fix: correct grammar in docstrings and error messages

src/diffusers/models/model_loading_utils.py

@@ -725,8 +725,8 @@ def _caching_allocator_warmup(
 ) -> None:
     """
     This function warm-ups the caching allocator based on the size of the model tensors that will reside on each
-    device. It allows to have one large call to Malloc, instead of recursively calling it later when loading the model,
-    which is actually the loading speed bottleneck. Calling this function allows to cut the model loading time by a
+    device. It allows having one large call to Malloc, instead of recursively calling it later when loading the model,
+    which is actually the loading speed bottleneck. Calling this function allows cutting the model loading time by a
     very large margin.
     """
     factor = 2 if hf_quantizer is None else hf_quantizer.get_cuda_warm_up_factor()

src/diffusers/pipelines/auto_pipeline.py

@@ -966,7 +966,7 @@ def __init__(self, *args, **kwargs):
     @validate_hf_hub_args
     def from_pretrained(cls, pretrained_model_or_path, **kwargs):
         r"""
-        Instantiates a inpainting Pytorch diffusion pipeline from pretrained pipeline weight.
+        Instantiates an inpainting Pytorch diffusion pipeline from pretrained pipeline weight.
 
         The from_pretrained() method takes care of returning the correct pipeline class instance by:
             1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
@@ -1121,7 +1121,7 @@ def from_pretrained(cls, pretrained_model_or_path, **kwargs):
     @classmethod
     def from_pipe(cls, pipeline, **kwargs):
         r"""
-        Instantiates a inpainting Pytorch diffusion pipeline from another instantiated diffusion pipeline class.
+        Instantiates an inpainting Pytorch diffusion pipeline from another instantiated diffusion pipeline class.
 
         The from_pipe() method takes care of returning the correct pipeline class instance by finding the inpainting
         pipeline linked to the pipeline class using pattern matching on pipeline class name.

src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py

@@ -247,8 +247,8 @@ class StableDiffusionXLPAGInpaintPipeline(
             A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`):
-            Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config
-            of `stabilityai/stable-diffusion-xl-refiner-1-0`.
+            Whether the `unet` requires an `aesthetic_score` condition to be passed during inference. Also see the
+            config of `stabilityai/stable-diffusion-xl-refiner-1-0`.
         force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
             Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
             `stabilityai/stable-diffusion-xl-base-1-0`.

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py

@@ -257,8 +257,8 @@ class StableDiffusionXLInpaintPipeline(
             A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`):
-            Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config
-            of `stabilityai/stable-diffusion-xl-refiner-1-0`.
+            Whether the `unet` requires an `aesthetic_score` condition to be passed during inference. Also see the
+            config of `stabilityai/stable-diffusion-xl-refiner-1-0`.
         force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
             Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
             `stabilityai/stable-diffusion-xl-base-1-0`.

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py

@@ -153,8 +153,8 @@ class StableDiffusionXLInstructPix2PixPipeline(
             A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`):
-            Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config
-            of `stabilityai/stable-diffusion-xl-refiner-1-0`.
+            Whether the `unet` requires an `aesthetic_score` condition to be passed during inference. Also see the
+            config of `stabilityai/stable-diffusion-xl-refiner-1-0`.
         force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
             Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
             `stabilityai/stable-diffusion-xl-base-1-0`.

src/diffusers/quantizers/base.py

@@ -78,7 +78,7 @@ def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
 
     def update_device_map(self, device_map: dict[str, Any] | None) -> dict[str, Any] | None:
         """
-        Override this method if you want to pass a override the existing device map with a new one. E.g. for
+        Override this method if you want to override the existing device map with a new one. E.g. for
         bitsandbytes, since `accelerate` is a hard requirement, if no device_map is passed, the device_map is set to
         `"auto"``