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Use HF Papers (#11567)

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docs/source/en/api/models/asymmetricautoencoderkl.md
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# AsymmetricAutoencoderKL
Improved larger variational autoencoder (VAE) model with KL loss for inpainting task: [Designing a Better Asymmetric VQGAN for StableDiffusion](https://arxiv.org/abs/2306.04632) by Zixin Zhu, Xuelu Feng, Dongdong Chen, Jianmin Bao, Le Wang, Yinpeng Chen, Lu Yuan, Gang Hua.
Improved larger variational autoencoder (VAE) model with KL loss for inpainting task: [Designing a Better Asymmetric VQGAN for StableDiffusion](https://huggingface.co/papers/2306.04632) by Zixin Zhu, Xuelu Feng, Dongdong Chen, Jianmin Bao, Le Wang, Yinpeng Chen, Lu Yuan, Gang Hua.
The abstract from the paper is:

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docs/source/en/api/models/autoencoderkl.md
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# AutoencoderKL
The variational autoencoder (VAE) model with KL loss was introduced in [Auto-Encoding Variational Bayes](https://arxiv.org/abs/1312.6114v11) by Diederik P. Kingma and Max Welling. The model is used in 🤗 Diffusers to encode images into latents and to decode latent representations into images.
The variational autoencoder (VAE) model with KL loss was introduced in [Auto-Encoding Variational Bayes](https://huggingface.co/papers/1312.6114v11) by Diederik P. Kingma and Max Welling. The model is used in 🤗 Diffusers to encode images into latents and to decode latent representations into images.
The abstract from the paper is:

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docs/source/en/api/models/consisid_transformer3d.md
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# ConsisIDTransformer3DModel
A Diffusion Transformer model for 3D data from [ConsisID](https://github.com/PKU-YuanGroup/ConsisID) was introduced in [Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://arxiv.org/pdf/2411.17440) by Peking University & University of Rochester & etc.
A Diffusion Transformer model for 3D data from [ConsisID](https://github.com/PKU-YuanGroup/ConsisID) was introduced in [Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://huggingface.co/papers/2411.17440) by Peking University & University of Rochester & etc.
The model can be loaded with the following code snippet.

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docs/source/en/api/models/controlnet_hunyuandit.md
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# HunyuanDiT2DControlNetModel
HunyuanDiT2DControlNetModel is an implementation of ControlNet for [Hunyuan-DiT](https://arxiv.org/abs/2405.08748).
HunyuanDiT2DControlNetModel is an implementation of ControlNet for [Hunyuan-DiT](https://huggingface.co/papers/2405.08748).
ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.

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# SparseControlNetModel
SparseControlNetModel is an implementation of ControlNet for [AnimateDiff](https://arxiv.org/abs/2307.04725).
SparseControlNetModel is an implementation of ControlNet for [AnimateDiff](https://huggingface.co/papers/2307.04725).
ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
The SparseCtrl version of ControlNet was introduced in [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models](https://arxiv.org/abs/2311.16933) for achieving controlled generation in text-to-video diffusion models by Yuwei Guo, Ceyuan Yang, Anyi Rao, Maneesh Agrawala, Dahua Lin, and Bo Dai.
The SparseCtrl version of ControlNet was introduced in [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models](https://huggingface.co/papers/2311.16933) for achieving controlled generation in text-to-video diffusion models by Yuwei Guo, Ceyuan Yang, Anyi Rao, Maneesh Agrawala, Dahua Lin, and Bo Dai.
The abstract from the paper is:

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docs/source/en/api/pipelines/amused.md
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aMUSEd was introduced in [aMUSEd: An Open MUSE Reproduction](https://huggingface.co/papers/2401.01808) by Suraj Patil, William Berman, Robin Rombach, and Patrick von Platen.
Amused is a lightweight text to image model based off of the [MUSE](https://arxiv.org/abs/2301.00704) architecture. Amused is particularly useful in applications that require a lightweight and fast model such as generating many images quickly at once.
Amused is a lightweight text to image model based off of the [MUSE](https://huggingface.co/papers/2301.00704) architecture. Amused is particularly useful in applications that require a lightweight and fast model such as generating many images quickly at once.
Amused is a vqvae token based transformer that can generate an image in fewer forward passes than many diffusion models. In contrast with muse, it uses the smaller text encoder CLIP-L/14 instead of t5-xxl. Due to its small parameter count and few forward pass generation process, amused can generate many images quickly. This benefit is seen particularly at larger batch sizes.

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docs/source/en/api/pipelines/animatediff.md
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## Overview
[AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725) by Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai.
[AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://huggingface.co/papers/2307.04725) by Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai.
The abstract of the paper is the following:
@@ -187,7 +187,7 @@ Here are some sample outputs:
### AnimateDiffSparseControlNetPipeline
[SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models](https://arxiv.org/abs/2311.16933) for achieving controlled generation in text-to-video diffusion models by Yuwei Guo, Ceyuan Yang, Anyi Rao, Maneesh Agrawala, Dahua Lin, and Bo Dai.
[SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models](https://huggingface.co/papers/2311.16933) for achieving controlled generation in text-to-video diffusion models by Yuwei Guo, Ceyuan Yang, Anyi Rao, Maneesh Agrawala, Dahua Lin, and Bo Dai.
The abstract from the paper is:
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## Using FreeInit
[FreeInit: Bridging Initialization Gap in Video Diffusion Models](https://arxiv.org/abs/2312.07537) by Tianxing Wu, Chenyang Si, Yuming Jiang, Ziqi Huang, Ziwei Liu.
[FreeInit: Bridging Initialization Gap in Video Diffusion Models](https://huggingface.co/papers/2312.07537) by Tianxing Wu, Chenyang Si, Yuming Jiang, Ziqi Huang, Ziwei Liu.
FreeInit is an effective method that improves temporal consistency and overall quality of videos generated using video-diffusion-models without any addition training. It can be applied to AnimateDiff, ModelScope, VideoCrafter and various other video generation models seamlessly at inference time, and works by iteratively refining the latent-initialization noise. More details can be found it the paper.
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## Using FreeNoise
[FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling](https://arxiv.org/abs/2310.15169) by Haonan Qiu, Menghan Xia, Yong Zhang, Yingqing He, Xintao Wang, Ying Shan, Ziwei Liu.
[FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling](https://huggingface.co/papers/2310.15169) by Haonan Qiu, Menghan Xia, Yong Zhang, Yingqing He, Xintao Wang, Ying Shan, Ziwei Liu.
FreeNoise is a sampling mechanism that can generate longer videos with short-video generation models by employing noise-rescheduling, temporal attention over sliding windows, and weighted averaging of latent frames. It also can be used with multiple prompts to allow for interpolated video generations. More details are available in the paper.

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# AudioLDM 2
AudioLDM 2 was proposed in [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://arxiv.org/abs/2308.05734) by Haohe Liu et al. AudioLDM 2 takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional sound effects, human speech and music.
AudioLDM 2 was proposed in [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://huggingface.co/papers/2308.05734) by Haohe Liu et al. AudioLDM 2 takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional sound effects, human speech and music.
Inspired by [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview), AudioLDM 2 is a text-to-audio _latent diffusion model (LDM)_ that learns continuous audio representations from text embeddings. Two text encoder models are used to compute the text embeddings from a prompt input: the text-branch of [CLAP](https://huggingface.co/docs/transformers/main/en/model_doc/clap) and the encoder of [Flan-T5](https://huggingface.co/docs/transformers/main/en/model_doc/flan-t5). These text embeddings are then projected to a shared embedding space by an [AudioLDM2ProjectionModel](https://huggingface.co/docs/diffusers/main/api/pipelines/audioldm2#diffusers.AudioLDM2ProjectionModel). A [GPT2](https://huggingface.co/docs/transformers/main/en/model_doc/gpt2) _language model (LM)_ is used to auto-regressively predict eight new embedding vectors, conditional on the projected CLAP and Flan-T5 embeddings. The generated embedding vectors and Flan-T5 text embeddings are used as cross-attention conditioning in the LDM. The [UNet](https://huggingface.co/docs/diffusers/main/en/api/pipelines/audioldm2#diffusers.AudioLDM2UNet2DConditionModel) of AudioLDM 2 is unique in the sense that it takes **two** cross-attention embeddings, as opposed to one cross-attention conditioning, as in most other LDMs.

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# BLIP-Diffusion
BLIP-Diffusion was proposed in [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://arxiv.org/abs/2305.14720). It enables zero-shot subject-driven generation and control-guided zero-shot generation.
BLIP-Diffusion was proposed in [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://huggingface.co/papers/2305.14720). It enables zero-shot subject-driven generation and control-guided zero-shot generation.
The abstract from the paper is:

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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
</div>
[CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://arxiv.org/abs/2408.06072) from Tsinghua University & ZhipuAI, by Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, Da Yin, Xiaotao Gu, Yuxuan Zhang, Weihan Wang, Yean Cheng, Ting Liu, Bin Xu, Yuxiao Dong, Jie Tang.
[CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://huggingface.co/papers/2408.06072) from Tsinghua University & ZhipuAI, by Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, Da Yin, Xiaotao Gu, Yuxuan Zhang, Weihan Wang, Yean Cheng, Ting Liu, Bin Xu, Yuxiao Dong, Jie Tang.
The abstract from the paper is:

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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
</div>
[Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://arxiv.org/abs/2411.17440) from Peking University & University of Rochester & etc, by Shenghai Yuan, Jinfa Huang, Xianyi He, Yunyang Ge, Yujun Shi, Liuhan Chen, Jiebo Luo, Li Yuan.
[Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://huggingface.co/papers/2411.17440) from Peking University & University of Rochester & etc, by Shenghai Yuan, Jinfa Huang, Xianyi He, Yunyang Ge, Yujun Shi, Liuhan Chen, Jiebo Luo, Li Yuan.
The abstract from the paper is:

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docs/source/en/api/pipelines/controlnet_hunyuandit.md
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# ControlNet with Hunyuan-DiT
HunyuanDiTControlNetPipeline is an implementation of ControlNet for [Hunyuan-DiT](https://arxiv.org/abs/2405.08748).
HunyuanDiTControlNetPipeline is an implementation of ControlNet for [Hunyuan-DiT](https://huggingface.co/papers/2405.08748).
ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.

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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
</div>
[Packing Input Frame Context in Next-Frame Prediction Models for Video Generation](https://arxiv.org/abs/2504.12626) by Lvmin Zhang and Maneesh Agrawala.
[Packing Input Frame Context in Next-Frame Prediction Models for Video Generation](https://huggingface.co/papers/2504.12626) by Lvmin Zhang and Maneesh Agrawala.
*We present a neural network structure, FramePack, to train next-frame (or next-frame-section) prediction models for video generation. The FramePack compresses input frames to make the transformer context length a fixed number regardless of the video length. As a result, we are able to process a large number of frames using video diffusion with computation bottleneck similar to image diffusion. This also makes the training video batch sizes significantly higher (batch sizes become comparable to image diffusion training). We also propose an anti-drifting sampling method that generates frames in inverted temporal order with early-established endpoints to avoid exposure bias (error accumulation over iterations). Finally, we show that existing video diffusion models can be finetuned with FramePack, and their visual quality may be improved because the next-frame prediction supports more balanced diffusion schedulers with less extreme flow shift timesteps.*

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# Hunyuan-DiT
![chinese elements understanding](https://github.com/gnobitab/diffusers-hunyuan/assets/1157982/39b99036-c3cb-4f16-bb1a-40ec25eda573)
[Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding](https://arxiv.org/abs/2405.08748) from Tencent Hunyuan.
[Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding](https://huggingface.co/papers/2405.08748) from Tencent Hunyuan.
The abstract from the paper is:

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* Unlike SVD, it additionally accepts text prompts as inputs.
* It can generate higher resolution videos.
* When using the [`DDIMScheduler`] (which is default for this pipeline), less than 50 steps for inference leads to bad results.
* This implementation is 1-stage variant of I2VGenXL. The main figure in the [I2VGen-XL](https://arxiv.org/abs/2311.04145) paper shows a 2-stage variant, however, 1-stage variant works well. See [this discussion](https://github.com/huggingface/diffusers/discussions/7952) for more details.
* This implementation is 1-stage variant of I2VGenXL. The main figure in the [I2VGen-XL](https://huggingface.co/papers/2311.04145) paper shows a 2-stage variant, however, 1-stage variant works well. See [this discussion](https://github.com/huggingface/diffusers/discussions/7952) for more details.
## I2VGenXLPipeline
[[autodoc]] I2VGenXLPipeline

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![latte text-to-video](https://github.com/Vchitect/Latte/blob/52bc0029899babbd6e9250384c83d8ed2670ff7a/visuals/latte.gif?raw=true)
[Latte: Latent Diffusion Transformer for Video Generation](https://arxiv.org/abs/2401.03048) from Monash University, Shanghai AI Lab, Nanjing University, and Nanyang Technological University.
[Latte: Latent Diffusion Transformer for Video Generation](https://huggingface.co/papers/2401.03048) from Monash University, Shanghai AI Lab, Nanjing University, and Nanyang Technological University.
The abstract from the paper is:
*We propose a novel Latent Diffusion Transformer, namely Latte, for video generation. Latte first extracts spatio-temporal tokens from input videos and then adopts a series of Transformer blocks to model video distribution in the latent space. In order to model a substantial number of tokens extracted from videos, four efficient variants are introduced from the perspective of decomposing the spatial and temporal dimensions of input videos. To improve the quality of generated videos, we determine the best practices of Latte through rigorous experimental analysis, including video clip patch embedding, model variants, timestep-class information injection, temporal positional embedding, and learning strategies. Our comprehensive evaluation demonstrates that Latte achieves state-of-the-art performance across four standard video generation datasets, i.e., FaceForensics, SkyTimelapse, UCF101, and Taichi-HD. In addition, we extend Latte to text-to-video generation (T2V) task, where Latte achieves comparable results compared to recent T2V models. We strongly believe that Latte provides valuable insights for future research on incorporating Transformers into diffusion models for video generation.*
**Highlights**: Latte is a latent diffusion transformer proposed as a backbone for modeling different modalities (trained for text-to-video generation here). It achieves state-of-the-art performance across four standard video benchmarks - [FaceForensics](https://arxiv.org/abs/1803.09179), [SkyTimelapse](https://arxiv.org/abs/1709.07592), [UCF101](https://arxiv.org/abs/1212.0402) and [Taichi-HD](https://arxiv.org/abs/2003.00196). To prepare and download the datasets for evaluation, please refer to [this https URL](https://github.com/Vchitect/Latte/blob/main/docs/datasets_evaluation.md).
**Highlights**: Latte is a latent diffusion transformer proposed as a backbone for modeling different modalities (trained for text-to-video generation here). It achieves state-of-the-art performance across four standard video benchmarks - [FaceForensics](https://huggingface.co/papers/1803.09179), [SkyTimelapse](https://huggingface.co/papers/1709.07592), [UCF101](https://huggingface.co/papers/1212.0402) and [Taichi-HD](https://huggingface.co/papers/2003.00196). To prepare and download the datasets for evaluation, please refer to [this https URL](https://github.com/Vchitect/Latte/blob/main/docs/datasets_evaluation.md).
This pipeline was contributed by [maxin-cn](https://github.com/maxin-cn). The original codebase can be found [here](https://github.com/Vchitect/Latte). The original weights can be found under [hf.co/maxin-cn](https://huggingface.co/maxin-cn).

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---
[Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers](https://arxiv.org/abs/2405.05945) from Alpha-VLLM, OpenGVLab, Shanghai AI Laboratory.
[Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers](https://huggingface.co/papers/2405.05945) from Alpha-VLLM, OpenGVLab, Shanghai AI Laboratory.
The abstract from the paper is:

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# OmniGen
[OmniGen: Unified Image Generation](https://arxiv.org/pdf/2409.11340) from BAAI, by Shitao Xiao, Yueze Wang, Junjie Zhou, Huaying Yuan, Xingrun Xing, Ruiran Yan, Chaofan Li, Shuting Wang, Tiejun Huang, Zheng Liu.
[OmniGen: Unified Image Generation](https://huggingface.co/papers/2409.11340) from BAAI, by Shitao Xiao, Yueze Wang, Junjie Zhou, Huaying Yuan, Xingrun Xing, Ruiran Yan, Chaofan Li, Shuting Wang, Tiejun Huang, Zheng Liu.
The abstract from the paper is:

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## Overview
[PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models](https://arxiv.org/abs/2312.13964) by Yiming Zhang, Zhening Xing, Yanhong Zeng, Youqing Fang, Kai Chen
[PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models](https://huggingface.co/papers/2312.13964) by Yiming Zhang, Zhening Xing, Yanhong Zeng, Youqing Fang, Kai Chen
Recent advancements in personalized text-to-image (T2I) models have revolutionized content creation, empowering non-experts to generate stunning images with unique styles. While promising, adding realistic motions into these personalized images by text poses significant challenges in preserving distinct styles, high-fidelity details, and achieving motion controllability by text. In this paper, we present PIA, a Personalized Image Animator that excels in aligning with condition images, achieving motion controllability by text, and the compatibility with various personalized T2I models without specific tuning. To achieve these goals, PIA builds upon a base T2I model with well-trained temporal alignment layers, allowing for the seamless transformation of any personalized T2I model into an image animation model. A key component of PIA is the introduction of the condition module, which utilizes the condition frame and inter-frame affinity as input to transfer appearance information guided by the affinity hint for individual frame synthesis in the latent space. This design mitigates the challenges of appearance-related image alignment within and allows for a stronger focus on aligning with motion-related guidance.
@@ -92,7 +92,7 @@ If you plan on using a scheduler that can clip samples, make sure to disable it
## Using FreeInit
[FreeInit: Bridging Initialization Gap in Video Diffusion Models](https://arxiv.org/abs/2312.07537) by Tianxing Wu, Chenyang Si, Yuming Jiang, Ziqi Huang, Ziwei Liu.
[FreeInit: Bridging Initialization Gap in Video Diffusion Models](https://huggingface.co/papers/2312.07537) by Tianxing Wu, Chenyang Si, Yuming Jiang, Ziqi Huang, Ziwei Liu.
FreeInit is an effective method that improves temporal consistency and overall quality of videos generated using video-diffusion-models without any addition training. It can be applied to PIA, AnimateDiff, ModelScope, VideoCrafter and various other video generation models seamlessly at inference time, and works by iteratively refining the latent-initialization noise. More details can be found it the paper.

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# Stable Audio
Stable Audio was proposed in [Stable Audio Open](https://arxiv.org/abs/2407.14358) by Zach Evans et al. . it takes a text prompt as input and predicts the corresponding sound or music sample.
Stable Audio was proposed in [Stable Audio Open](https://huggingface.co/papers/2407.14358) by Zach Evans et al. . it takes a text prompt as input and predicts the corresponding sound or music sample.
Stable Audio Open generates variable-length (up to 47s) stereo audio at 44.1kHz from text prompts. It comprises three components: an autoencoder that compresses waveforms into a manageable sequence length, a T5-based text embedding for text conditioning, and a transformer-based diffusion (DiT) model that operates in the latent space of the autoencoder.

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# T2I-Adapter
[T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.08453) by Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie.
[T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.08453) by Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie.
Using the pretrained models we can provide control images (for example, a depth map) to control Stable Diffusion text-to-image generation so that it follows the structure of the depth image and fills in the details.

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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.
Two checkpoints are available for use:
- [ldm3d-original](https://huggingface.co/Intel/ldm3d). The original checkpoint used in the [paper](https://arxiv.org/pdf/2305.10853.pdf)
- [ldm3d-original](https://huggingface.co/Intel/ldm3d). The original checkpoint used in the [paper](https://huggingface.co/papers/2305.10853)
- [ldm3d-4c](https://huggingface.co/Intel/ldm3d-4c). The new version of LDM3D using 4 channels inputs instead of 6-channels inputs and finetuned on higher resolution images.
@@ -48,7 +48,7 @@ Make sure to check out the Stable Diffusion [Tips](overview#tips) section to lea
# Upscaler
[LDM3D-VR](https://arxiv.org/pdf/2311.03226.pdf) is an extended version of LDM3D.
[LDM3D-VR](https://huggingface.co/papers/2311.03226) is an extended version of LDM3D.
The abstract from the paper is:
*Latent diffusion models have proven to be state-of-the-art in the creation and manipulation of visual outputs. However, as far as we know, the generation of depth maps jointly with RGB is still limited. We introduce LDM3D-VR, a suite of diffusion models targeting virtual reality development that includes LDM3D-pano and LDM3D-SR. These models enable the generation of panoramic RGBD based on textual prompts and the upscaling of low-resolution inputs to high-resolution RGBD, respectively. Our models are fine-tuned from existing pretrained models on datasets containing panoramic/high-resolution RGB images, depth maps and captions. Both models are evaluated in comparison to existing related methods*

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@@ -17,7 +17,7 @@ specific language governing permissions and limitations under the License.
<img alt="MPS" src="https://img.shields.io/badge/MPS-000000?style=flat&logo=apple&logoColor=white%22">
</div>
Stable Diffusion 3 (SD3) was proposed in [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://arxiv.org/pdf/2403.03206.pdf) by Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Muller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach.
Stable Diffusion 3 (SD3) was proposed in [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://huggingface.co/papers/2403.03206) by Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Muller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach.
The abstract from the paper is:

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@@ -22,7 +22,7 @@ specific language governing permissions and limitations under the License.
<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
</div>
[ModelScope Text-to-Video Technical Report](https://arxiv.org/abs/2308.06571) is by Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, Shiwei Zhang.
[ModelScope Text-to-Video Technical Report](https://huggingface.co/papers/2308.06571) is by Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, Shiwei Zhang.
The abstract from the paper is:

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@@ -34,7 +34,7 @@ Our key modifications include (i) enriching the latent codes of the generated fr
Experiments show that this leads to low overhead, yet high-quality and remarkably consistent video generation. Moreover, our approach is not limited to text-to-video synthesis but is also applicable to other tasks such as conditional and content-specialized video generation, and Video Instruct-Pix2Pix, i.e., instruction-guided video editing.
As experiments show, our method performs comparably or sometimes better than recent approaches, despite not being trained on additional video data.*
You can find additional information about Text2Video-Zero on the [project page](https://text2video-zero.github.io/), [paper](https://arxiv.org/abs/2303.13439), and [original codebase](https://github.com/Picsart-AI-Research/Text2Video-Zero).
You can find additional information about Text2Video-Zero on the [project page](https://text2video-zero.github.io/), [paper](https://huggingface.co/papers/2303.13439), and [original codebase](https://github.com/Picsart-AI-Research/Text2Video-Zero).
## Usage example
@@ -55,9 +55,9 @@ result = [(r * 255).astype("uint8") for r in result]
imageio.mimsave("video.mp4", result, fps=4)
```
You can change these parameters in the pipeline call:
* Motion field strength (see the [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1):
* Motion field strength (see the [paper](https://huggingface.co/papers/2303.13439), Sect. 3.3.1):
* `motion_field_strength_x` and `motion_field_strength_y`. Default: `motion_field_strength_x=12`, `motion_field_strength_y=12`
* `T` and `T'` (see the [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1)
* `T` and `T'` (see the [paper](https://huggingface.co/papers/2303.13439), Sect. 3.3.1)
* `t0` and `t1` in the range `{0, ..., num_inference_steps}`. Default: `t0=45`, `t1=48`
* Video length:
* `video_length`, the number of frames video_length to be generated. Default: `video_length=8`

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@@ -15,7 +15,7 @@
# VisualCloze
[VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning](https://arxiv.org/abs/2504.07960) is an innovative in-context learning based universal image generation framework that offers key capabilities:
[VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning](https://huggingface.co/papers/2504.07960) is an innovative in-context learning based universal image generation framework that offers key capabilities:
1. Support for various in-domain tasks
2. Generalization to unseen tasks through in-context learning
3. Unify multiple tasks into one step and generate both target image and intermediate results
@@ -33,7 +33,7 @@ The abstract from the paper is:
VisualCloze is a two-stage cascade pipeline, containing `VisualClozeGenerationPipeline` and `VisualClozeUpsamplingPipeline`.
- In `VisualClozeGenerationPipeline`, each image is downsampled before concatenating images into a grid layout, avoiding excessively high resolutions. VisualCloze releases two models suitable for diffusers, i.e., [VisualClozePipeline-384](https://huggingface.co/VisualCloze/VisualClozePipeline-384) and [VisualClozePipeline-512](https://huggingface.co/VisualCloze/VisualClozePipeline-384), which downsample images to resolutions of 384 and 512, respectively.
- `VisualClozeUpsamplingPipeline` uses [SDEdit](https://arxiv.org/abs/2108.01073) to enable high-resolution image synthesis.
- `VisualClozeUpsamplingPipeline` uses [SDEdit](https://huggingface.co/papers/2108.01073) to enable high-resolution image synthesis.
The `VisualClozePipeline` integrates both stages to support convenient end-to-end sampling, while also allowing users to utilize each pipeline independently as needed.

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# CosineDPMSolverMultistepScheduler
The [`CosineDPMSolverMultistepScheduler`] is a variant of [`DPMSolverMultistepScheduler`] with cosine schedule, proposed by Nichol and Dhariwal (2021).
It is being used in the [Stable Audio Open](https://arxiv.org/abs/2407.14358) paper and the [Stability-AI/stable-audio-tool](https://github.com/Stability-AI/stable-audio-tool) codebase.
It is being used in the [Stable Audio Open](https://huggingface.co/papers/2407.14358) paper and the [Stability-AI/stable-audio-tool](https://github.com/Stability-AI/stable-audio-tool) codebase.
This scheduler was contributed by [Yoach Lacombe](https://huggingface.co/ylacombe).

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# FlowMatchEulerDiscreteScheduler
`FlowMatchEulerDiscreteScheduler` is based on the flow-matching sampling introduced in [Stable Diffusion 3](https://arxiv.org/abs/2403.03206).
`FlowMatchEulerDiscreteScheduler` is based on the flow-matching sampling introduced in [Stable Diffusion 3](https://huggingface.co/papers/2403.03206).
## FlowMatchEulerDiscreteScheduler
[[autodoc]] FlowMatchEulerDiscreteScheduler

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# FlowMatchHeunDiscreteScheduler
`FlowMatchHeunDiscreteScheduler` is based on the flow-matching sampling introduced in [EDM](https://arxiv.org/abs/2403.03206).
`FlowMatchHeunDiscreteScheduler` is based on the flow-matching sampling introduced in [EDM](https://huggingface.co/papers/2403.03206).
## FlowMatchHeunDiscreteScheduler
[[autodoc]] FlowMatchHeunDiscreteScheduler

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@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
## Overview
Multistep and onestep scheduler (Algorithm 3) introduced alongside latent consistency models in the paper [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.
Multistep and onestep scheduler (Algorithm 3) introduced alongside latent consistency models in the paper [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.
This scheduler should be able to generate good samples from [`LatentConsistencyModelPipeline`] in 1-8 steps.
## LCMScheduler

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@@ -54,7 +54,7 @@ The team works daily to make the technical and non-technical tools available to
- **Encouraging safety in deployment**
- [**Safe Stable Diffusion**](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_safe): It mitigates the well-known issue that models, like Stable Diffusion, that are trained on unfiltered, web-crawled datasets tend to suffer from inappropriate degeneration. Related paper: [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://arxiv.org/abs/2211.05105).
- [**Safe Stable Diffusion**](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_safe): It mitigates the well-known issue that models, like Stable Diffusion, that are trained on unfiltered, web-crawled datasets tend to suffer from inappropriate degeneration. Related paper: [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://huggingface.co/papers/2211.05105).
- [**Safety Checker**](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py): It checks and compares the class probability of a set of hard-coded harmful concepts in the embedding space against an image after it has been generated. The harmful concepts are intentionally hidden to prevent reverse engineering of the checker.

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> [!TIP]
> This document has now grown outdated given the emergence of existing evaluation frameworks for diffusion models for image generation. Please check
> out works like [HEIM](https://crfm.stanford.edu/helm/heim/latest/), [T2I-Compbench](https://arxiv.org/abs/2307.06350),
> [GenEval](https://arxiv.org/abs/2310.11513).
> out works like [HEIM](https://crfm.stanford.edu/helm/heim/latest/), [T2I-Compbench](https://huggingface.co/papers/2307.06350),
> [GenEval](https://huggingface.co/papers/2310.11513).
Evaluation of generative models like [Stable Diffusion](https://huggingface.co/docs/diffusers/stable_diffusion) is subjective in nature. But as practitioners and researchers, we often have to make careful choices amongst many different possibilities. So, when working with different generative models (like GANs, Diffusion, etc.), how do we choose one over the other?
@@ -122,7 +122,7 @@ In this section, we will walk you through how to evaluate three different diffus
### Text-guided image generation
[CLIP score](https://arxiv.org/abs/2104.08718) measures the compatibility of image-caption pairs. Higher CLIP scores imply higher compatibility 🔼. The CLIP score is a quantitative measurement of the qualitative concept "compatibility". Image-caption pair compatibility can also be thought of as the semantic similarity between the image and the caption. CLIP score was found to have high correlation with human judgement.
[CLIP score](https://huggingface.co/papers/2104.08718) measures the compatibility of image-caption pairs. Higher CLIP scores imply higher compatibility 🔼. The CLIP score is a quantitative measurement of the qualitative concept "compatibility". Image-caption pair compatibility can also be thought of as the semantic similarity between the image and the caption. CLIP score was found to have high correlation with human judgement.
Let's first load a [`StableDiffusionPipeline`]:
@@ -222,7 +222,7 @@ Here is one example:
![edit-instruction](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-instruction.png)
One strategy to evaluate such a model is to measure the consistency of the change between the two images (in [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) space) with the change between the two image captions (as shown in [CLIP-Guided Domain Adaptation of Image Generators](https://arxiv.org/abs/2108.00946)). This is referred to as the "**CLIP directional similarity**".
One strategy to evaluate such a model is to measure the consistency of the change between the two images (in [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) space) with the change between the two image captions (as shown in [CLIP-Guided Domain Adaptation of Image Generators](https://huggingface.co/papers/2108.00946)). This is referred to as the "**CLIP directional similarity**".
- Caption 1 corresponds to the input image (image 1) that is to be edited.
- Caption 2 corresponds to the edited image (image 2). It should reflect the edit instruction.
@@ -433,7 +433,7 @@ Both CLIP score and CLIP direction similarity rely on the CLIP model, which can
### Class-conditioned image generation
Class-conditioned generative models are usually pre-trained on a class-labeled dataset such as [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k). Popular metrics for evaluating these models include Fréchet Inception Distance (FID), Kernel Inception Distance (KID), and Inception Score (IS). In this document, we focus on FID ([Heusel et al.](https://arxiv.org/abs/1706.08500)). We show how to compute it with the [`DiTPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/dit), which uses the [DiT model](https://arxiv.org/abs/2212.09748) under the hood.
Class-conditioned generative models are usually pre-trained on a class-labeled dataset such as [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k). Popular metrics for evaluating these models include Fréchet Inception Distance (FID), Kernel Inception Distance (KID), and Inception Score (IS). In this document, we focus on FID ([Heusel et al.](https://huggingface.co/papers/1706.08500)). We show how to compute it with the [`DiTPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/dit), which uses the [DiT model](https://huggingface.co/papers/2212.09748) under the hood.
FID aims to measure how similar are two datasets of images. As per [this resource](https://mmgeneration.readthedocs.io/en/latest/quick_run.html#fid):

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@@ -37,7 +37,7 @@ Then load and enable the [`DeepCacheSDHelper`](https://github.com/horseee/DeepCa
```
The `set_params` method accepts two arguments: `cache_interval` and `cache_branch_id`. `cache_interval` means the frequency of feature caching, specified as the number of steps between each cache operation. `cache_branch_id` identifies which branch of the network (ordered from the shallowest to the deepest layer) is responsible for executing the caching processes.
Opting for a lower `cache_branch_id` or a larger `cache_interval` can lead to faster inference speed at the expense of reduced image quality (ablation experiments of these two hyperparameters can be found in the [paper](https://arxiv.org/abs/2312.00858)). Once those arguments are set, use the `enable` or `disable` methods to activate or deactivate the `DeepCacheSDHelper`.
Opting for a lower `cache_branch_id` or a larger `cache_interval` can lead to faster inference speed at the expense of reduced image quality (ablation experiments of these two hyperparameters can be found in the [paper](https://huggingface.co/papers/2312.00858)). Once those arguments are set, use the `enable` or `disable` methods to activate or deactivate the `DeepCacheSDHelper`.
<div class="flex justify-center">
<img src="https://github.com/horseee/Diffusion_DeepCache/raw/master/static/images/example.png">

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@@ -2,7 +2,7 @@
[xDiT](https://github.com/xdit-project/xDiT) is an inference engine designed for the large scale parallel deployment of Diffusion Transformers (DiTs). xDiT provides a suite of efficient parallel approaches for Diffusion Models, as well as GPU kernel accelerations.
There are four parallel methods supported in xDiT, including [Unified Sequence Parallelism](https://arxiv.org/abs/2405.07719), [PipeFusion](https://arxiv.org/abs/2405.14430), CFG parallelism and data parallelism. The four parallel methods in xDiT can be configured in a hybrid manner, optimizing communication patterns to best suit the underlying network hardware.
There are four parallel methods supported in xDiT, including [Unified Sequence Parallelism](https://huggingface.co/papers/2405.07719), [PipeFusion](https://huggingface.co/papers/2405.14430), CFG parallelism and data parallelism. The four parallel methods in xDiT can be configured in a hybrid manner, optimizing communication patterns to best suit the underlying network hardware.
Optimization orthogonal to parallelization focuses on accelerating single GPU performance. In addition to utilizing well-known Attention optimization libraries, we leverage compilation acceleration technologies such as torch.compile and onediff.
@@ -116,6 +116,6 @@ More detailed performance metric can be found on our [github page](https://githu
[xDiT-project](https://github.com/xdit-project/xDiT)
[USP: A Unified Sequence Parallelism Approach for Long Context Generative AI](https://arxiv.org/abs/2405.07719)
[USP: A Unified Sequence Parallelism Approach for Long Context Generative AI](https://huggingface.co/papers/2405.07719)
[PipeFusion: Displaced Patch Pipeline Parallelism for Inference of Diffusion Transformer Models](https://arxiv.org/abs/2405.14430)
[PipeFusion: Displaced Patch Pipeline Parallelism for Inference of Diffusion Transformer Models](https://huggingface.co/papers/2405.14430)

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# Reinforcement learning training with DDPO
You can fine-tune Stable Diffusion on a reward function via reinforcement learning with the 🤗 TRL library and 🤗 Diffusers. This is done with the Denoising Diffusion Policy Optimization (DDPO) algorithm introduced by Black et al. in [Training Diffusion Models with Reinforcement Learning](https://arxiv.org/abs/2305.13301), which is implemented in 🤗 TRL with the [`~trl.DDPOTrainer`].
You can fine-tune Stable Diffusion on a reward function via reinforcement learning with the 🤗 TRL library and 🤗 Diffusers. This is done with the Denoising Diffusion Policy Optimization (DDPO) algorithm introduced by Black et al. in [Training Diffusion Models with Reinforcement Learning](https://huggingface.co/papers/2305.13301), which is implemented in 🤗 TRL with the [`~trl.DDPOTrainer`].
For more information, check out the [`~trl.DDPOTrainer`] API reference and the [Finetune Stable Diffusion Models with DDPO via TRL](https://huggingface.co/blog/trl-ddpo) blog post.

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@@ -65,14 +65,14 @@ For convenience, we provide a table to denote which methods are inference-only a
| [Fabric](#fabric) | ✅ | ❌ | |
## InstructPix2Pix
[Paper](https://arxiv.org/abs/2211.09800)
[Paper](https://huggingface.co/papers/2211.09800)
[InstructPix2Pix](../api/pipelines/pix2pix) is fine-tuned from Stable Diffusion to support editing input images. It takes as inputs an image and a prompt describing an edit, and it outputs the edited image.
InstructPix2Pix has been explicitly trained to work well with [InstructGPT](https://openai.com/blog/instruction-following/)-like prompts.
## Pix2Pix Zero
[Paper](https://arxiv.org/abs/2302.03027)
[Paper](https://huggingface.co/papers/2302.03027)
[Pix2Pix Zero](../api/pipelines/pix2pix_zero) allows modifying an image so that one concept or subject is translated to another one while preserving general image semantics.
@@ -104,7 +104,7 @@ apply Pix2Pix Zero to any of the available Stable Diffusion models.
## Attend and Excite
[Paper](https://arxiv.org/abs/2301.13826)
[Paper](https://huggingface.co/papers/2301.13826)
[Attend and Excite](../api/pipelines/attend_and_excite) allows subjects in the prompt to be faithfully represented in the final image.
@@ -114,7 +114,7 @@ Like Pix2Pix Zero, Attend and Excite also involves a mini optimization loop (lea
## Semantic Guidance (SEGA)
[Paper](https://arxiv.org/abs/2301.12247)
[Paper](https://huggingface.co/papers/2301.12247)
[SEGA](../api/pipelines/semantic_stable_diffusion) allows applying or removing one or more concepts from an image. The strength of the concept can also be controlled. I.e. the smile concept can be used to incrementally increase or decrease the smile of a portrait.
@@ -124,7 +124,7 @@ Unlike Pix2Pix Zero or Attend and Excite, SEGA directly interacts with the diffu
## Self-attention Guidance (SAG)
[Paper](https://arxiv.org/abs/2210.00939)
[Paper](https://huggingface.co/papers/2210.00939)
[Self-attention Guidance](../api/pipelines/self_attention_guidance) improves the general quality of images.
@@ -140,7 +140,7 @@ It conditions on a monocular depth estimate of the original image.
## MultiDiffusion Panorama
[Paper](https://arxiv.org/abs/2302.08113)
[Paper](https://huggingface.co/papers/2302.08113)
[MultiDiffusion Panorama](../api/pipelines/panorama) defines a new generation process over a pre-trained diffusion model. This process binds together multiple diffusion generation methods that can be readily applied to generate high quality and diverse images. Results adhere to user-provided controls, such as desired aspect ratio (e.g., panorama), and spatial guiding signals, ranging from tight segmentation masks to bounding boxes.
MultiDiffusion Panorama allows to generate high-quality images at arbitrary aspect ratios (e.g., panoramas).
@@ -157,13 +157,13 @@ In addition to pre-trained models, Diffusers has training scripts for fine-tunin
## Textual Inversion
[Paper](https://arxiv.org/abs/2208.01618)
[Paper](https://huggingface.co/papers/2208.01618)
[Textual Inversion](../training/text_inversion) fine-tunes a model to teach it about a new concept. I.e. a few pictures of a style of artwork can be used to generate images in that style.
## ControlNet
[Paper](https://arxiv.org/abs/2302.05543)
[Paper](https://huggingface.co/papers/2302.05543)
[ControlNet](../api/pipelines/controlnet) is an auxiliary network which adds an extra condition.
There are 8 canonical pre-trained ControlNets trained on different conditionings such as edge detection, scribbles,
@@ -176,7 +176,7 @@ input.
## Custom Diffusion
[Paper](https://arxiv.org/abs/2212.04488)
[Paper](https://huggingface.co/papers/2212.04488)
[Custom Diffusion](../training/custom_diffusion) only fine-tunes the cross-attention maps of a pre-trained
text-to-image diffusion model. It also allows for additionally performing Textual Inversion. It supports
@@ -186,7 +186,7 @@ concept(s) of interest.
## Model Editing
[Paper](https://arxiv.org/abs/2303.08084)
[Paper](https://huggingface.co/papers/2303.08084)
The [text-to-image model editing pipeline](../api/pipelines/model_editing) helps you mitigate some of the incorrect implicit assumptions a pre-trained text-to-image
diffusion model might make about the subjects present in the input prompt. For example, if you prompt Stable Diffusion to generate images for "A pack of roses", the roses in the generated images
@@ -194,14 +194,14 @@ are more likely to be red. This pipeline helps you change that assumption.
## DiffEdit
[Paper](https://arxiv.org/abs/2210.11427)
[Paper](https://huggingface.co/papers/2210.11427)
[DiffEdit](../api/pipelines/diffedit) allows for semantic editing of input images along with
input prompts while preserving the original input images as much as possible.
## T2I-Adapter
[Paper](https://arxiv.org/abs/2302.08453)
[Paper](https://huggingface.co/papers/2302.08453)
[T2I-Adapter](../api/pipelines/stable_diffusion/adapter) is an auxiliary network which adds an extra condition.
There are 8 canonical pre-trained adapters trained on different conditionings such as edge detection, sketch,
@@ -209,7 +209,7 @@ depth maps, and semantic segmentations.
## Fabric
[Paper](https://arxiv.org/abs/2307.10159)
[Paper](https://huggingface.co/papers/2307.10159)
[Fabric](https://github.com/huggingface/diffusers/tree/442017ccc877279bcf24fbe92f92d3d0def191b6/examples/community#stable-diffusion-fabric-pipeline) is a training-free
approach applicable to a wide range of popular diffusion models, which exploits

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@@ -18,7 +18,7 @@ specific language governing permissions and limitations under the License.
> [!TIP] Take a look at GitHub Issue [#841](https://github.com/huggingface/diffusers/issues/841) for more context about why we're adding community pipelines to help everyone easily share their work without being slowed down.
Community pipelines are any [`DiffusionPipeline`] class that are different from the original paper implementation (for example, the [`StableDiffusionControlNetPipeline`] corresponds to the [Text-to-Image Generation with ControlNet Conditioning](https://arxiv.org/abs/2302.05543) paper). They provide additional functionality or extend the original implementation of a pipeline.
Community pipelines are any [`DiffusionPipeline`] class that are different from the original paper implementation (for example, the [`StableDiffusionControlNetPipeline`] corresponds to the [Text-to-Image Generation with ControlNet Conditioning](https://huggingface.co/papers/2302.05543) paper). They provide additional functionality or extend the original implementation of a pipeline.
There are many cool community pipelines like [Marigold Depth Estimation](https://github.com/huggingface/diffusers/tree/main/examples/community#marigold-depth-estimation) or [InstantID](https://github.com/huggingface/diffusers/tree/main/examples/community#instantid-pipeline), and you can find all the official community pipelines [here](https://github.com/huggingface/diffusers/tree/main/examples/community).

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@@ -25,7 +25,7 @@ The major advantages of TCD are:
- Freely change detail level: During inference, the level of detail in the image can be adjusted with a single hyperparameter, *gamma*.
> [!TIP]
> For more technical details of TCD, please refer to the [paper](https://arxiv.org/abs/2402.19159) or official [project page](https://mhh0318.github.io/tcd/)).
> For more technical details of TCD, please refer to the [paper](https://huggingface.co/papers/2402.19159) or official [project page](https://mhh0318.github.io/tcd/).
For large models like SDXL, TCD is trained with [LoRA](https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) to reduce memory usage. This is also useful because you can reuse LoRAs between different finetuned models, as long as they share the same base model, without further training.

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@@ -15,7 +15,7 @@ OmniGen is an image generation model. Unlike existing text-to-image models, Omni
- Minimalist model architecture, consisting of only a VAE and a transformer module, for joint modeling of text and images.
- Support for multimodal inputs. It can process any text-image mixed data as instructions for image generation, rather than relying solely on text.
For more information, please refer to the [paper](https://arxiv.org/pdf/2409.11340).
For more information, please refer to the [paper](https://huggingface.co/papers/2409.11340).
This guide will walk you through using OmniGen for various tasks and use cases.
## Load model checkpoints

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@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
# Stable diffusion XL
Stable Diffusion XL은 Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, Robin Rombach에 의해 [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://arxiv.org/abs/2307.01952)에서 제안되었습니다.
Stable Diffusion XL은 Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, Robin Rombach에 의해 [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://huggingface.co/papers/2307.01952)에서 제안되었습니다.
논문 초록은 다음을 따릅니다:
@@ -125,7 +125,7 @@ image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inferen
refiner를 사용할 때, 쉽게 사용할 수 있습니다
- 1.) base 모델과 refiner을 사용하는데, 이는 *Denoisers의 앙상블*을 위한 첫 번째 제안된 [eDiff-I](https://research.nvidia.com/labs/dir/eDiff-I/)를 사용하거나
- 2.) base 모델을 거친 후 [SDEdit](https://arxiv.org/abs/2108.01073) 방법으로 단순하게 refiner를 실행시킬 수 있습니다.
- 2.) base 모델을 거친 후 [SDEdit](https://huggingface.co/papers/2108.01073) 방법으로 단순하게 refiner를 실행시킬 수 있습니다.
**참고**: SD-XL base와 refiner를 앙상블로 사용하는 아이디어는 커뮤니티 기여자들이 처음으로 제안했으며, 이는 다음과 같은 `diffusers`를 구현하는 데도 도움을 주셨습니다.
- [SytanSD](https://github.com/SytanSD)

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@@ -55,7 +55,7 @@ Diffusers 커뮤니티는 프로젝트의 개발에 다음과 같은 윤리 지
- **배포에서의 안전 유도**
- [**안전한 Stable Diffusion**](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_safe): 이는 필터되지 않은 웹 크롤링 데이터셋으로 훈련된 Stable Diffusion과 같은 모델이 부적절한 변질에 취약한 문제를 완화합니다. 관련 논문: [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://arxiv.org/abs/2211.05105).
- [**안전한 Stable Diffusion**](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_safe): 이는 필터되지 않은 웹 크롤링 데이터셋으로 훈련된 Stable Diffusion과 같은 모델이 부적절한 변질에 취약한 문제를 완화합니다. 관련 논문: [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://huggingface.co/papers/2211.05105).
- [**안전 검사기**](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py): 이미지가 생성된 후에 이미자가 임베딩 공간에서 일련의 하드코딩된 유해 개념의 클래스일 확률을 확인하고 비교합니다. 유해 개념은 역공학을 방지하기 위해 의도적으로 숨겨져 있습니다.

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@@ -111,7 +111,7 @@ images = sd_pipeline(sample_prompts, num_images_per_prompt=1, generator=generato
### 텍스트 안내 이미지 생성[[text-guided-image-generation]]
[CLIP 점수](https://arxiv.org/abs/2104.08718)는 이미지-캡션 쌍의 호환성을 측정합니다. 높은 CLIP 점수는 높은 호환성🔼을 나타냅니다. CLIP 점수는 이미지와 캡션 사이의 의미적 유사성으로 생각할 수도 있습니다. CLIP 점수는 인간 판단과 높은 상관관계를 가지고 있습니다.
[CLIP 점수](https://huggingface.co/papers/2104.08718)는 이미지-캡션 쌍의 호환성을 측정합니다. 높은 CLIP 점수는 높은 호환성🔼을 나타냅니다. CLIP 점수는 이미지와 캡션 사이의 의미적 유사성으로 생각할 수도 있습니다. CLIP 점수는 인간 판단과 높은 상관관계를 가지고 있습니다.
[`StableDiffusionPipeline`]을 일단 로드해봅시다:
@@ -207,7 +207,7 @@ print(f"CLIP Score with v-1-5: {sd_clip_score_1_5}")
![edit-instruction](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-instruction.png)
모델을 평가하는 한 가지 전략은 두 이미지 캡션 간의 변경과([CLIP-Guided Domain Adaptation of Image Generators](https://arxiv.org/abs/2108.00946)에서 보여줍니다) 함께 두 이미지 사이의 변경의 일관성을 측정하는 것입니다 ([CLIP](https://huggingface.co/docs/transformers/model_doc/clip) 공간에서). 이를 "**CLIP 방향성 유사성**"이라고 합니다.
모델을 평가하는 한 가지 전략은 두 이미지 캡션 간의 변경과([CLIP-Guided Domain Adaptation of Image Generators](https://huggingface.co/papers/2108.00946)에서 보여줍니다) 함께 두 이미지 사이의 변경의 일관성을 측정하는 것입니다 ([CLIP](https://huggingface.co/docs/transformers/model_doc/clip) 공간에서). 이를 "**CLIP 방향성 유사성**"이라고 합니다.
- 캡션 1은 편집할 이미지 (이미지 1)에 해당합니다.
- 캡션 2는 편집된 이미지 (이미지 2)에 해당합니다. 편집 지시를 반영해야 합니다.
@@ -417,7 +417,7 @@ CLIP 점수와 CLIP 방향 유사성 모두 CLIP 모델에 의존하기 때문
### 클래스 조건화 이미지 생성[[class-conditioned-image-generation]]
클래스 조건화 생성 모델은 일반적으로 [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k)와 같은 클래스 레이블이 지정된 데이터셋에서 사전 훈련됩니다. 이러한 모델을 평가하는 인기있는 지표에는 Fréchet Inception Distance (FID), Kernel Inception Distance (KID) 및 Inception Score (IS)가 있습니다. 이 문서에서는 FID ([Heusel et al.](https://arxiv.org/abs/1706.08500))에 초점을 맞추고 있습니다. [`DiTPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/dit)을 사용하여 FID를 계산하는 방법을 보여줍니다. 이는 내부적으로 [DiT 모델](https://arxiv.org/abs/2212.09748)을 사용합니다.
클래스 조건화 생성 모델은 일반적으로 [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k)와 같은 클래스 레이블이 지정된 데이터셋에서 사전 훈련됩니다. 이러한 모델을 평가하는 인기있는 지표에는 Fréchet Inception Distance (FID), Kernel Inception Distance (KID) 및 Inception Score (IS)가 있습니다. 이 문서에서는 FID ([Heusel et al.](https://huggingface.co/papers/1706.08500))에 초점을 맞추고 있습니다. [`DiTPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/dit)을 사용하여 FID를 계산하는 방법을 보여줍니다. 이는 내부적으로 [DiT 모델](https://huggingface.co/papers/2212.09748)을 사용합니다.
FID는 두 개의 이미지 데이터셋이 얼마나 유사한지를 측정하는 것을 목표로 합니다. [이 자료](https://mmgeneration.readthedocs.io/en/latest/quick_run.html#fid)에 따르면:

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@@ -373,7 +373,7 @@ with torch.inference_mode():
## Memory-efficient attention
어텐션 블록의 대역폭을 최적화하는 최근 작업으로 GPU 메모리 사용량이 크게 향상되고 향상되었습니다.
@tridao의 가장 최근의 플래시 어텐션: [code](https://github.com/HazyResearch/flash-attention), [paper](https://arxiv.org/pdf/2205.14135.pdf).
@tridao의 가장 최근의 플래시 어텐션: [code](https://github.com/HazyResearch/flash-attention), [paper](https://huggingface.co/papers/2205.14135).
배치 크기 1(프롬프트 1개)의 512x512 크기로 추론을 실행할 때 몇 가지 Nvidia GPU에서 얻은 속도 향상은 다음과 같습니다:

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@@ -12,9 +12,9 @@ specific language governing permissions and limitations under the License.
# Token Merging (토큰 병합)
Token Merging (introduced in [Token Merging: Your ViT But Faster](https://arxiv.org/abs/2210.09461))은 트랜스포머 기반 네트워크의 forward pass에서 중복 토큰이나 패치를 점진적으로 병합하는 방식으로 작동합니다. 이를 통해 기반 네트워크의 추론 지연 시간을 단축할 수 있습니다.
Token Merging (introduced in [Token Merging: Your ViT But Faster](https://huggingface.co/papers/2210.09461))은 트랜스포머 기반 네트워크의 forward pass에서 중복 토큰이나 패치를 점진적으로 병합하는 방식으로 작동합니다. 이를 통해 기반 네트워크의 추론 지연 시간을 단축할 수 있습니다.
Token Merging(ToMe)이 출시된 후, 저자들은 [Fast Stable Diffusion을 위한 토큰 병합](https://arxiv.org/abs/2303.17604)을 발표하여 Stable Diffusion과 더 잘 호환되는 ToMe 버전을 소개했습니다. ToMe를 사용하면 [`DiffusionPipeline`]의 추론 지연 시간을 부드럽게 단축할 수 있습니다. 이 문서에서는 ToMe를 [`StableDiffusionPipeline`]에 적용하는 방법, 예상되는 속도 향상, [`StableDiffusionPipeline`]에서 ToMe를 사용할 때의 질적 측면에 대해 설명합니다.
Token Merging(ToMe)이 출시된 후, 저자들은 [Fast Stable Diffusion을 위한 토큰 병합](https://huggingface.co/papers/2303.17604)을 발표하여 Stable Diffusion과 더 잘 호환되는 ToMe 버전을 소개했습니다. ToMe를 사용하면 [`DiffusionPipeline`]의 추론 지연 시간을 부드럽게 단축할 수 있습니다. 이 문서에서는 ToMe를 [`StableDiffusionPipeline`]에 적용하는 방법, 예상되는 속도 향상, [`StableDiffusionPipeline`]에서 ToMe를 사용할 때의 질적 측면에 대해 설명합니다.
## ToMe 사용하기
@@ -34,7 +34,7 @@ image = pipeline("a photo of an astronaut riding a horse on mars").images[0]
이것이 다입니다!
`tomesd.apply_patch()`는 파이프라인 추론 속도와 생성된 토큰의 품질 사이의 균형을 맞출 수 있도록 [여러 개의 인자](https://github.com/dbolya/tomesd#usage)를 노출합니다. 이러한 인수 중 가장 중요한 것은 `ratio(비율)`입니다. `ratio`은 forward pass 중에 병합될 토큰의 수를 제어합니다. `tomesd`에 대한 자세한 내용은 해당 리포지토리(https://github.com/dbolya/tomesd) 및 [논문](https://arxiv.org/abs/2303.17604)을 참고하시기 바랍니다.
`tomesd.apply_patch()`는 파이프라인 추론 속도와 생성된 토큰의 품질 사이의 균형을 맞출 수 있도록 [여러 개의 인자](https://github.com/dbolya/tomesd#usage)를 노출합니다. 이러한 인수 중 가장 중요한 것은 `ratio(비율)`입니다. `ratio`은 forward pass 중에 병합될 토큰의 수를 제어합니다. `tomesd`에 대한 자세한 내용은 해당 리포지토리(https://github.com/dbolya/tomesd) 및 [논문](https://huggingface.co/papers/2303.17604)을 참고하시기 바랍니다.
## `StableDiffusionPipeline`으로 `tomesd` 벤치마킹하기
@@ -102,11 +102,11 @@ We benchmarked the impact of using `tomesd` on [`StableDiffusionPipeline`] along
## 품질
As reported in [the paper](https://arxiv.org/abs/2303.17604), ToMe can preserve the quality of the generated images to a great extent while speeding up inference. By increasing the `ratio`, it is possible to further speed up inference, but that might come at the cost of a deterioration in the image quality.
As reported in [the paper](https://huggingface.co/papers/2303.17604), ToMe can preserve the quality of the generated images to a great extent while speeding up inference. By increasing the `ratio`, it is possible to further speed up inference, but that might come at the cost of a deterioration in the image quality.
To test the quality of the generated samples using our setup, we sampled a few prompts from the “Parti Prompts” (introduced in [Parti](https://parti.research.google/)) and performed inference with the [`StableDiffusionPipeline`] in the following settings:
[논문](https://arxiv.org/abs/2303.17604)에 보고된 바와 같이, ToMe는 생성된 이미지의 품질을 상당 부분 보존하면서 추론 속도를 높일 수 있습니다. `ratio`을 높이면 추론 속도를 더 높일 수 있지만, 이미지 품질이 저하될 수 있습니다.
[논문](https://huggingface.co/papers/2303.17604)에 보고된 바와 같이, ToMe는 생성된 이미지의 품질을 상당 부분 보존하면서 추론 속도를 높일 수 있습니다. `ratio`을 높이면 추론 속도를 더 높일 수 있지만, 이미지 품질이 저하될 수 있습니다.
해당 설정을 사용하여 생성된 샘플의 품질을 테스트하기 위해, "Parti 프롬프트"([Parti](https://parti.research.google/)에서 소개)에서 몇 가지 프롬프트를 샘플링하고 다음 설정에서 [`StableDiffusionPipeline`]을 사용하여 추론을 수행했습니다:

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# ControlNet
[Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) (ControlNet)은 Lvmin Zhang과 Maneesh Agrawala에 의해 쓰여졌습니다.
[Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) (ControlNet)은 Lvmin Zhang과 Maneesh Agrawala에 의해 쓰여졌습니다.
이 예시는 [원본 ControlNet 리포지토리에서 예시 학습하기](https://github.com/lllyasviel/ControlNet/blob/main/docs/train.md)에 기반합니다. ControlNet은 원들을 채우기 위해 [small synthetic dataset](https://huggingface.co/datasets/fusing/fill50k)을 사용해서 학습됩니다.

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# 커스텀 Diffusion 학습 예제
[커스텀 Diffusion](https://arxiv.org/abs/2212.04488)은 피사체의 이미지 몇 장(4~5장)만 주어지면 Stable Diffusion처럼 text-to-image 모델을 커스터마이징하는 방법입니다.
[커스텀 Diffusion](https://huggingface.co/papers/2212.04488)은 피사체의 이미지 몇 장(4~5장)만 주어지면 Stable Diffusion처럼 text-to-image 모델을 커스터마이징하는 방법입니다.
'train_custom_diffusion.py' 스크립트는 학습 과정을 구현하고 이를 Stable Diffusion에 맞게 조정하는 방법을 보여줍니다.
이 교육 사례는 [Nupur Kumari](https://nupurkmr9.github.io/)가 제공하였습니다. (Custom Diffusion의 저자 중 한명).

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# DreamBooth
[DreamBooth](https://arxiv.org/abs/2208.12242)는 한 주제에 대한 적은 이미지(3~5개)만으로도 stable diffusion과 같이 text-to-image 모델을 개인화할 수 있는 방법입니다. 이를 통해 모델은 다양한 장면, 포즈 및 장면(뷰)에서 피사체에 대해 맥락화(contextualized)된 이미지를 생성할 수 있습니다.
[DreamBooth](https://huggingface.co/papers/2208.12242)는 한 주제에 대한 적은 이미지(3~5개)만으로도 stable diffusion과 같이 text-to-image 모델을 개인화할 수 있는 방법입니다. 이를 통해 모델은 다양한 장면, 포즈 및 장면(뷰)에서 피사체에 대해 맥락화(contextualized)된 이미지를 생성할 수 있습니다.
![프로젝트 블로그에서의 DreamBooth 예시](https://dreambooth.github.io/DreamBooth_files/teaser_static.jpg)
<small>에서의 Dreambooth 예시 <a href="https://dreambooth.github.io">project's blog.</a></small>
@@ -118,7 +118,7 @@ python train_dreambooth_flax.py \
### Prior-preserving(사전 보존) loss를 사용한 파인튜닝
과적합과 language drift를 방지하기 위해 사전 보존이 사용됩니다(관심이 있는 경우 [논문](https://arxiv.org/abs/2208.12242)을 참조하세요). 사전 보존을 위해 동일한 클래스의 다른 이미지를 학습 프로세스의 일부로 사용합니다. 좋은 점은 Stable Diffusion 모델 자체를 사용하여 이러한 이미지를 생성할 수 있다는 것입니다! 학습 스크립트는 생성된 이미지를 우리가 지정한 로컬 경로에 저장합니다.
과적합과 language drift를 방지하기 위해 사전 보존이 사용됩니다(관심이 있는 경우 [논문](https://huggingface.co/papers/2208.12242)을 참조하세요). 사전 보존을 위해 동일한 클래스의 다른 이미지를 학습 프로세스의 일부로 사용합니다. 좋은 점은 Stable Diffusion 모델 자체를 사용하여 이러한 이미지를 생성할 수 있다는 것입니다! 학습 스크립트는 생성된 이미지를 우리가 지정한 로컬 경로에 저장합니다.
저자들에 따르면 사전 보존을 위해 `num_epochs * num_samples`개의 이미지를 생성하는 것이 좋습니다. 200-300개에서 대부분 잘 작동합니다.

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# InstructPix2Pix
[InstructPix2Pix](https://arxiv.org/abs/2211.09800)는 text-conditioned diffusion 모델이 한 이미지에 편집을 따를 수 있도록 파인튜닝하는 방법입니다. 이 방법을 사용하여 파인튜닝된 모델은 다음을 입력으로 사용합니다:
[InstructPix2Pix](https://huggingface.co/papers/2211.09800)는 text-conditioned diffusion 모델이 한 이미지에 편집을 따를 수 있도록 파인튜닝하는 방법입니다. 이 방법을 사용하여 파인튜닝된 모델은 다음을 입력으로 사용합니다:
<p align="center">
<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-instruction.png" alt="instructpix2pix-inputs" width=600/>

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</Tip>
[LoRA(Low-Rank Adaptation of Large Language Models)](https://arxiv.org/abs/2106.09685)는 메모리를 적게 사용하면서 대규모 모델의 학습을 가속화하는 학습 방법입니다. 이는 rank-decomposition weight 행렬 쌍(**업데이트 행렬**이라고 함)을 추가하고 새로 추가된 가중치**만** 학습합니다. 여기에는 몇 가지 장점이 있습니다.
[LoRA(Low-Rank Adaptation of Large Language Models)](https://huggingface.co/papers/2106.09685)는 메모리를 적게 사용하면서 대규모 모델의 학습을 가속화하는 학습 방법입니다. 이는 rank-decomposition weight 행렬 쌍(**업데이트 행렬**이라고 함)을 추가하고 새로 추가된 가중치**만** 학습합니다. 여기에는 몇 가지 장점이 있습니다.
- 이전에 미리 학습된 가중치는 고정된 상태로 유지되므로 모델이 [치명적인 망각](https://www.pnas.org/doi/10.1073/pnas.1611835114) 경향이 없습니다.
- Rank-decomposition 행렬은 원래 모델보다 파라메터 수가 훨씬 적으므로 학습된 LoRA 가중치를 쉽게 끼워넣을 수 있습니다.

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[[open-in-colab]]
[textual-inversion](https://arxiv.org/abs/2208.01618)은 소수의 예시 이미지에서 새로운 콘셉트를 포착하는 기법입니다. 이 기술은 원래 [Latent Diffusion](https://github.com/CompVis/latent-diffusion)에서 시연되었지만, 이후 [Stable Diffusion](https://huggingface.co/docs/diffusers/main/en/conceptual/stable_diffusion)과 같은 유사한 다른 모델에도 적용되었습니다. 학습된 콘셉트는 text-to-image 파이프라인에서 생성된 이미지를 더 잘 제어하는 데 사용할 수 있습니다. 이 모델은 텍스트 인코더의 임베딩 공간에서 새로운 '단어'를 학습하여 개인화된 이미지 생성을 위한 텍스트 프롬프트 내에서 사용됩니다.
[textual-inversion](https://huggingface.co/papers/2208.01618)은 소수의 예시 이미지에서 새로운 콘셉트를 포착하는 기법입니다. 이 기술은 원래 [Latent Diffusion](https://github.com/CompVis/latent-diffusion)에서 시연되었지만, 이후 [Stable Diffusion](https://huggingface.co/docs/diffusers/main/en/conceptual/stable_diffusion)과 같은 유사한 다른 모델에도 적용되었습니다. 학습된 콘셉트는 text-to-image 파이프라인에서 생성된 이미지를 더 잘 제어하는 데 사용할 수 있습니다. 이 모델은 텍스트 인코더의 임베딩 공간에서 새로운 '단어'를 학습하여 개인화된 이미지 생성을 위한 텍스트 프롬프트 내에서 사용됩니다.
![Textual Inversion example](https://textual-inversion.github.io/static/images/editing/colorful_teapot.JPG)
<small>By using just 3-5 images you can teach new concepts to a model such as Stable Diffusion for personalized image generation <a href="https://github.com/rinongal/textual_inversion">(image source)</a>.</small>

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@@ -64,7 +64,7 @@ diffusion 모델 생성을 제어하기 위해 `diffusers`가 지원하는 몇
## Pix2Pix Instruct
[Paper](https://arxiv.org/abs/2211.09800)
[Paper](https://huggingface.co/papers/2211.09800)
[Instruct Pix2Pix](../api/pipelines/stable_diffusion/pix2pix) 는 입력 이미지 편집을 지원하기 위해 stable diffusion에서 미세-조정되었습니다. 이미지와 편집을 설명하는 프롬프트를 입력으로 받아 편집된 이미지를 출력합니다.
Instruct Pix2Pix는 [InstructGPT](https://openai.com/blog/instruction-following/)와 같은 프롬프트와 잘 작동하도록 명시적으로 훈련되었습니다.
@@ -73,7 +73,7 @@ Instruct Pix2Pix는 [InstructGPT](https://openai.com/blog/instruction-following/
## Pix2Pix Zero
[Paper](https://arxiv.org/abs/2302.03027)
[Paper](https://huggingface.co/papers/2302.03027)
[Pix2Pix Zero](../api/pipelines/stable_diffusion/pix2pix_zero)를 사용하면 일반적인 이미지 의미를 유지하면서 한 개념이나 피사체가 다른 개념이나 피사체로 변환되도록 이미지를 수정할 수 있습니다.
@@ -98,7 +98,7 @@ Pix2Pix Zero는 '제로 샷(zero-shot)' 이미지 편집이 가능한 최초의
## Attend and Excite
[Paper](https://arxiv.org/abs/2301.13826)
[Paper](https://huggingface.co/papers/2301.13826)
[Attend and Excite](../api/pipelines/stable_diffusion/attend_and_excite)를 사용하면 프롬프트의 피사체가 최종 이미지에 충실하게 표현되도록 할 수 있습니다.
@@ -110,7 +110,7 @@ Pix2Pix Zero와 마찬가지로 Attend and Excite 역시 파이프라인에 미
## Semantic Guidance (SEGA)
[Paper](https://arxiv.org/abs/2301.12247)
[Paper](https://huggingface.co/papers/2301.12247)
의미유도(SEGA)를 사용하면 이미지에서 하나 이상의 컨셉을 적용하거나 제거할 수 있습니다. 컨셉의 강도도 조절할 수 있습니다. 즉, 스마일 컨셉을 사용하여 인물 사진의 스마일을 점진적으로 늘리거나 줄일 수 있습니다.
@@ -122,7 +122,7 @@ Pix2Pix Zero 또는 Attend and Excite와 달리 SEGA는 명시적인 그라데
## Self-attention Guidance (SAG)
[Paper](https://arxiv.org/abs/2210.00939)
[Paper](https://huggingface.co/papers/2210.00939)
[자기 주의 안내](../api/pipelines/stable_diffusion/self_attention_guidance)는 이미지의 전반적인 품질을 개선합니다.
@@ -150,7 +150,7 @@ InstructPix2Pix와 Pix2Pix Zero와 같은 방법의 중요한 차이점은 전
## MultiDiffusion Panorama
[Paper](https://arxiv.org/abs/2302.08113)
[Paper](https://huggingface.co/papers/2302.08113)
MultiDiffusion은 사전 학습된 diffusion model을 통해 새로운 생성 프로세스를 정의합니다. 이 프로세스는 고품질의 다양한 이미지를 생성하는 데 쉽게 적용할 수 있는 여러 diffusion 생성 방법을 하나로 묶습니다. 결과는 원하는 종횡비(예: 파노라마) 및 타이트한 분할 마스크에서 바운딩 박스에 이르는 공간 안내 신호와 같은 사용자가 제공한 제어를 준수합니다.
[MultiDiffusion 파노라마](../api/pipelines/stable_diffusion/panorama)를 사용하면 임의의 종횡비(예: 파노라마)로 고품질 이미지를 생성할 수 있습니다.
@@ -175,7 +175,7 @@ MultiDiffusion은 사전 학습된 diffusion model을 통해 새로운 생성
## ControlNet
[Paper](https://arxiv.org/abs/2302.05543)
[Paper](https://huggingface.co/papers/2302.05543)
[ControlNet](../api/pipelines/stable_diffusion/controlnet)은 추가 조건을 추가하는 보조 네트워크입니다.
가장자리 감지, 낙서, 깊이 맵, 의미적 세그먼트와 같은 다양한 조건에 대해 훈련된 8개의 표준 사전 훈련된 ControlNet이 있습니다,
@@ -200,7 +200,7 @@ DreamBooth 및 Textual Inversion 마찬가지로, 사용자 지정 확산은 사
## Model Editing
[Paper](https://arxiv.org/abs/2303.08084)
[Paper](https://huggingface.co/papers/2303.08084)
[텍스트-이미지 모델 편집 파이프라인](../api/pipelines/model_editing)을 사용하면 사전학습된 text-to-image diffusion 모델이 입력 프롬프트에 있는 피사체에 대해 내릴 수 있는 잘못된 암시적 가정을 완화하는 데 도움이 됩니다.
예를 들어, 안정적 확산에 "A pack of roses"에 대한 이미지를 생성하라는 메시지를 표시하면 생성된 이미지의 장미는 빨간색일 가능성이 높습니다. 이 파이프라인은 이러한 가정을 변경하는 데 도움이 됩니다.
@@ -209,7 +209,7 @@ DreamBooth 및 Textual Inversion 마찬가지로, 사용자 지정 확산은 사
## DiffEdit
[Paper](https://arxiv.org/abs/2210.11427)
[Paper](https://huggingface.co/papers/2210.11427)
[DiffEdit](../api/pipelines/diffedit)를 사용하면 원본 입력 이미지를 최대한 보존하면서 입력 프롬프트와 함께 입력 이미지의 의미론적 편집이 가능합니다.
@@ -218,7 +218,7 @@ DreamBooth 및 Textual Inversion 마찬가지로, 사용자 지정 확산은 사
## T2I-Adapter
[Paper](https://arxiv.org/abs/2302.08453)
[Paper](https://huggingface.co/papers/2302.08453)
[T2I-어댑터](../api/pipelines/stable_diffusion/adapter)는 추가적인 조건을 추가하는 auxiliary 네트워크입니다.
가장자리 감지, 스케치, depth maps, semantic segmentations와 같은 다양한 조건에 대해 훈련된 8개의 표준 사전훈련된 adapter가 있습니다,

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[[open-in-colab]]
커뮤니티 파이프라인은 논문에 명시된 원래의 구현체와 다른 형태로 구현된 모든 [`DiffusionPipeline`] 클래스를 의미합니다. (예를 들어, [`StableDiffusionControlNetPipeline`]는 ["Text-to-Image Generation with ControlNet Conditioning"](https://arxiv.org/abs/2302.05543) 해당) 이들은 추가 기능을 제공하거나 파이프라인의 원래 구현을 확장합니다.
커뮤니티 파이프라인은 논문에 명시된 원래의 구현체와 다른 형태로 구현된 모든 [`DiffusionPipeline`] 클래스를 의미합니다. (예를 들어, [`StableDiffusionControlNetPipeline`]는 ["Text-to-Image Generation with ControlNet Conditioning"](https://huggingface.co/papers/2302.05543) 해당) 이들은 추가 기능을 제공하거나 파이프라인의 원래 구현을 확장합니다.
[Speech to Image](https://github.com/huggingface/diffusers/tree/main/examples/community#speech-to-image) 또는 [Composable Stable Diffusion](https://github.com/huggingface/diffusers/tree/main/examples/community#composable-stable-diffusion) 과 같은 멋진 커뮤니티 파이프라인이 많이 있으며 [여기에서](https://github.com/huggingface/diffusers/tree/main/examples/community) 모든 공식 커뮤니티 파이프라인을 찾을 수 있습니다.

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</Tip>
이 가이드에서는 unconditional 이미지 생성에 ['DiffusionPipeline']과 [DDPM](https://arxiv.org/abs/2006.11239)을 사용합니다:
이 가이드에서는 unconditional 이미지 생성에 ['DiffusionPipeline']과 [DDPM](https://huggingface.co/papers/2006.11239)을 사용합니다:
```python
>>> from diffusers import DiffusionPipeline

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@@ -56,32 +56,32 @@ specific language governing permissions and limitations under the License.
| 管道 | 论文/仓库 | 任务 |
|---|---|:---:|
| [alt_diffusion](./api/pipelines/alt_diffusion) | [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) | Image-to-Image Text-Guided Generation |
| [alt_diffusion](./api/pipelines/alt_diffusion) | [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://huggingface.co/papers/2211.06679) | Image-to-Image Text-Guided Generation |
| [audio_diffusion](./api/pipelines/audio_diffusion) | [Audio Diffusion](https://github.com/teticio/audio-diffusion.git) | Unconditional Audio Generation |
| [controlnet](./api/pipelines/stable_diffusion/controlnet) | [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) | Image-to-Image Text-Guided Generation |
| [cycle_diffusion](./api/pipelines/cycle_diffusion) | [Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance](https://arxiv.org/abs/2210.05559) | Image-to-Image Text-Guided Generation |
| [controlnet](./api/pipelines/stable_diffusion/controlnet) | [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) | Image-to-Image Text-Guided Generation |
| [cycle_diffusion](./api/pipelines/cycle_diffusion) | [Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance](https://huggingface.co/papers/2210.05559) | Image-to-Image Text-Guided Generation |
| [dance_diffusion](./api/pipelines/dance_diffusion) | [Dance Diffusion](https://github.com/williamberman/diffusers.git) | Unconditional Audio Generation |
| [ddpm](./api/pipelines/ddpm) | [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |
| [ddim](./api/pipelines/ddim) | [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation |
| [ddpm](./api/pipelines/ddpm) | [Denoising Diffusion Probabilistic Models](https://huggingface.co/papers/2006.11239) | Unconditional Image Generation |
| [ddim](./api/pipelines/ddim) | [Denoising Diffusion Implicit Models](https://huggingface.co/papers/2010.02502) | Unconditional Image Generation |
| [if](./if) | [**IF**](./api/pipelines/if) | Image Generation |
| [if_img2img](./if) | [**IF**](./api/pipelines/if) | Image-to-Image Generation |
| [if_inpainting](./if) | [**IF**](./api/pipelines/if) | Image-to-Image Generation |
| [latent_diffusion](./api/pipelines/latent_diffusion) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation |
| [latent_diffusion](./api/pipelines/latent_diffusion) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)| Super Resolution Image-to-Image |
| [latent_diffusion_uncond](./api/pipelines/latent_diffusion_uncond) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) | Unconditional Image Generation |
| [paint_by_example](./api/pipelines/paint_by_example) | [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://arxiv.org/abs/2211.13227) | Image-Guided Image Inpainting |
| [pndm](./api/pipelines/pndm) | [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation |
| [latent_diffusion](./api/pipelines/latent_diffusion) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752)| Text-to-Image Generation |
| [latent_diffusion](./api/pipelines/latent_diffusion) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752)| Super Resolution Image-to-Image |
| [latent_diffusion_uncond](./api/pipelines/latent_diffusion_uncond) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752) | Unconditional Image Generation |
| [paint_by_example](./api/pipelines/paint_by_example) | [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://huggingface.co/papers/2211.13227) | Image-Guided Image Inpainting |
| [pndm](./api/pipelines/pndm) | [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://huggingface.co/papers/2202.09778) | Unconditional Image Generation |
| [score_sde_ve](./api/pipelines/score_sde_ve) | [Score-Based Generative Modeling through Stochastic Differential Equations](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation |
| [score_sde_vp](./api/pipelines/score_sde_vp) | [Score-Based Generative Modeling through Stochastic Differential Equations](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation |
| [semantic_stable_diffusion](./api/pipelines/semantic_stable_diffusion) | [Semantic Guidance](https://arxiv.org/abs/2301.12247) | Text-Guided Generation |
| [semantic_stable_diffusion](./api/pipelines/semantic_stable_diffusion) | [Semantic Guidance](https://huggingface.co/papers/2301.12247) | Text-Guided Generation |
| [stable_diffusion_text2img](./api/pipelines/stable_diffusion/text2img) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation |
| [stable_diffusion_img2img](./api/pipelines/stable_diffusion/img2img) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation |
| [stable_diffusion_inpaint](./api/pipelines/stable_diffusion/inpaint) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting |
| [stable_diffusion_panorama](./api/pipelines/stable_diffusion/panorama) | [MultiDiffusion](https://multidiffusion.github.io/) | Text-to-Panorama Generation |
| [stable_diffusion_pix2pix](./api/pipelines/stable_diffusion/pix2pix) | [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://arxiv.org/abs/2211.09800) | Text-Guided Image Editing|
| [stable_diffusion_pix2pix](./api/pipelines/stable_diffusion/pix2pix) | [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://huggingface.co/papers/2211.09800) | Text-Guided Image Editing|
| [stable_diffusion_pix2pix_zero](./api/pipelines/stable_diffusion/pix2pix_zero) | [Zero-shot Image-to-Image Translation](https://pix2pixzero.github.io/) | Text-Guided Image Editing |
| [stable_diffusion_attend_and_excite](./api/pipelines/stable_diffusion/attend_and_excite) | [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://arxiv.org/abs/2301.13826) | Text-to-Image Generation |
| [stable_diffusion_self_attention_guidance](./api/pipelines/stable_diffusion/self_attention_guidance) | [Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://arxiv.org/abs/2210.00939) | Text-to-Image Generation Unconditional Image Generation |
| [stable_diffusion_attend_and_excite](./api/pipelines/stable_diffusion/attend_and_excite) | [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://huggingface.co/papers/2301.13826) | Text-to-Image Generation |
| [stable_diffusion_self_attention_guidance](./api/pipelines/stable_diffusion/self_attention_guidance) | [Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://huggingface.co/papers/2210.00939) | Text-to-Image Generation Unconditional Image Generation |
| [stable_diffusion_image_variation](./stable_diffusion/image_variation) | [Stable Diffusion Image Variations](https://github.com/LambdaLabsML/lambda-diffusers#stable-diffusion-image-variations) | Image-to-Image Generation |
| [stable_diffusion_latent_upscale](./stable_diffusion/latent_upscale) | [Stable Diffusion Latent Upscaler](https://twitter.com/StabilityAI/status/1590531958815064065) | Text-Guided Super Resolution Image-to-Image |
| [stable_diffusion_model_editing](./api/pipelines/stable_diffusion/model_editing) | [Editing Implicit Assumptions in Text-to-Image Diffusion Models](https://time-diffusion.github.io/) | Text-to-Image Model Editing |
@@ -89,13 +89,13 @@ specific language governing permissions and limitations under the License.
| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Image Inpainting |
| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Depth-Conditional Stable Diffusion](https://github.com/Stability-AI/stablediffusion#depth-conditional-stable-diffusion) | Depth-to-Image Generation |
| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Super Resolution Image-to-Image |
| [stable_diffusion_safe](./api/pipelines/stable_diffusion_safe) | [Safe Stable Diffusion](https://arxiv.org/abs/2211.05105) | Text-Guided Generation |
| [stable_diffusion_safe](./api/pipelines/stable_diffusion_safe) | [Safe Stable Diffusion](https://huggingface.co/papers/2211.05105) | Text-Guided Generation |
| [stable_unclip](./stable_unclip) | Stable unCLIP | Text-to-Image Generation |
| [stable_unclip](./stable_unclip) | Stable unCLIP | Image-to-Image Text-Guided Generation |
| [stochastic_karras_ve](./api/pipelines/stochastic_karras_ve) | [Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation |
| [stochastic_karras_ve](./api/pipelines/stochastic_karras_ve) | [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) | Unconditional Image Generation |
| [text_to_video_sd](./api/pipelines/text_to_video) | [Modelscope's Text-to-video-synthesis Model in Open Domain](https://modelscope.cn/models/damo/text-to-video-synthesis/summary) | Text-to-Video Generation |
| [unclip](./api/pipelines/unclip) | [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://arxiv.org/abs/2204.06125)(implementation by [kakaobrain](https://github.com/kakaobrain/karlo)) | Text-to-Image Generation |
| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Text-to-Image Generation |
| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Image Variations Generation |
| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Dual Image and Text Guided Generation |
| [vq_diffusion](./api/pipelines/vq_diffusion) | [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) | Text-to-Image Generation |
| [unclip](./api/pipelines/unclip) | [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://huggingface.co/papers/2204.06125)(implementation by [kakaobrain](https://github.com/kakaobrain/karlo)) | Text-to-Image Generation |
| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://huggingface.co/papers/2211.08332) | Text-to-Image Generation |
| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://huggingface.co/papers/2211.08332) | Image Variations Generation |
| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://huggingface.co/papers/2211.08332) | Dual Image and Text Guided Generation |
| [vq_diffusion](./api/pipelines/vq_diffusion) | [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://huggingface.co/papers/2111.14822) | Text-to-Image Generation |