full codespell coverage

Signed-off-by: Vladimir Mandic <mandic00@live.com>

scripts/lbm/base/base_model.py

@@ -16,7 +16,7 @@ class BaseModel(nn.Module):
         """Called when the training starts
 
         Args:
-            device (Optional[torch.device], optional): The device to use. Usefull to set
+            device (Optional[torch.device], optional): The device to use. Useful to set
                 relevant parameters on the model and embedder to the right device only
                 once at the start of the training. Defaults to None.
         """

scripts/lbm/config.py

@@ -21,10 +21,10 @@ class BaseConfig:
 
     @classmethod
     def from_dict(cls, config_dict: Dict[str, Any]) -> "BaseConfig":
-        """Creates a BaseConfig instance from a dictionnary
+        """Creates a BaseConfig instance from a dictionary
 
         Args:
-            config_dict (dict): The Python dictionnary containing all the parameters
+            config_dict (dict): The Python dictionary containing all the parameters
 
         Returns:
             :class:`BaseConfig`: The created instance
@@ -78,10 +78,10 @@ class BaseConfig:
         return cls.from_dict(config_dict)
 
     def to_dict(self) -> dict:
-        """Transforms object into a Python dictionnary
+        """Transforms object into a Python dictionary
 
         Returns:
-            (dict): The dictionnary containing all the parameters"""
+            (dict): The dictionary containing all the parameters"""
         return asdict(self)
 
     def to_json_string(self):

scripts/lbm/tiler.py

@@ -84,7 +84,7 @@ class Tiler:
     def merge_tiles(
         self, tiles: List[List[torch.Tensor]], tiling_method: str = "gaussian"
     ) -> torch.Tensor:
-        """Merge tiles by averaging the overlaping regions
+        """Merge tiles by averaging the overlapping regions
         Args:
             tiles (Dict[str, Tile]): dictionary of processed tiles
             tiling_method (str): tiling method. Can be "average", "gaussian" or "linear"
@@ -103,7 +103,7 @@ class Tiler:
             )
 
     def _average_merge_tiles(self, tiles: List[List[torch.Tensor]]) -> torch.Tensor:
-        """Merge tiles by averaging the overlaping regions
+        """Merge tiles by averaging the overlapping regions
         Args:
             tiles (Dict[str, Tile]): dictionary of processed tiles
         Returns:
@@ -149,7 +149,7 @@ class Tiler:
                 ] += 1
 
         # outputs is summed up with this multiplicity
-        # so we need to divide by the weights wich is either 1, 2 or 4 depending on the region
+        # so we need to divide by the weights which is either 1, 2 or 4 depending on the region
         output = output / weights
         return output
 
@@ -204,7 +204,7 @@ class Tiler:
         )
 
     def _gaussian_merge_tiles(self, tiles: List[List[torch.Tensor]]) -> torch.Tensor:
-        """Merge tiles by averaging the overlaping regions
+        """Merge tiles by averaging the overlapping regions
         Args:
             List[List[torch.Tensor]]: List of processed tiles
         Returns:
@@ -278,7 +278,7 @@ class Tiler:
         return b
 
     def _linear_merge_tiles(self, tiles: List[List[torch.Tensor]]) -> torch.Tensor:
-        """Merge tiles by blending the overlaping regions
+        """Merge tiles by blending the overlapping regions
         Args:
             tiles (List[List[torch.Tensor]]): List of processed tiles
         Returns:

scripts/lbm/unets/unet.py

@@ -111,7 +111,7 @@ class DiffusersUNet2DCondWrapper(UNet2DConditionModel):
             down_intrablock_additional_residuals_clone = None
 
         # Check diffusers.models.embeddings.py > MultiIPAdapterImageProjectionLayer > forward() for implementation
-        # Exepected format : List[torch.Tensor] of shape (batch_size, num_image_embeds, embed_dim)
+        # Expected format : List[torch.Tensor] of shape (batch_size, num_image_embeds, embed_dim)
         # with length = number of ip_adapters loaded in the ip_adapter_wrapper
         if ip_adapter_cond_embedding is not None:
             added_cond_kwargs = {