[Docs] Fix typos (#7451)

* Fix typos

* Fix typos

* Fix typos

---------

Co-authored-by: Sayak Paul <[email protected]>

Author: tolgacangoz

docs/source/en/training/controlnet.md

@@ -88,7 +88,7 @@ accelerate config default
 
 Or if your environment doesn't support an interactive shell, like a notebook, you can use:
 
-```bash
+```py
 from accelerate.utils import write_basic_config
 
 write_basic_config()

docs/source/en/training/custom_diffusion.md

@@ -54,7 +54,7 @@ accelerate config default
 
 Or if your environment doesn't support an interactive shell, like a notebook, you can use:
 
-```bash
+```py
 from accelerate.utils import write_basic_config
 
 write_basic_config()
@@ -84,7 +84,7 @@ Many of the basic parameters are described in the [DreamBooth](dreambooth#script
 - `--freeze_model`: freezes the key and value parameters in the cross-attention layer; the default is `crossattn_kv`, but you can set it to `crossattn` to train all the parameters in the cross-attention layer
 - `--concepts_list`: to learn multiple concepts, provide a path to a JSON file containing the concepts
 - `--modifier_token`: a special word used to represent the learned concept
-- `--initializer_token`:
+- `--initializer_token`: a special word used to initialize the embeddings of the `modifier_token`
 
 ### Prior preservation loss
 

docs/source/en/training/dreambooth.md

@@ -67,7 +67,7 @@ accelerate config default
 
 Or if your environment doesn't support an interactive shell, like a notebook, you can use:
 
-```bash
+```py
 from accelerate.utils import write_basic_config
 
 write_basic_config()
@@ -180,7 +180,7 @@ elif args.pretrained_model_name_or_path:
         revision=args.revision,
         use_fast=False,
     )
-    
+
 # Load scheduler and models
 noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
 text_encoder = text_encoder_cls.from_pretrained(

docs/source/en/training/instructpix2pix.md

@@ -51,7 +51,7 @@ accelerate config default
 
 Or if your environment doesn't support an interactive shell, like a notebook, you can use:
 
-```bash
+```py
 from accelerate.utils import write_basic_config
 
 write_basic_config()
@@ -89,7 +89,7 @@ The dataset preprocessing code and training loop are found in the [`main()`](htt
 
 As with the script parameters, a walkthrough of the training script is provided in the [Text-to-image](text2image#training-script) training guide. Instead, this guide takes a look at the InstructPix2Pix relevant parts of the script.
 
-The script begins by modifing the [number of input channels](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L445) in the first convolutional layer of the UNet to account for InstructPix2Pix's additional conditioning image:
+The script begins by modifying the [number of input channels](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L445) in the first convolutional layer of the UNet to account for InstructPix2Pix's additional conditioning image:
 
 ```py
 in_channels = 8

docs/source/en/training/kandinsky.md

@@ -59,7 +59,7 @@ accelerate config default
 
 Or if your environment doesn't support an interactive shell, like a notebook, you can use:
 
-```bash
+```py
 from accelerate.utils import write_basic_config
 
 write_basic_config()
@@ -235,7 +235,7 @@ accelerate launch --mixed_precision="fp16"  train_text_to_image_prior.py \
   --validation_prompts="A robot pokemon, 4k photo" \
   --report_to="wandb" \
   --push_to_hub \
-  --output_dir="kandi2-prior-pokemon-model" 
+  --output_dir="kandi2-prior-pokemon-model"
 ```
 
 </hfoption>
@@ -259,7 +259,7 @@ accelerate launch --mixed_precision="fp16"  train_text_to_image_decoder.py \
   --validation_prompts="A robot pokemon, 4k photo" \
   --report_to="wandb" \
   --push_to_hub \
-  --output_dir="kandi2-decoder-pokemon-model" 
+  --output_dir="kandi2-decoder-pokemon-model"
 ```
 
 </hfoption>

docs/source/en/training/lcm_distill.md

@@ -53,7 +53,7 @@ accelerate config default
 
 Or if your environment doesn't support an interactive shell, like a notebook, you can use:
 
-```bash
+```py
 from accelerate.utils import write_basic_config
 
 write_basic_config()
@@ -252,4 +252,4 @@ The SDXL training script is discussed in more detail in the [SDXL training](sdxl
 Congratulations on distilling a LCM model! To learn more about LCM, the following may be helpful:
 
 - Learn how to use [LCMs for inference](../using-diffusers/lcm) for text-to-image, image-to-image, and with LoRA checkpoints.
-- Read the [SDXL in 4 steps with Latent Consistency LoRAs](https://huggingface.co/blog/lcm_lora) blog post to learn more about SDXL LCM-LoRA's for super fast inference, quality comparisons, benchmarks, and more.
+- Read the [SDXL in 4 steps with Latent Consistency LoRAs](https://huggingface.co/blog/lcm_lora) blog post to learn more about SDXL LCM-LoRA's for super fast inference, quality comparisons, benchmarks, and more.

docs/source/en/training/sdxl.md

@@ -59,7 +59,7 @@ accelerate config default
 
 Or if your environment doesn't support an interactive shell, like a notebook, you can use:
 
-```bash
+```py
 from accelerate.utils import write_basic_config
 
 write_basic_config()

docs/source/en/training/t2i_adapters.md

@@ -53,7 +53,7 @@ accelerate config default
 
 Or if your environment doesn't support an interactive shell, like a notebook, you can use:
 
-```bash
+```py
 from accelerate.utils import write_basic_config
 
 write_basic_config()

docs/source/en/training/text2image.md

@@ -69,7 +69,7 @@ accelerate config default
 
 Or if your environment doesn't support an interactive shell, like a notebook, you can use:
 
-```bash
+```py
 from accelerate.utils import write_basic_config
 
 write_basic_config()

docs/source/en/training/text_inversion.md

@@ -67,7 +67,7 @@ accelerate config default
 
 Or if your environment doesn't support an interactive shell, like a notebook, you can use:
 
-```bash
+```py
 from accelerate.utils import write_basic_config
 
 write_basic_config()

docs/source/en/training/unconditional_training.md

@@ -51,7 +51,7 @@ accelerate config default
 
 Or if your environment doesn't support an interactive shell like a notebook, you can use:
 
-```bash
+```py
 from accelerate.utils import write_basic_config
 
 write_basic_config()

docs/source/en/training/wuerstchen.md

@@ -53,7 +53,7 @@ accelerate config default
 
 Or if your environment doesn't support an interactive shell, like a notebook, you can use:
 
-```bash
+```py
 from accelerate.utils import write_basic_config
 
 write_basic_config()
@@ -173,7 +173,7 @@ pipeline = AutoPipelineForText2Image.from_pretrained("path/to/saved/model", torc
 
 caption = "A cute bird pokemon holding a shield"
 images = pipeline(
-    caption, 
+    caption,
     width=1024,
     height=1536,
     prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,

examples/community/README.md

@@ -935,7 +935,7 @@ image = pipe(prompt, generator=generator, num_inference_steps=50).images[0]
 ### Checkpoint Merger Pipeline
 Based on the AUTOMATIC1111/webui for checkpoint merging. This is a custom pipeline that merges upto 3 pretrained model checkpoints as long as they are in the HuggingFace model_index.json format.
 
-The checkpoint merging is currently memory intensive as it modifies the weights of a DiffusionPipeline object in place. Expect atleast 13GB RAM Usage on Kaggle GPU kernels and
+The checkpoint merging is currently memory intensive as it modifies the weights of a DiffusionPipeline object in place. Expect at least 13GB RAM Usage on Kaggle GPU kernels and
 on colab you might run out of the 12GB memory even while merging two checkpoints.
 
 Usage:-

examples/community/checkpoint_merger.py

@@ -103,7 +103,7 @@ def merge(self, pretrained_model_name_or_path_list: List[Union[str, os.PathLike]
         print(f"Combining with alpha={alpha}, interpolation mode={interp}")
 
         checkpoint_count = len(pretrained_model_name_or_path_list)
-        # Ignore result from model_index_json comparision of the two checkpoints
+        # Ignore result from model_index_json comparison of the two checkpoints
         force = kwargs.pop("force", False)
 
         # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
@@ -217,7 +217,7 @@ def merge(self, pretrained_model_name_or_path_list: List[Union[str, os.PathLike]
                         ]
                         checkpoint_path_2 = files[0] if len(files) > 0 else None
                 # For an attr if both checkpoint_path_1 and 2 are None, ignore.
-                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
+                # If at least one is present, deal with it according to interp method, of course only if the state_dict keys match.
                 if checkpoint_path_1 is None and checkpoint_path_2 is None:
                     print(f"Skipping {attr}: not present in 2nd or 3d model")
                     continue

examples/community/latent_consistency_interpolate.py

@@ -726,7 +726,7 @@ def __call__(
             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.
             embedding_interpolation_type (`str`, *optional*, defaults to `"lerp"`):
                 The type of interpolation to use for interpolating between text embeddings. Choose between `"lerp"` and `"slerp"`.
             latent_interpolation_type (`str`, *optional*, defaults to `"slerp"`):
@@ -779,7 +779,7 @@ def __call__(
         else:
             batch_size = prompt_embeds.shape[0]
         if batch_size < 2:
-            raise ValueError(f"`prompt` must have length of atleast 2 but found {batch_size}")
+            raise ValueError(f"`prompt` must have length of at least 2 but found {batch_size}")
         if num_images_per_prompt != 1:
             raise ValueError("`num_images_per_prompt` must be `1` as no other value is supported yet")
         if prompt_embeds is not None:
@@ -883,7 +883,7 @@ def __call__(
                 ) as batch_progress_bar:
                     for batch_index in range(0, bs, process_batch_size):
                         batch_inference_latents = inference_latents[batch_index : batch_index + process_batch_size]
-                        batch_inference_embedddings = inference_embeddings[
+                        batch_inference_embeddings = inference_embeddings[
                             batch_index : batch_index + process_batch_size
                         ]
 
@@ -892,7 +892,7 @@ def __call__(
                         )
                         timesteps = self.scheduler.timesteps
 
-                        current_bs = batch_inference_embedddings.shape[0]
+                        current_bs = batch_inference_embeddings.shape[0]
                         w = torch.tensor(self.guidance_scale - 1).repeat(current_bs)
                         w_embedding = self.get_guidance_scale_embedding(
                             w, embedding_dim=self.unet.config.time_cond_proj_dim
@@ -901,14 +901,14 @@ def __call__(
                         # 10. Perform inference for current batch
                         with self.progress_bar(total=num_inference_steps) as progress_bar:
                             for index, t in enumerate(timesteps):
-                                batch_inference_latents = batch_inference_latents.to(batch_inference_embedddings.dtype)
+                                batch_inference_latents = batch_inference_latents.to(batch_inference_embeddings.dtype)
 
                                 # model prediction (v-prediction, eps, x)
                                 model_pred = self.unet(
                                     batch_inference_latents,
                                     t,
                                     timestep_cond=w_embedding,
-                                    encoder_hidden_states=batch_inference_embedddings,
+                                    encoder_hidden_states=batch_inference_embeddings,
                                     cross_attention_kwargs=self.cross_attention_kwargs,
                                     return_dict=False,
                                 )[0]
@@ -924,8 +924,8 @@ def __call__(
                                     callback_outputs = callback_on_step_end(self, index, t, callback_kwargs)
 
                                     batch_inference_latents = callback_outputs.pop("latents", batch_inference_latents)
-                                    batch_inference_embedddings = callback_outputs.pop(
-                                        "prompt_embeds", batch_inference_embedddings
+                                    batch_inference_embeddings = callback_outputs.pop(
+                                        "prompt_embeds", batch_inference_embeddings
                                     )
                                     w_embedding = callback_outputs.pop("w_embedding", w_embedding)
                                     denoised = callback_outputs.pop("denoised", denoised)
@@ -939,7 +939,7 @@ def __call__(
                                         step_idx = index // getattr(self.scheduler, "order", 1)
                                         callback(step_idx, t, batch_inference_latents)
 
-                        denoised = denoised.to(batch_inference_embedddings.dtype)
+                        denoised = denoised.to(batch_inference_embeddings.dtype)
 
                         # Note: This is not supported because you would get black images in your latent walk if
                         #       NSFW concept is detected

examples/community/lpw_stable_diffusion_xl.py

@@ -164,7 +164,7 @@ def get_prompts_tokens_with_weights(clip_tokenizer: CLIPTokenizer, prompt: str):
         text_tokens (list)
             A list contains token ids
         text_weight (list)
-            A list contains the correspodent weight of token ids
+            A list contains the correspondent weight of token ids
 
     Example:
         import torch
@@ -1028,7 +1028,7 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
                 # because `num_inference_steps` might be even given that every timestep
                 # (except the highest one) is duplicated. If `num_inference_steps` is even it would
                 # mean that we cut the timesteps in the middle of the denoising step
-                # (between 1st and 2nd devirative) which leads to incorrect results. By adding 1
+                # (between 1st and 2nd derivative) which leads to incorrect results. By adding 1
                 # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler
                 num_inference_steps = num_inference_steps + 1
 
@@ -1531,7 +1531,7 @@ def __call__(
             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:
 
@@ -2131,7 +2131,7 @@ def inpaint(
             **kwargs,
         )
 
-    # 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

examples/community/mixture_tiling.py

@@ -196,7 +196,7 @@ def __call__(
             guidance_scale_tiles: specific weights for classifier-free guidance in each tile.
             guidance_scale_tiles: specific weights for classifier-free guidance in each tile. If None, the value provided in guidance_scale will be used.
             seed_tiles: specific seeds for the initialization latents in each tile. These will override the latents generated for the whole canvas using the standard seed parameter.
-            seed_tiles_mode: either "full" "exclusive". If "full", all the latents affected by the tile be overriden. If "exclusive", only the latents that are affected exclusively by this tile (and no other tiles) will be overrriden.
+            seed_tiles_mode: either "full" "exclusive". If "full", all the latents affected by the tile be overriden. If "exclusive", only the latents that are affected exclusively by this tile (and no other tiles) will be overriden.
             seed_reroll_regions: a list of tuples in the form (start row, end row, start column, end column, seed) defining regions in pixel space for which the latents will be overriden using the given seed. Takes priority over seed_tiles.
             cpu_vae: the decoder from latent space to pixel space can require too mucho GPU RAM for large images. If you find out of memory errors at the end of the generation process, try setting this parameter to True to run the decoder in CPU. Slower, but should run without memory issues.
 
@@ -325,7 +325,7 @@ def __call__(
         if accepts_eta:
             extra_step_kwargs["eta"] = eta
 
-        # Mask for tile weights strenght
+        # Mask for tile weights strength
         tile_weights = self._gaussian_weights(tile_width, tile_height, batch_size)
 
         # Diffusion timesteps

examples/community/pipeline_animatediff_controlnet.py

@@ -832,15 +832,15 @@ def __call__(
             clip_skip (`int`, *optional*):
                 Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
                 the output of the pre-final layer will be used for computing the prompt embeddings.
-            allback_on_step_end (`Callable`, *optional*):
+            callback_on_step_end (`Callable`, *optional*):
                 A function that calls at the end of each denoising steps during the inference. The function is called
                 with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
                 callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
                 `callback_on_step_end_tensor_inputs`.
             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:
 

examples/community/pipeline_demofusion_sdxl.py

@@ -1280,7 +1280,7 @@ def __call__(
 
         return output_images
 
-    # 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

examples/community/pipeline_sdxl_style_aligned.py

@@ -887,7 +887,7 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
                 # because `num_inference_steps` might be even given that every timestep
                 # (except the highest one) is duplicated. If `num_inference_steps` is even it would
                 # mean that we cut the timesteps in the middle of the denoising step
-                # (between 1st and 2nd devirative) which leads to incorrect results. By adding 1
+                # (between 1st and 2nd derivative) which leads to incorrect results. By adding 1
                 # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler
                 num_inference_steps = num_inference_steps + 1