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main/fresco_v2v.py

@@ -404,10 +404,11 @@ def my_forward(
             # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
             # This would be a good case for the `match` statement (Python 3.10+)
             is_mps = sample.device.type == "mps"
+            is_npu = sample.device.type == "npu"
             if isinstance(timestep, float):
-                dtype = torch.float32 if is_mps else torch.float64
+                dtype = torch.float32 if (is_mps or is_npu) else torch.float64
             else:
-                dtype = torch.int32 if is_mps else torch.int64
+                dtype = torch.int32 if (is_mps or is_npu) else torch.int64
             timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
         elif len(timesteps.shape) == 0:
             timesteps = timesteps[None].to(sample.device)

main/matryoshka.py

@@ -2806,10 +2806,11 @@ class MatryoshkaUNet2DConditionModel(
             # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
             # This would be a good case for the `match` statement (Python 3.10+)
             is_mps = sample.device.type == "mps"
+            is_npu = sample.device.type == "npu"
             if isinstance(timestep, float):
-                dtype = torch.float32 if is_mps else torch.float64
+                dtype = torch.float32 if (is_mps or is_npu) else torch.float64
             else:
-                dtype = torch.int32 if is_mps else torch.int64
+                dtype = torch.int32 if (is_mps or is_npu) else torch.int64
             timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
         elif len(timesteps.shape) == 0:
             timesteps = timesteps[None].to(sample.device)