tensor([2, *angles, *fp])
         else:
             cls_value = torch.tensor([1, *angles, *fp])
-        cls_batch = cls_value.unsqueeze(0).repeat(input_batch["img"].shape[0], 1).cuda().half()
+        cls_batch = cls_value.unsqueeze(0).repeat(input_batch["img"].shape[0], 1)
 
     # Generate noise
     noise = BASELINE_NOISE.repeat(input_batch["img"].shape[0], 1, 1, 1)
     model_kwargs = {
         "cls": cls_batch,
-        "concat": input_batch["img"].cuda().half(),
+        "concat": input_batch["img"]
     }
     
     # Make predictions