bugfix for npu not support float64

examples/community/fresco_v2v.py

@@ -404,10 +404,11 @@ def 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)

examples/community/matryoshka.py

@@ -2806,10 +2806,11 @@ def get_time_embed(
             # 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)

examples/research_projects/pixart/pipeline_pixart_alpha_controlnet.py

@@ -1031,10 +1031,11 @@ def __call__(
                     # 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 = latent_model_input.device.type == "mps"
+                    is_npu = latent_model_input.device.type == "npu"
                     if isinstance(current_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
                     current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
                 elif len(current_timestep.shape) == 0:
                     current_timestep = current_timestep[None].to(latent_model_input.device)

examples/research_projects/promptdiffusion/promptdiffusioncontrolnet.py

@@ -258,10 +258,11 @@ def 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)

src/diffusers/models/controlnets/controlnet.py

@@ -740,10 +740,11 @@ def 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)

src/diffusers/models/controlnets/controlnet_sparsectrl.py

@@ -671,10 +671,11 @@ def 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)

src/diffusers/models/controlnets/controlnet_union.py

@@ -681,10 +681,11 @@ def 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)

src/diffusers/models/controlnets/controlnet_xs.py

@@ -1088,10 +1088,11 @@ def 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)

src/diffusers/models/unets/unet_2d_condition.py

@@ -915,10 +915,11 @@ def get_time_embed(
             # 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)

src/diffusers/models/unets/unet_3d_condition.py

@@ -624,10 +624,11 @@ def 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)

src/diffusers/models/unets/unet_i2vgen_xl.py

@@ -575,10 +575,11 @@ def 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(timesteps, 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)

src/diffusers/models/unets/unet_motion_model.py

@@ -2114,10 +2114,11 @@ def 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)

src/diffusers/models/unets/unet_spatio_temporal_condition.py

@@ -402,10 +402,11 @@ def 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)

src/diffusers/pipelines/audioldm2/modeling_audioldm2.py

@@ -768,10 +768,11 @@ def 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)

src/diffusers/pipelines/deprecated/versatile_diffusion/modeling_text_unet.py

@@ -1163,10 +1163,11 @@ def 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)

src/diffusers/pipelines/dit/pipeline_dit.py

@@ -187,10 +187,11 @@ def __call__(
                 # 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 = latent_model_input.device.type == "mps"
+                is_npu = latent_model_input.device.type == "npu"
                 if isinstance(timesteps, 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=latent_model_input.device)
             elif len(timesteps.shape) == 0:
                 timesteps = timesteps[None].to(latent_model_input.device)

src/diffusers/pipelines/latte/pipeline_latte.py

@@ -797,10 +797,11 @@ def __call__(
                     # 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 = latent_model_input.device.type == "mps"
+                    is_npu = latent_model_input.device.type == "npu"
                     if isinstance(current_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
                     current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
                 elif len(current_timestep.shape) == 0:
                     current_timestep = current_timestep[None].to(latent_model_input.device)

src/diffusers/pipelines/lumina/pipeline_lumina.py

@@ -806,10 +806,11 @@ def __call__(
                     # 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 = latent_model_input.device.type == "mps"
+                    is_npu = latent_model_input.device.type == "npu"
                     if isinstance(current_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
                     current_timestep = torch.tensor(
                         [current_timestep],
                         dtype=dtype,

src/diffusers/pipelines/pag/pipeline_pag_pixart_sigma.py

@@ -807,10 +807,11 @@ def __call__(
                     # 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 = latent_model_input.device.type == "mps"
+                    is_npu = latent_model_input.device.type == "npu"
                     if isinstance(current_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
                     current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
                 elif len(current_timestep.shape) == 0:
                     current_timestep = current_timestep[None].to(latent_model_input.device)

src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py

@@ -907,10 +907,11 @@ def __call__(
                     # 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 = latent_model_input.device.type == "mps"
+                    is_npu = latent_model_input.device.type == "npu"
                     if isinstance(current_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
                     current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
                 elif len(current_timestep.shape) == 0:
                     current_timestep = current_timestep[None].to(latent_model_input.device)

src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py

@@ -822,10 +822,11 @@ def __call__(
                     # 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 = latent_model_input.device.type == "mps"
+                    is_npu = latent_model_input.device.type == "npu"
                     if isinstance(current_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
                     current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
                 elif len(current_timestep.shape) == 0:
                     current_timestep = current_timestep[None].to(latent_model_input.device)