[Docs] Fix typos (#5583)

* Add Copyright info

* Fix typos, improve, update

* Update deepfloyd_if.md

* Update ldm3d_diffusion.md

* Update opt_overview.md

docs/source/en/api/pipelines/paint_by_example.md

@@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# PaintByExample
+# Paint By Example
 
 [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://huggingface.co/papers/2211.13227) is by Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, Fang Wen.
 

docs/source/en/api/pipelines/stable_diffusion/ldm3d_diffusion.md

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # Text-to-(RGB, depth)
 
-LDM3D was proposed in [LDM3D: Latent Diffusion Model for 3D](https://huggingface.co/papers/2305.10853) by Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle Aflalo, Shao-Yen Tseng, Fabio Nonato, Matthias Muller, and Vasudev Lal. LDM3D generates an image and a depth map from a given text prompt unlike the existing text-to-image diffusion models such as [Stable Diffusion](./stable_diffusion/overview) which only generates an image. With almost the same number of parameters, LDM3D achieves to create a latent space that can compress both the RGB images and the depth maps. 
+LDM3D was proposed in [LDM3D: Latent Diffusion Model for 3D](https://huggingface.co/papers/2305.10853) by Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle Aflalo, Shao-Yen Tseng, Fabio Nonato, Matthias Muller, and Vasudev Lal. LDM3D generates an image and a depth map from a given text prompt unlike the existing text-to-image diffusion models such as [Stable Diffusion](./overview) which only generates an image. With almost the same number of parameters, LDM3D achieves to create a latent space that can compress both the RGB images and the depth maps. 
 
 The abstract from the paper is:
 

docs/source/en/api/pipelines/unclip.md

@@ -7,9 +7,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# UnCLIP
+# unCLIP
 
-[Hierarchical Text-Conditional Image Generation with CLIP Latents](https://huggingface.co/papers/2204.06125) is by Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen. The UnCLIP model in 🤗 Diffusers comes from kakaobrain's [karlo]((https://github.com/kakaobrain/karlo)).
+[Hierarchical Text-Conditional Image Generation with CLIP Latents](https://huggingface.co/papers/2204.06125) is by Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen. The unCLIP model in 🤗 Diffusers comes from kakaobrain's [karlo]((https://github.com/kakaobrain/karlo)).
 
 The abstract from the paper is following:
 
@@ -34,4 +34,4 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)
 	- __call__
 
 ## ImagePipelineOutput
-[[autodoc]] pipelines.ImagePipelineOutput
+[[autodoc]] pipelines.ImagePipelineOutput
\ No newline at end of file

docs/source/en/optimization/opt_overview.md

@@ -12,6 +12,6 @@ specific language governing permissions and limitations under the License.
 
 # Overview
 
-Generating high-quality outputs is computationally intensive, especially during each iterative step where you go from a noisy output to a less noisy output. One of 🤗 Diffuser's goal is to make this technology widely accessible to everyone, which includes enabling fast inference on consumer and specialized hardware.
+Generating high-quality outputs is computationally intensive, especially during each iterative step where you go from a noisy output to a less noisy output. One of 🤗 Diffuser's goals is to make this technology widely accessible to everyone, which includes enabling fast inference on consumer and specialized hardware.
 
-This section will cover tips and tricks - like half-precision weights and sliced attention - for optimizing inference speed and reducing memory-consumption. You'll also learn how to speed up your PyTorch code with [`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) or [ONNX Runtime](https://onnxruntime.ai/docs/), and enable memory-efficient attention with [xFormers](https://facebookresearch.github.io/xformers/). There are also guides for running inference on specific hardware like Apple Silicon, and Intel or Habana processors.
+This section will cover tips and tricks - like half-precision weights and sliced attention - for optimizing inference speed and reducing memory-consumption. You'll also learn how to speed up your PyTorch code with [`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) or [ONNX Runtime](https://onnxruntime.ai/docs/), and enable memory-efficient attention with [xFormers](https://facebookresearch.github.io/xformers/). There are also guides for running inference on specific hardware like Apple Silicon, and Intel or Habana processors.
\ No newline at end of file

docs/source/en/using-diffusers/control_brightness.md

+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
 # Control image brightness
 
 The Stable Diffusion pipeline is mediocre at generating images that are either very bright or dark as explained in the [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) paper. The solutions proposed in the paper are currently implemented in the [`DDIMScheduler`] which you can use to improve the lighting in your images.

docs/source/en/using-diffusers/controlnet.md

+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
 # ControlNet
 
 ControlNet is a type of model for controlling image diffusion models by conditioning the model with an additional input image. There are many types of conditioning inputs (canny edge, user sketching, human pose, depth, and more) you can use to control a diffusion model. This is hugely useful because it affords you greater control over image generation, making it easier to generate specific images without experimenting with different text prompts or denoising values as much.

docs/source/en/using-diffusers/diffedit.md

+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
 # DiffEdit
 
 [[open-in-colab]]

docs/source/en/using-diffusers/distilled_sd.md

+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
 # Distilled Stable Diffusion inference
 
 [[open-in-colab]]

docs/source/en/using-diffusers/freeu.md

+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
 # Improve generation quality with FreeU
 
 [[open-in-colab]]

docs/source/en/using-diffusers/pipeline_overview.md

@@ -14,4 +14,4 @@ specific language governing permissions and limitations under the License.
 
 A pipeline is an end-to-end class that provides a quick and easy way to use a diffusion system for inference by bundling independently trained models and schedulers together. Certain combinations of models and schedulers define specific pipeline types, like [`StableDiffusionXLPipeline`] or [`StableDiffusionControlNetPipeline`], with specific capabilities. All pipeline types inherit from the base [`DiffusionPipeline`] class; pass it any checkpoint, and it'll automatically detect the pipeline type and load the necessary components.
 
-This section demonstrates how to use specific pipelines such as Stable Diffusion XL, ControlNet, and DiffEdit. You'll also learn how to use a distilled version of the Stable Diffusion model to speed up inference, how to create reproducible pipelines, and how to use and contribute community pipelines.
+This section demonstrates how to use specific pipelines such as Stable Diffusion XL, ControlNet, and DiffEdit. You'll also learn how to use a distilled version of the Stable Diffusion model to speed up inference, how to create reproducible pipelines, and how to use and contribute community pipelines.
\ No newline at end of file

docs/source/en/using-diffusers/sdxl.md

+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
 # Stable Diffusion XL
 
 [[open-in-colab]]

docs/source/en/using-diffusers/shap-e.md

+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
 # Shap-E
 
 [[open-in-colab]]

docs/source/en/using-diffusers/stable_diffusion_jax_how_to.md

+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
 # JAX/Flax
 
 [[open-in-colab]]

docs/source/en/using-diffusers/textual_inversion_inference.md

+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
 # Textual inversion
 
 [[open-in-colab]]

examples/README.md

@@ -19,7 +19,7 @@ Diffusers examples are a collection of scripts to demonstrate how to effectively
 for a variety of use cases involving training or fine-tuning.
 
 **Note**: If you are looking for **official** examples on how to use `diffusers` for inference, 
-please have a look at [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines)
+please have a look at [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines).
 
 Our examples aspire to be **self-contained**, **easy-to-tweak**, **beginner-friendly** and for **one-purpose-only**.
 More specifically, this means:

examples/textual_inversion/README.md

@@ -25,12 +25,12 @@ cd diffusers
 pip install .
 ```
 
-Then cd in the example folder  and run
+Then cd in the example folder and run:
 ```bash
 pip install -r requirements.txt
 ```
 
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
+And initialize an [🤗 Accelerate](https://github.com/huggingface/accelerate/) environment with:
 
 ```bash
 accelerate config
@@ -56,7 +56,7 @@ snapshot_download("diffusers/cat_toy_example", local_dir=local_dir, repo_type="d
 ```
 
 This will be our training data.
-Now we can launch the training using
+Now we can launch the training using:
 
 **___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
 
@@ -68,12 +68,14 @@ accelerate launch textual_inversion.py \
   --pretrained_model_name_or_path=$MODEL_NAME \
   --train_data_dir=$DATA_DIR \
   --learnable_property="object" \
-  --placeholder_token="<cat-toy>" --initializer_token="toy" \
+  --placeholder_token="<cat-toy>" \
+  --initializer_token="toy" \
   --resolution=512 \
   --train_batch_size=1 \
   --gradient_accumulation_steps=4 \
   --max_train_steps=3000 \
-  --learning_rate=5.0e-04 --scale_lr \
+  --learning_rate=5.0e-04 \
+  --scale_lr \
   --lr_scheduler="constant" \
   --lr_warmup_steps=0 \
   --push_to_hub \
@@ -85,10 +87,10 @@ A full training run takes ~1 hour on one V100 GPU.
 **Note**: As described in [the official paper](https://arxiv.org/abs/2208.01618) 
 only one embedding vector is used for the placeholder token, *e.g.* `"<cat-toy>"`.
 However, one can also add multiple embedding vectors for the placeholder token 
-to inclease the number of fine-tuneable parameters. This can help the model to learn 
-more complex details. To use multiple embedding vectors, you can should define `--num_vectors` 
+to increase the number of fine-tuneable parameters. This can help the model to learn 
+more complex details. To use multiple embedding vectors, you should define `--num_vectors` 
 to a number larger than one, *e.g.*:
-```
+```bash
 --num_vectors 5
 ```
 
@@ -131,11 +133,13 @@ python textual_inversion_flax.py \
   --pretrained_model_name_or_path=$MODEL_NAME \
   --train_data_dir=$DATA_DIR \
   --learnable_property="object" \
-  --placeholder_token="<cat-toy>" --initializer_token="toy" \
+  --placeholder_token="<cat-toy>" \
+  --initializer_token="toy" \
   --resolution=512 \
   --train_batch_size=1 \
   --max_train_steps=3000 \
-  --learning_rate=5.0e-04 --scale_lr \
+  --learning_rate=5.0e-04 \
+  --scale_lr \
   --output_dir="textual_inversion_cat"
 ```
 It should be at least 70% faster than the PyTorch script with the same configuration.