Fix typos

src/diffusers/models/attention.py

@@ -69,7 +69,7 @@ def __init__(
         self.value = nn.Linear(channels, channels)
 
         self.rescale_output_factor = rescale_output_factor
-        self.proj_attn = nn.Linear(channels, channels, 1)
+        self.proj_attn = nn.Linear(channels, channels, bias=True)
 
         self._use_memory_efficient_attention_xformers = False
         self._attention_op = None

src/diffusers/models/controlnet.py

@@ -344,7 +344,7 @@ def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, Atte
             `processor (`dict` of `AttentionProcessor` or `AttentionProcessor`):
                 The instantiated processor class or a dictionary of processor classes that will be set as the processor
                 of **all** `Attention` layers.
-            In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainablae attention processors.:
+            In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.:
 
         """
         count = len(self.attn_processors.keys())
@@ -379,34 +379,34 @@ def set_attention_slice(self, slice_size):
         Args:
             slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
                 When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
-                `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
+                `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
                 provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
                 must be a multiple of `slice_size`.
         """
         sliceable_head_dims = []
 
-        def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
+        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
             if hasattr(module, "set_attention_slice"):
                 sliceable_head_dims.append(module.sliceable_head_dim)
 
             for child in module.children():
-                fn_recursive_retrieve_slicable_dims(child)
+                fn_recursive_retrieve_sliceable_dims(child)
 
         # retrieve number of attention layers
         for module in self.children():
-            fn_recursive_retrieve_slicable_dims(module)
+            fn_recursive_retrieve_sliceable_dims(module)
 
-        num_slicable_layers = len(sliceable_head_dims)
+        num_sliceable_layers = len(sliceable_head_dims)
 
         if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             slice_size = [dim // 2 for dim in sliceable_head_dims]
         elif slice_size == "max":
             # make smallest slice possible
-            slice_size = num_slicable_layers * [1]
+            slice_size = num_sliceable_layers * [1]
 
-        slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
 
         if len(slice_size) != len(sliceable_head_dims):
             raise ValueError(

src/diffusers/models/modeling_utils.py

@@ -575,7 +575,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                         raise ValueError(
                             f"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are"
                             f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass"
-                            " `low_cpu_mem_usage=False` and `device_map=None` if you want to randomely initialize"
+                            " `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize"
                             " those weights or else make sure your checkpoint file is correct."
                         )
 
@@ -591,7 +591,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                             set_module_tensor_to_device(model, param_name, param_device, value=param)
                 else:  # else let accelerate handle loading and dispatching.
                     # Load weights and dispatch according to the device_map
-                    # by deafult the device_map is None and the weights are loaded on the CPU
+                    # by default the device_map is None and the weights are loaded on the CPU
                     accelerate.load_checkpoint_and_dispatch(model, model_file, device_map, dtype=torch_dtype)
 
                 loading_info = {

src/diffusers/models/resnet.py

@@ -418,7 +418,7 @@ class ResnetBlock2D(nn.Module):
         time_embedding_norm (`str`, *optional*, default to `"default"` ): Time scale shift config.
             By default, apply timestep embedding conditioning with a simple shift mechanism. Choose "scale_shift" or
             "ada_group" for a stronger conditioning with scale and shift.
-        kernal (`torch.FloatTensor`, optional, default to None): FIR filter, see
+        kernel (`torch.FloatTensor`, optional, default to None): FIR filter, see
             [`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].
         output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.
         use_in_shortcut (`bool`, *optional*, default to `True`):

src/diffusers/models/transformer_2d.py

@@ -105,7 +105,7 @@ def __init__(
         self.attention_head_dim = attention_head_dim
         inner_dim = num_attention_heads * attention_head_dim
 
-        # 1. Transformer2DModel can process both standard continous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
+        # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
         # Define whether input is continuous or discrete depending on configuration
         self.is_input_continuous = (in_channels is not None) and (patch_size is None)
         self.is_input_vectorized = num_vector_embeds is not None
@@ -198,7 +198,7 @@ def __init__(
         # 4. Define output layers
         self.out_channels = in_channels if out_channels is None else out_channels
         if self.is_input_continuous:
-            # TODO: should use out_channels for continous projections
+            # TODO: should use out_channels for continuous projections
             if use_linear_projection:
                 self.proj_out = nn.Linear(inner_dim, in_channels)
             else:
@@ -223,7 +223,7 @@ def forward(
         """
         Args:
             hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`.
-                When continous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input
+                When continuous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input
                 hidden_states
             encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
                 Conditional embeddings for cross attention layer. If not given, cross-attention defaults to

src/diffusers/models/unet_1d.py

@@ -59,7 +59,7 @@ class UNet1DModel(ModelMixin, ConfigMixin):
             obj:`(32, 32, 64)`): Tuple of block output channels.
         mid_block_type (`str`, *optional*, defaults to "UNetMidBlock1D"): block type for middle of UNet.
         out_block_type (`str`, *optional*, defaults to `None`): optional output processing of UNet.
-        act_fn (`str`, *optional*, defaults to None): optional activitation function in UNet blocks.
+        act_fn (`str`, *optional*, defaults to None): optional activation function in UNet blocks.
         norm_num_groups (`int`, *optional*, defaults to 8): group norm member count in UNet blocks.
         layers_per_block (`int`, *optional*, defaults to 1): added number of layers in a UNet block.
         downsample_each_block (`int`, *optional*, defaults to False:

src/diffusers/models/unet_1d_blocks.py

@@ -331,7 +331,7 @@ def __init__(self, in_channels, n_head=1, dropout_rate=0.0):
         self.key = nn.Linear(self.channels, self.channels)
         self.value = nn.Linear(self.channels, self.channels)
 
-        self.proj_attn = nn.Linear(self.channels, self.channels, 1)
+        self.proj_attn = nn.Linear(self.channels, self.channels, bias=True)
 
         self.dropout = nn.Dropout(dropout_rate, inplace=True)
 

src/diffusers/models/unet_2d_blocks.py

@@ -2684,7 +2684,7 @@ def __init__(
                 dropout=dropout,
                 bias=attention_bias,
                 cross_attention_dim=None,
-                cross_attention_norm=None,
+                cross_attention_norm=False,
             )
 
         # 2. Cross-Attn

src/diffusers/models/unet_2d_condition.py

@@ -197,7 +197,7 @@ def __init__(
             timestep_input_dim = block_out_channels[0]
         else:
             raise ValueError(
-                f"{time_embedding_type} does not exist. Pleaes make sure to use one of `fourier` or `positional`."
+                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
             )
 
         self.time_embedding = TimestepEmbedding(
@@ -391,7 +391,7 @@ def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, Atte
             `processor (`dict` of `AttentionProcessor` or `AttentionProcessor`):
                 The instantiated processor class or a dictionary of processor classes that will be set as the processor
                 of **all** `Attention` layers.
-            In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainablae attention processors.:
+            In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.:
 
         """
         count = len(self.attn_processors.keys())
@@ -425,34 +425,34 @@ def set_attention_slice(self, slice_size):
         Args:
             slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
                 When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
-                `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
+                `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
                 provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
                 must be a multiple of `slice_size`.
         """
         sliceable_head_dims = []
 
-        def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
+        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
             if hasattr(module, "set_attention_slice"):
                 sliceable_head_dims.append(module.sliceable_head_dim)
 
             for child in module.children():
-                fn_recursive_retrieve_slicable_dims(child)
+                fn_recursive_retrieve_sliceable_dims(child)
 
         # retrieve number of attention layers
         for module in self.children():
-            fn_recursive_retrieve_slicable_dims(module)
+            fn_recursive_retrieve_sliceable_dims(module)
 
-        num_slicable_layers = len(sliceable_head_dims)
+        num_sliceable_layers = len(sliceable_head_dims)
 
         if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             slice_size = [dim // 2 for dim in sliceable_head_dims]
         elif slice_size == "max":
             # make smallest slice possible
-            slice_size = num_slicable_layers * [1]
+            slice_size = num_sliceable_layers * [1]
 
-        slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
 
         if len(slice_size) != len(sliceable_head_dims):
             raise ValueError(
@@ -515,7 +515,7 @@ def forward(
             returning a tuple, the first element is the sample tensor.
         """
         # By default samples have to be AT least a multiple of the overall upsampling factor.
-        # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
         # However, the upsampling interpolation output size can be forced to fit any upsampling size
         # on the fly if necessary.
         default_overall_up_factor = 2**self.num_upsamplers

src/diffusers/pipelines/pipeline_utils.py

@@ -1351,7 +1351,7 @@ def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto
         Args:
             slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
                 When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
-                `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
+                `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
                 provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
                 must be a multiple of `slice_size`.
         """

src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py

@@ -287,7 +287,7 @@ def __init__(
             timestep_input_dim = block_out_channels[0]
         else:
             raise ValueError(
-                f"{time_embedding_type} does not exist. Pleaes make sure to use one of `fourier` or `positional`."
+                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
             )
 
         self.time_embedding = TimestepEmbedding(
@@ -481,7 +481,7 @@ def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, Atte
             `processor (`dict` of `AttentionProcessor` or `AttentionProcessor`):
                 The instantiated processor class or a dictionary of processor classes that will be set as the processor
                 of **all** `Attention` layers.
-            In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainablae attention processors.:
+            In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.:
 
         """
         count = len(self.attn_processors.keys())
@@ -515,34 +515,34 @@ def set_attention_slice(self, slice_size):
         Args:
             slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
                 When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
-                `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
+                `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
                 provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
                 must be a multiple of `slice_size`.
         """
         sliceable_head_dims = []
 
-        def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
+        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
             if hasattr(module, "set_attention_slice"):
                 sliceable_head_dims.append(module.sliceable_head_dim)
 
             for child in module.children():
-                fn_recursive_retrieve_slicable_dims(child)
+                fn_recursive_retrieve_sliceable_dims(child)
 
         # retrieve number of attention layers
         for module in self.children():
-            fn_recursive_retrieve_slicable_dims(module)
+            fn_recursive_retrieve_sliceable_dims(module)
 
-        num_slicable_layers = len(sliceable_head_dims)
+        num_sliceable_layers = len(sliceable_head_dims)
 
         if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             slice_size = [dim // 2 for dim in sliceable_head_dims]
         elif slice_size == "max":
             # make smallest slice possible
-            slice_size = num_slicable_layers * [1]
+            slice_size = num_sliceable_layers * [1]
 
-        slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
 
         if len(slice_size) != len(sliceable_head_dims):
             raise ValueError(
@@ -605,7 +605,7 @@ def forward(
             returning a tuple, the first element is the sample tensor.
         """
         # By default samples have to be AT least a multiple of the overall upsampling factor.
-        # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
         # However, the upsampling interpolation output size can be forced to fit any upsampling size
         # on the fly if necessary.
         default_overall_up_factor = 2**self.num_upsamplers

tests/models/test_models_unet_2d_condition.py

@@ -223,23 +223,23 @@ def test_model_attention_slicing(self):
             output = model(**inputs_dict)
         assert output is not None
 
-    def test_model_slicable_head_dim(self):
+    def test_model_sliceable_head_dim(self):
         init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
 
         init_dict["attention_head_dim"] = (8, 16)
 
         model = self.model_class(**init_dict)
 
-        def check_slicable_dim_attr(module: torch.nn.Module):
+        def check_sliceable_dim_attr(module: torch.nn.Module):
             if hasattr(module, "set_attention_slice"):
                 assert isinstance(module.sliceable_head_dim, int)
 
             for child in module.children():
-                check_slicable_dim_attr(child)
+                check_sliceable_dim_attr(child)
 
         # retrieve number of attention layers
         for module in model.children():
-            check_slicable_dim_attr(module)
+            check_sliceable_dim_attr(module)
 
     def test_special_attn_proc(self):
         class AttnEasyProc(torch.nn.Module):
@@ -658,7 +658,7 @@ def test_set_attention_slice_list(self):
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()
 
-        # there are 32 slicable layers
+        # there are 32 sliceable layers
         slice_list = 16 * [2, 3]
         unet = self.get_unet_model()
         unet.set_attention_slice(slice_list)