full codespell coverage

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

scripts/consistory/consistory_pipeline.py

@@ -214,7 +214,7 @@ class ConsistoryExtendAttnSDXLPipeline(
             callback_on_step_end_tensor_inputs (`List`, *optional*):
                 The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                 will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
-                `._callback_tensor_inputs` attribute of your pipeine class.
+                `._callback_tensor_inputs` attribute of your pipeline class.
 
         Examples:
 

scripts/consistory/consistory_unet_sdxl.py

@@ -857,7 +857,7 @@ class ConsistorySDXLUNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditio
             cross_attention_kwargs (`dict`, *optional*):
                 A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
             added_cond_kwargs: (`dict`, *optional*):
-                A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
+                A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
                 are passed along to the UNet blocks.
             down_block_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
                 additional residuals to be added to UNet long skip connections from down blocks to up blocks for

scripts/demofusion.py

@@ -1157,7 +1157,7 @@ class DemoFusionSDXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderM
         output = ImagePipelineOutput(images=output_images)
         return output
 
-    # Overrride to properly handle the loading and unloading of the additional text encoder.
+    # Override to properly handle the loading and unloading of the additional text encoder.
     def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs): # pylint: disable=arguments-differ
         # We could have accessed the unet config from `lora_state_dict()` too. We pass
         # it here explicitly to be able to tell that it's coming from an SDXL

scripts/differential_diffusion.py

@@ -940,7 +940,7 @@ class StableDiffusionXLDiffImg2ImgPipeline(DiffusionPipeline, FromSingleFileMixi
             num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
             timesteps = timesteps[:num_inference_steps]
 
-        # prepartions for diff diff
+        # preparations for diff diff
         original_with_noise = self.prepare_latents(
             original_image, timesteps, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator
         )
@@ -1764,7 +1764,7 @@ class StableDiffusionDiffImg2ImgPipeline(DiffusionPipeline):
         # 8. Denoising loop
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
 
-        # prepartions
+        # preparations
         original_with_noise = self.prepare_latents(
             image, timesteps, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator
         )

scripts/example.py

@@ -118,7 +118,7 @@ class Script(scripts_manager.Script):
         for i in range(len(args)):
             p.task_args[params[i]] = args[i]
 
-        # you can also re-use existing params from `p` object if pipeline wants them, but under a different name
+        # you can also reuse existing params from `p` object if pipeline wants them, but under a different name
         # for example, if pipeline expects 'image' param, but you want to use 'init_images' instead which is what img2img tab uses
         # p.task_args['image'] = p.init_images[0]
 

scripts/freescale/freescale_pipeline.py

@@ -1118,7 +1118,7 @@ class StableDiffusionXLFreeScale(DiffusionPipeline, FromSingleFileMixin, LoraLoa
         """
         return StableDiffusionXLPipelineOutput(images=results_list)
 
-    # Overrride to properly handle the loading and unloading of the additional text encoder.
+    # Override to properly handle the loading and unloading of the additional text encoder.
     def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
         # We could have accessed the unet config from `lora_state_dict()` too. We pass
         # it here explicitly to be able to tell that it's coming from an SDXL

scripts/freescale/freescale_pipeline_img2img.py

@@ -1174,7 +1174,7 @@ class StableDiffusionXLFreeScaleImg2Img(DiffusionPipeline, FromSingleFileMixin,
         """
         return StableDiffusionXLPipelineOutput(images=results_list)
 
-    # Overrride to properly handle the loading and unloading of the additional text encoder.
+    # Override to properly handle the loading and unloading of the additional text encoder.
     def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
         # We could have accessed the unet config from `lora_state_dict()` too. We pass
         # it here explicitly to be able to tell that it's coming from an SDXL

scripts/instantir/ip_adapter/attention_processor.py

@@ -459,7 +459,7 @@ class split_AttnProcessor2_0(torch.nn.Module):
             hidden_states_0 = hidden_states_0.view(batch_size, channel, height * width).transpose(1, 2)
             hidden_states_1 = hidden_states_1.view(batch_size, channel, height * width).transpose(1, 2)
         else:
-            # directly split sqeuence according to concat dim.
+            # directly split sequence according to concat dim.
             single_dim = original_shape[2] if cat_dim==-2 or cat_dim==2 else original_shape[1]
             hidden_states_0 = hidden_states[:, :single_dim*single_dim,:]
             hidden_states_1 = hidden_states[:, single_dim*(single_dim+1):,:]
@@ -593,7 +593,7 @@ class sep_split_AttnProcessor2_0(torch.nn.Module):
             hidden_states_0 = hidden_states_0.view(batch_size, channel, height * width).transpose(1, 2)
             hidden_states_1 = hidden_states_1.view(batch_size, channel, height * width).transpose(1, 2)
         else:
-            # directly split sqeuence according to concat dim.
+            # directly split sequence according to concat dim.
             single_dim = original_shape[2] if cat_dim==-2 or cat_dim==2 else original_shape[1]
             hidden_states_0 = hidden_states[:, :single_dim*single_dim,:]
             hidden_states_1 = hidden_states[:, single_dim*(single_dim+1):,:]

scripts/instantir/ip_adapter/utils.py

@@ -65,7 +65,7 @@ def init_adapter_in_unet(
         image_projection_layers.append(image_proj_model)
         unet.encoder_hid_proj = MultiIPAdapterImageProjection(image_projection_layers)
 
-        # Adjust unet config to handle addtional ip hidden states.
+        # Adjust unet config to handle additional ip hidden states.
         unet.config.encoder_hid_dim_type = "ip_image_proj"
         unet.to(dtype=dtype, device=device)
 
@@ -155,7 +155,7 @@ def load_adapter_to_pipe(
     image_projection_layers.append(image_proj_model)
     unet.encoder_hid_proj = MultiIPAdapterImageProjection(image_projection_layers)
 
-    # Adjust unet config to handle addtional ip hidden states.
+    # Adjust unet config to handle additional ip hidden states.
     unet.config.encoder_hid_dim_type = "ip_image_proj"
     unet.to(dtype=pipe.dtype, device=pipe.device)
 

scripts/instantir/sdxl_instantir.py

@@ -932,7 +932,7 @@ class InstantIRPipeline(
         noise = torch.randn(latents.shape, generator=generator[0] if isinstance(generator, list) else generator, device=self.vae.device, dtype=self.vae.dtype, layout=torch.strided)
         bsz = latents.shape[0]
         timestep = torch.tensor([timestep]*bsz, device=self.vae.device)
-        # Note that the latents will be scaled aleady by scheduler.add_noise
+        # Note that the latents will be scaled already by scheduler.add_noise
         latents = self.scheduler.add_noise(latents, noise, timestep)
         return latents
 

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 = {

scripts/mulan.py

@@ -3,7 +3,7 @@
 # https://huggingface.co/OpenGVLab/InternVL-14B-224px
 
 """
-- [MuLan](https://github.com/mulanai/MuLan) Multi-langunage prompts - wirte your prompts in ~110 auto-detected languages!
+- [MuLan](https://github.com/mulanai/MuLan) Multi-langunage prompts - write your prompts in ~110 auto-detected languages!
   Compatible with SD15 and SDXL
   Enable in scripts -> MuLan and set encoder to `InternVL-14B-224px` encoder
   (that is currently only supported encoder, but others will be added)

scripts/nudenet_ext.py

@@ -61,7 +61,7 @@ def process(
         policy=False,
         banned=False,
         metadata=True,
-        copy=False, # pylint: disable=unused-argument # compatability
+        copy=False, # pylint: disable=unused-argument # compatibility
         score=0.2,
         blocks=3,
         censor=[],

scripts/pixelsmith/autoencoder_kl.py

@@ -48,7 +48,7 @@ class PixelSmithVAE(ModelMixin, ConfigMixin, FromOriginalModelMixin):
             Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
         force_upcast (`bool`, *optional*, default to `True`):
             If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
-            can be fine-tuned / trained to a lower range without loosing too much precision in which case
+            can be fine-tuned / trained to a lower range without losing too much precision in which case
             `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
     """
 

scripts/pulid/eva_clip/eva_vit_model.py

@@ -71,7 +71,7 @@ class Mlp(nn.Module):
         x = self.fc1(x)
         x = self.act(x)
         # x = self.drop(x)
-        # commit this for the orignal BERT implement
+        # commit this for the original BERT implement
         x = self.ffn_ln(x)
 
         x = self.fc2(x)

scripts/pulid/eva_clip/rope.py

@@ -4,7 +4,7 @@ from torch import nn
 from einops import rearrange, repeat
 import logging
 
-def broadcat(tensors, dim = -1):
+def broadcast(tensors, dim = -1):
     num_tensors = len(tensors)
     shape_lens = set(map(lambda t: len(t.shape), tensors))
     assert len(shape_lens) == 1, 'tensors must all have the same number of dimensions'
@@ -60,7 +60,7 @@ class VisionRotaryEmbedding(nn.Module):
         freqs_w = torch.einsum('..., f -> ... f', t, freqs)
         freqs_w = repeat(freqs_w, '... n -> ... (n r)', r = 2)
 
-        freqs = broadcat((freqs_h[:, None, :], freqs_w[None, :, :]), dim = -1)
+        freqs = broadcast((freqs_h[:, None, :], freqs_w[None, :, :]), dim = -1)
 
         self.register_buffer("freqs_cos", freqs.cos())
         self.register_buffer("freqs_sin", freqs.sin())
@@ -106,7 +106,7 @@ class VisionRotaryEmbeddingFast(nn.Module):
 
         freqs = torch.einsum('..., f -> ... f', t, freqs)
         freqs = repeat(freqs, '... n -> ... (n r)', r = 2)
-        freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim = -1)
+        freqs = broadcast((freqs[:, None, :], freqs[None, :, :]), dim = -1)
 
         freqs_cos = freqs.cos().view(-1, freqs.shape[-1])
         freqs_sin = freqs.sin().view(-1, freqs.shape[-1])

scripts/pulid/eva_clip/tokenizer.py

@@ -27,7 +27,7 @@ def bytes_to_unicode():
     The reversible bpe codes work on unicode strings.
     This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
     When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
+    This is a significant percentage of your normal, say, 32K bpe vocab.
     To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
     And avoids mapping to whitespace/control characters the bpe code barfs on.
     """

scripts/pulid/pulid_sdxl.py

@@ -229,7 +229,7 @@ class StableDiffusionXLPuLIDPipeline:
             if len(self.face_helper.cropped_faces) == 0:
                 raise RuntimeError('facexlib align face fail')
             align_face = self.face_helper.cropped_faces[0]
-            # incase insightface didn't detect face
+            # in case insightface didn't detect face
             if id_ante_embedding is None:
                 id_ante_embedding = self.handler_ante.get_feat(align_face)
 

scripts/softfill.py

@@ -6,7 +6,7 @@ code from: https://github.com/zacheryvaughn/softfill-pipelines/blob/main/pipelin
 sdnext implementation follows after pipeline-end
 """
 
-pnoise2 = None # dynamically instlled and imported module
+pnoise2 = None # dynamically installed and imported module
 
 ### pipeline start
 

scripts/xyz/xyz_grid_classes.py

@@ -144,7 +144,7 @@ class SharedSettingsStackHelper():
         shared.opts.data["disable_apply_params"] = ''
 
     def __exit__(self, exc_type, exc_value, tb):
-        # Restore overriden settings after plot generation
+        # Restore overridden settings after plot generation
         shared.opts.data["disable_apply_metadata"] = self.disable_apply_metadata
         shared.opts.data["disable_apply_params"] = self.disable_apply_params
         shared.opts.data["extra_networks_default_multiplier"] = self.extra_networks_default_multiplier
@@ -278,7 +278,7 @@ axis_options = [
     AxisOption("[Control] End", float, apply_control('control_end')),
     AxisOption("[HiDiffusion] T1", float, apply_override('hidiffusion_t1')),
     AxisOption("[HiDiffusion] T2", float, apply_override('hidiffusion_t2')),
-    AxisOption("[HiDiffusion] Agression step", float, apply_field('hidiffusion_steps')),
+    AxisOption("[HiDiffusion] Aggression step", float, apply_field('hidiffusion_steps')),
     AxisOption("[PAG] Attention scale", float, apply_field('cfg_true')),
     AxisOption("[PAG] Adaptive scaling", float, apply_field('cfg_adaptive')),
     AxisOption("[PAG] Applied layers", str, apply_setting('pag_apply_layers')),