[`Docs`] Fix typos and update files at API's Pipelines page 1 (#5744)

* Fix typos, update, add Copyright info, and trim trailing whitespace

* Update alt_diffusion.md

* Remove nonoperational demo

* Update docs/source/en/api/pipelines/consistency_models.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/en/api/pipelines/latent_consistency_models.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

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

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

+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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+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
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+http://www.apache.org/licenses/LICENSE-2.0
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+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.
+-->
+
 # Latent Consistency Models
 
-Latent Consistency Models (LCMs) were proposed in [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) by Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao.
+Latent Consistency Models (LCMs) were proposed in [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://huggingface.co/papers/2310.04378) by Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao.
 
-The abstract of the [paper](https://arxiv.org/pdf/2310.04378.pdf) is as follows:
+The abstract of the paper is as follows:
 
-*Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. However, the iterative sampling process is computationally intensive and leads to slow generation. Inspired by Consistency Models (song et al.), we propose Latent Consistency Models (LCMs), enabling swift inference with minimal steps on any pre-trained LDMs, including Stable Diffusion (rombach et al). Viewing the guided reverse diffusion process as solving an augmented probability flow ODE (PF-ODE), LCMs are designed to directly predict the solution of such ODE in latent space, mitigating the need for numerous iterations and allowing rapid, high-fidelity sampling. Efficiently distilled from pre-trained classifier-free guided diffusion models, a high-quality 768 x 768 2~4-step LCM takes only 32 A100 GPU hours for training. Furthermore, we introduce Latent Consistency Fine-tuning (LCF), a novel method that is tailored for fine-tuning LCMs on customized image datasets. Evaluation on the LAION-5B-Aesthetics dataset demonstrates that LCMs achieve state-of-the-art text-to-image generation performance with few-step inference.*
+*Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. However, the iterative sampling process is computationally intensive and leads to slow generation. Inspired by Consistency Models (song et al.), we propose Latent Consistency Models (LCMs), enabling swift inference with minimal steps on any pre-trained LDMs, including Stable Diffusion (rombach et al). Viewing the guided reverse diffusion process as solving an augmented probability flow ODE (PF-ODE), LCMs are designed to directly predict the solution of such ODE in latent space, mitigating the need for numerous iterations and allowing rapid, high-fidelity sampling. Efficiently distilled from pre-trained classifier-free guided diffusion models, a high-quality 768 x 768 2~4-step LCM takes only 32 A100 GPU hours for training. Furthermore, we introduce Latent Consistency Fine-tuning (LCF), a novel method that is tailored for fine-tuning LCMs on customized image datasets. Evaluation on the LAION-5B-Aesthetics dataset demonstrates that LCMs achieve state-of-the-art text-to-image generation performance with few-step inference. Project Page: [this https URL](https://latent-consistency-models.github.io/).*
 
 A demo for the [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) checkpoint can be found [here](https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model).
 

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

@@ -18,7 +18,7 @@ The abstract from the paper is:
 
 *By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs.*
 
-The original codebase can be found at [Compvis/latent-diffusion](https://github.com/CompVis/latent-diffusion).
+The original codebase can be found at [CompVis/latent-diffusion](https://github.com/CompVis/latent-diffusion).
 
 <Tip>
 
@@ -37,4 +37,4 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)
 	- __call__
 
 ## ImagePipelineOutput
-[[autodoc]] pipelines.ImagePipelineOutput
\ No newline at end of file
+[[autodoc]] pipelines.ImagePipelineOutput

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

@@ -32,4 +32,4 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)
 	- all
 
 ## StableDiffusionPipelineOutput
-[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
\ No newline at end of file
+[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput

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

@@ -13,20 +13,17 @@ specific language governing permissions and limitations under the License.
 # MusicLDM
 
 MusicLDM was proposed in [MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies](https://huggingface.co/papers/2308.01546) by Ke Chen, Yusong Wu, Haohe Liu, Marianna Nezhurina, Taylor Berg-Kirkpatrick, Shlomo Dubnov.
-MusicLDM takes a text prompt as input and predicts the corresponding music sample. 
+MusicLDM takes a text prompt as input and predicts the corresponding music sample.
 
-Inspired by [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview) and [AudioLDM](https://huggingface.co/docs/diffusers/api/pipelines/audioldm/overview),
+Inspired by [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview) and [AudioLDM](https://huggingface.co/docs/diffusers/api/pipelines/audioldm),
 MusicLDM is a text-to-music _latent diffusion model (LDM)_ that learns continuous audio representations from [CLAP](https://huggingface.co/docs/transformers/main/model_doc/clap)
 latents.
 
-MusicLDM is trained on a corpus of 466 hours of music data. Beat-synchronous data augmentation strategies are applied to 
-the music samples, both in the time domain and in the latent space. Using beat-synchronous data augmentation strategies 
-encourages the model to interpolate between the training samples, but stay within the domain of the training data. The 
-result is generated music that is more diverse while staying faithful to the corresponding style.
+MusicLDM is trained on a corpus of 466 hours of music data. Beat-synchronous data augmentation strategies are applied to the music samples, both in the time domain and in the latent space. Using beat-synchronous data augmentation strategies encourages the model to interpolate between the training samples, but stay within the domain of the training data. The result is generated music that is more diverse while staying faithful to the corresponding style.
 
 The abstract of the paper is the following:
 
-*In this paper, we present MusicLDM, a state-of-the-art text-to-music model that adapts Stable Diffusion and AudioLDM architectures to the music domain. We achieve this by retraining the contrastive language-audio pretraining model (CLAP) and the Hifi-GAN vocoder, as components of MusicLDM, on a collection of music data samples. Then, we leverage a beat tracking model and propose two different mixup strategies for data augmentation: beat-synchronous audio mixup and beat-synchronous latent mixup, to encourage the model to generate music more diverse while still staying faithful to the corresponding style.*
+*Diffusion models have shown promising results in cross-modal generation tasks, including text-to-image and text-to-audio generation. However, generating music, as a special type of audio, presents unique challenges due to limited availability of music data and sensitive issues related to copyright and plagiarism. In this paper, to tackle these challenges, we first construct a state-of-the-art text-to-music model, MusicLDM, that adapts Stable Diffusion and AudioLDM architectures to the music domain. We achieve this by retraining the contrastive language-audio pretraining model (CLAP) and the Hifi-GAN vocoder, as components of MusicLDM, on a collection of music data samples. Then, to address the limitations of training data and to avoid plagiarism, we leverage a beat tracking model and propose two different mixup strategies for data augmentation: beat-synchronous audio mixup and beat-synchronous latent mixup, which recombine training audio directly or via a latent embeddings space, respectively. Such mixup strategies encourage the model to interpolate between musical training samples and generate new music within the convex hull of the training data, making the generated music more diverse while still staying faithful to the corresponding style. In addition to popular evaluation metrics, we design several new evaluation metrics based on CLAP score to demonstrate that our proposed MusicLDM and beat-synchronous mixup strategies improve both the quality and novelty of generated music, as well as the correspondence between input text and generated music.*
 
 This pipeline was contributed by [sanchit-gandhi](https://huggingface.co/sanchit-gandhi).
 
@@ -52,4 +49,4 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)
 ## MusicLDMPipeline
 [[autodoc]] MusicLDMPipeline
 	- all
-	- __call__
\ No newline at end of file
+	- __call__

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

@@ -31,6 +31,7 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an
 | Pipeline | Tasks |
 |---|---|
 | [AltDiffusion](alt_diffusion) | image2image |
+| [AnimateDiff](animatediff) | text2video |
 | [Attend-and-Excite](attend_and_excite) | text2image |
 | [Audio Diffusion](audio_diffusion) | image2audio |
 | [AudioLDM](audioldm) | text2audio |
@@ -46,33 +47,35 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an
 | [DeepFloyd IF](deepfloyd_if) | text2image, image2image, inpainting, super-resolution |
 | [DiffEdit](diffedit) | inpainting |
 | [DiT](dit) | text2image |
-| [GLIGEN](gligen) | text2image |
+| [GLIGEN](stable_diffusion/gligen) | text2image |
 | [InstructPix2Pix](pix2pix) | image editing |
-| [Kandinsky](kandinsky) | text2image, image2image, inpainting, interpolation |
+| [Kandinsky 2.1](kandinsky) | text2image, image2image, inpainting, interpolation |
 | [Kandinsky 2.2](kandinsky_v22) | text2image, image2image, inpainting |
+| [Latent Consistency Models](latent_consistency_models) | text2image |
 | [Latent Diffusion](latent_diffusion) | text2image, super-resolution |
-| [LDM3D](ldm3d_diffusion) | text2image, text-to-3D |
+| [LDM3D](stable_diffusion/ldm3d_diffusion) | text2image, text-to-3D |
 | [MultiDiffusion](panorama) | text2image |
 | [MusicLDM](musicldm) | text2audio |
-| [PaintByExample](paint_by_example) | inpainting |
+| [Paint by Example](paint_by_example) | inpainting |
 | [ParaDiGMS](paradigms) | text2image |
 | [Pix2Pix Zero](pix2pix_zero) | image editing |
+| [PixArt-α](pixart) | text2image |
 | [PNDM](pndm) | unconditional image generation |
 | [RePaint](repaint) | inpainting |
-| [ScoreSdeVe](score_sde_ve) | unconditional image generation |
+| [Score SDE VE](score_sde_ve) | unconditional image generation |
 | [Self-Attention Guidance](self_attention_guidance) | text2image |
 | [Semantic Guidance](semantic_stable_diffusion) | text2image |
 | [Shap-E](shap_e) | text-to-3D, image-to-3D |
 | [Spectrogram Diffusion](spectrogram_diffusion) |  |
 | [Stable Diffusion](stable_diffusion/overview) | text2image, image2image, depth2image, inpainting, image variation, latent upscaler, super-resolution |
 | [Stable Diffusion Model Editing](model_editing) | model editing |
-| [Stable Diffusion XL](stable_diffusion_xl) | text2image, image2image, inpainting |
+| [Stable Diffusion XL](stable_diffusion/stable_diffusion_xl) | text2image, image2image, inpainting |
 | [Stable unCLIP](stable_unclip) | text2image, image variation |
-| [KarrasVe](karras_ve) | unconditional image generation |
-| [T2I Adapter](adapter) | text2image |
+| [Stochastic Karras VE](stochastic_karras_ve) | unconditional image generation |
+| [T2I-Adapter](stable_diffusion/adapter) | text2image |
 | [Text2Video](text_to_video) | text2video, video2video |
-| [Text2Video Zero](text_to_video_zero) | text2video |
-| [UnCLIP](unclip) | text2image, image variation |
+| [Text2Video-Zero](text_to_video_zero) | text2video |
+| [unCLIP](unclip) | text2image, image variation |
 | [Unconditional Latent Diffusion](latent_diffusion_uncond) | unconditional image generation |
 | [UniDiffuser](unidiffuser) | text2image, image2text, image variation, text variation, unconditional image generation, unconditional audio generation |
 | [Value-guided planning](value_guided_sampling) | value guided sampling |

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.
 -->
 
-# Paint By Example
+# 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.
 
@@ -22,7 +22,7 @@ The original codebase can be found at [Fantasy-Studio/Paint-by-Example](https://
 
 ## Tips
 
-PaintByExample is supported by the official [Fantasy-Studio/Paint-by-Example](https://huggingface.co/Fantasy-Studio/Paint-by-Example) checkpoint. The checkpoint is warm-started from [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) to inpaint partly masked images conditioned on example and reference images.
+Paint by Example is supported by the official [Fantasy-Studio/Paint-by-Example](https://huggingface.co/Fantasy-Studio/Paint-by-Example) checkpoint. The checkpoint is warm-started from [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) to inpaint partly masked images conditioned on example and reference images.
 
 <Tip>
 
@@ -36,4 +36,4 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)
 	- __call__
 
 ## StableDiffusionPipelineOutput
-[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
\ No newline at end of file
+[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput

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

@@ -22,19 +22,12 @@ You can find additional information about MultiDiffusion on the [project page](h
 
 ## Tips
 
-While calling [`StableDiffusionPanoramaPipeline`], it's possible to specify the `view_batch_size` parameter to be > 1. 
+While calling [`StableDiffusionPanoramaPipeline`], it's possible to specify the `view_batch_size` parameter to be > 1.
 For some GPUs with high performance, this can speedup the generation process and increase VRAM usage.
 
 To generate panorama-like images make sure you pass the width parameter accordingly. We recommend a width value of 2048 which is the default.
 
-Circular padding is applied to ensure there are no stitching artifacts when working with 
-panoramas to ensure a seamless transition from the rightmost part to the leftmost part. 
-By enabling circular padding (set `circular_padding=True`), the operation applies additional 
-crops after the rightmost point of the image, allowing the model to "see” the transition 
-from the rightmost part to the leftmost part. This helps maintain visual consistency in 
-a 360-degree sense and creates a proper “panorama” that can be viewed using 360-degree 
-panorama viewers. When decoding latents in Stable Diffusion, circular padding is applied 
-to ensure that the decoded latents match in the RGB space.
+Circular padding is applied to ensure there are no stitching artifacts when working with panoramas to ensure a seamless transition from the rightmost part to the leftmost part. By enabling circular padding (set `circular_padding=True`), the operation applies additional crops after the rightmost point of the image, allowing the model to "see” the transition from the rightmost part to the leftmost part. This helps maintain visual consistency in a 360-degree sense and creates a proper “panorama” that can be viewed using 360-degree panorama viewers. When decoding latents in Stable Diffusion, circular padding is applied to ensure that the decoded latents match in the RGB space.
 
 For example, without circular padding, there is a stitching artifact (default):
 ![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/indoor_%20no_circular_padding.png)
@@ -54,4 +47,4 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)
 	- all
 
 ## StableDiffusionPipelineOutput
-[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
\ No newline at end of file
+[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput