cancel einops

examples/train_unconditional.py

@@ -173,7 +173,7 @@ if __name__ == "__main__":
     parser.add_argument("--lr", type=float, default=1e-4)
     parser.add_argument("--warmup_steps", type=int, default=500)
     parser.add_argument("--ema_inv_gamma", type=float, default=1.0)
-    parser.add_argument("--ema_power", type=float, default=3/4)
+    parser.add_argument("--ema_power", type=float, default=3 / 4)
     parser.add_argument("--ema_max_decay", type=float, default=0.999)
     parser.add_argument("--push_to_hub", action="store_true")
     parser.add_argument("--hub_token", type=str, default=None)

src/diffusers/models/unet_ldm.py

@@ -13,13 +13,6 @@ from .embeddings import get_timestep_embedding
 from .resnet import Upsample
 
 
-# try:
-#    from einops import rearrange, repeat
-# except:
-#    print("Einops is not installed")
-#    pass
-
-
 def exists(val):
     return val is not None
 

src/diffusers/models/unet_rl.py

@@ -17,7 +17,6 @@ from ..modeling_utils import ModelMixin
 #    pass
 
 
-
 class SinusoidalPosEmb(nn.Module):
     def __init__(self, dim):
         super().__init__()

src/diffusers/schedulers/scheduling_utils.py

@@ -11,11 +11,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from typing import Union
+
 import numpy as np
 import torch
 
-from typing import Union
-
 
 SCHEDULER_CONFIG_NAME = "scheduler_config.json"
 
@@ -53,20 +53,16 @@ class SchedulerMixin:
 
         raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
 
-    def match_shape(
-            self,
-            values: Union[np.ndarray, torch.Tensor],
-            broadcast_array: Union[np.ndarray, torch.Tensor]
-        ):
+    def match_shape(self, values: Union[np.ndarray, torch.Tensor], broadcast_array: Union[np.ndarray, torch.Tensor]):
         """
-            Turns a 1-D array into an array or tensor with len(broadcast_array.shape) dims.
-
-            Args:
-                timesteps: an array or tensor of values to extract.
-                broadcast_array: an array with a larger shape of K dimensions with the batch
-                    dimension equal to the length of timesteps.
-            Returns:
-                a tensor of shape [batch_size, 1, ...] where the shape has K dims.
+        Turns a 1-D array into an array or tensor with len(broadcast_array.shape) dims.
+
+        Args:
+            timesteps: an array or tensor of values to extract.
+            broadcast_array: an array with a larger shape of K dimensions with the batch
+                dimension equal to the length of timesteps.
+        Returns:
+            a tensor of shape [batch_size, 1, ...] where the shape has K dims.
         """
 
         tensor_format = getattr(self, "tensor_format", "pt")

tests/test_modeling_utils.py

@@ -21,7 +21,8 @@ import unittest
 import numpy as np
 import torch
 
-from diffusers import (  # GradTTSPipeline,
+from diffusers import (
+    GradTTSPipeline,
     BDDMPipeline,
     DDIMPipeline,
     DDIMScheduler,