Errata: Fix typos & `\s+$` (#9008)

* Fix typos

* chore: Fix typos

* chore: Update README.md for promptdiffusion example

* Trim trailing white spaces

* Fix a typo

* update number

* chore: update number

* Trim trailing white space

* Update README.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update README.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

examples/research_projects/promptdiffusion/README.md

@@ -46,5 +46,4 @@ pipe.enable_model_cpu_offload()
 # generate image
 generator = torch.manual_seed(0)
 image = pipe("a tortoise", num_inference_steps=20, generator=generator, image_pair=[image_a,image_b], image=query).images[0]
-
 ```

examples/research_projects/promptdiffusion/convert_original_promptdiffusion_to_diffusers.py

@@ -2051,7 +2051,7 @@ if __name__ == "__main__":
         default=512,
         type=int,
         help=(
-            "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
+            "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2"
             " Base. Use 768 for Stable Diffusion v2."
         ),
     )

examples/research_projects/promptdiffusion/pipeline_prompt_diffusion.py

@@ -1253,7 +1253,7 @@ class PromptDiffusionPipeline(
                 )
 
                 if guess_mode and self.do_classifier_free_guidance:
-                    # Infered ControlNet only for the conditional batch.
+                    # Inferred ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]

scripts/convert_original_controlnet_to_diffusers.py

@@ -42,7 +42,7 @@ if __name__ == "__main__":
         default=512,
         type=int,
         help=(
-            "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
+            "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2"
             " Base. Use 768 for Stable Diffusion v2."
         ),
     )

scripts/convert_original_stable_diffusion_to_diffusers.py

@@ -67,7 +67,7 @@ if __name__ == "__main__":
         default=None,
         type=int,
         help=(
-            "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
+            "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2"
             " Base. Use 768 for Stable Diffusion v2."
         ),
     )

src/diffusers/models/embeddings.py

@@ -302,7 +302,7 @@ def get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True):
         If True, return real part and imaginary part separately. Otherwise, return complex numbers.
 
     Returns:
-        `torch.Tensor`: positional embdding with shape `( grid_size * grid_size, embed_dim/2)`.
+        `torch.Tensor`: positional embedding with shape `( grid_size * grid_size, embed_dim/2)`.
     """
     start, stop = crops_coords
     grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32)
@@ -902,7 +902,7 @@ class HunyuanCombinedTimestepTextSizeStyleEmbedding(nn.Module):
         pooled_projections = self.pooler(encoder_hidden_states)  # (N, 1024)
 
         if self.use_style_cond_and_image_meta_size:
-            # extra condition2: image meta size embdding
+            # extra condition2: image meta size embedding
             image_meta_size = self.size_proj(image_meta_size.view(-1))
             image_meta_size = image_meta_size.to(dtype=hidden_dtype)
             image_meta_size = image_meta_size.view(-1, 6 * 256)  # (N, 1536)

src/diffusers/optimization.py

@@ -87,7 +87,7 @@ def get_piecewise_constant_schedule(optimizer: Optimizer, step_rules: str, last_
             The optimizer for which to schedule the learning rate.
         step_rules (`string`):
             The rules for the learning rate. ex: rule_steps="1:10,0.1:20,0.01:30,0.005" it means that the learning rate
-            if multiple 1 for the first 10 steps, mutiple 0.1 for the next 20 steps, multiple 0.01 for the next 30
+            if multiple 1 for the first 10 steps, multiple 0.1 for the next 20 steps, multiple 0.01 for the next 30
             steps and multiple 0.005 for the other steps.
         last_epoch (`int`, *optional*, defaults to -1):
             The index of the last epoch when resuming training.

src/diffusers/pipelines/controlnet/pipeline_controlnet.py

@@ -1272,7 +1272,7 @@ class StableDiffusionControlNetPipeline(
                 )
 
                 if guess_mode and self.do_classifier_free_guidance:
-                    # Infered ControlNet only for the conditional batch.
+                    # Inferred ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]

src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py

@@ -1244,7 +1244,7 @@ class StableDiffusionControlNetImg2ImgPipeline(
                 )
 
                 if guess_mode and self.do_classifier_free_guidance:
-                    # Infered ControlNet only for the conditional batch.
+                    # Inferred ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py

@@ -1408,7 +1408,7 @@ class StableDiffusionControlNetInpaintPipeline(
                 )
 
                 if guess_mode and self.do_classifier_free_guidance:
-                    # Infered ControlNet only for the conditional batch.
+                    # Inferred ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py

@@ -1739,7 +1739,7 @@ class StableDiffusionXLControlNetInpaintPipeline(
                 )
 
                 if guess_mode and self.do_classifier_free_guidance:
-                    # Infered ControlNet only for the conditional batch.
+                    # Inferred ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py

@@ -1487,7 +1487,7 @@ class StableDiffusionXLControlNetPipeline(
                 )
 
                 if guess_mode and self.do_classifier_free_guidance:
-                    # Infered ControlNet only for the conditional batch.
+                    # Inferred ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -1551,7 +1551,7 @@ class StableDiffusionXLControlNetImg2ImgPipeline(
                 )
 
                 if guess_mode and self.do_classifier_free_guidance:
-                    # Infered ControlNet only for the conditional batch.
+                    # Inferred ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]

src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py

@@ -1249,7 +1249,7 @@ class StableDiffusionControlNetPAGPipeline(
                 )
 
                 if guess_mode and self.do_classifier_free_guidance:
-                    # Infered ControlNet only for the conditional batch.
+                    # Inferred ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]

utils/tests_fetcher.py

@@ -106,7 +106,7 @@ def checkout_commit(repo: Repo, commit_id: str):
 def clean_code(content: str) -> str:
     """
     Remove docstrings, empty line or comments from some code (used to detect if a diff is real or only concern
-    comments or docstings).
+    comments or docstrings).
 
     Args:
         content (`str`): The code to clean
@@ -165,7 +165,7 @@ def keep_doc_examples_only(content: str) -> str:
 def get_all_tests() -> List[str]:
     """
     Walks the `tests` folder to return a list of files/subfolders. This is used to split the tests to run when using
-    paralellism. The split is:
+    parallelism. The split is:
 
     - folders under `tests`: (`tokenization`, `pipelines`, etc) except the subfolder `models` is excluded.
     - folders under `tests/models`: `bert`, `gpt2`, etc.
@@ -635,7 +635,7 @@ def get_tree_starting_at(module: str, edges: List[Tuple[str, str]]) -> List[Unio
 
     Args:
         module (`str`): The module that will be the root of the subtree we want.
-        eges (`List[Tuple[str, str]]`): The list of all edges of the tree.
+        edges (`List[Tuple[str, str]]`): The list of all edges of the tree.
 
     Returns:
         `List[Union[str, List[str]]]`: The tree to print in the following format: [module, [list of edges
@@ -663,7 +663,7 @@ def print_tree_deps_of(module, all_edges=None):
 
     Args:
         module (`str`): The module that will be the root of the subtree we want.
-        all_eges (`List[Tuple[str, str]]`, *optional*):
+        all_edges (`List[Tuple[str, str]]`, *optional*):
             The list of all edges of the tree. Will be set to `create_reverse_dependency_tree()` if not passed.
     """
     if all_edges is None:
@@ -706,7 +706,7 @@ def init_test_examples_dependencies() -> Tuple[Dict[str, List[str]], List[str]]:
     for framework in ["flax", "pytorch", "tensorflow"]:
         test_files = list((PATH_TO_EXAMPLES / framework).glob("test_*.py"))
         all_examples.extend(test_files)
-        # Remove the files at the root of examples/framework since they are not proper examples (they are eith utils
+        # Remove the files at the root of examples/framework since they are not proper examples (they are either utils
         # or example test files).
         examples = [
             f for f in (PATH_TO_EXAMPLES / framework).glob("**/*.py") if f.parent != PATH_TO_EXAMPLES / framework