## Prompt-Diffusion: In-Context Learning Unlocked for Diffusion Models
[Project Page](https://zhendong-wang.github.io/prompt-diffusion.github.io/) | [Paper](https://arxiv.org/abs/2305.01115) | [GitHub](https://github.com/Zhendong-Wang/Prompt-Diffusion)
![Illustration](./assets/teaser_img.png)

**In-Context Learning Unlocked for Diffusion Models**<br>
Zhendong Wang, Yifan Jiang, Yadong Lu, Yelong Shen, Pengcheng He, Weizhu Chen, Zhangyang Wang and Mingyuan Zhou <br>

[//]: # (https://arxiv.org/abs/2206.02262 <br>)

Abstract: *We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. 
Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance,
our model automatically understands the underlying task and performs the same task on a new query image following the text guidance.
To achieve this, we propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input.
The diffusion model is trained jointly on six different tasks using these prompts. 
The resulting Prompt Diffusion model becomes the first diffusion-based vision-language foundation model capable of in-context learning. 
It demonstrates high-quality in-context generation for the trained tasks and effectively generalizes to new, unseen vision tasks using their respective prompts.
Our model also shows compelling text-guided image editing results. Our framework aims to facilitate research into in-context learning for computer vision, with code publicly available here.*

![Illustration](./assets/illustration.png)

## Note
We have made our pretrained model checkpoints available here. For more information on how to use them, please visit our GitHub page at https://github.com/Zhendong-Wang/Prompt-Diffusion. 


## Citation


```
@article{wang2023promptdiffusion,
  title     = {In-Context Learning Unlocked for Diffusion Models},
  author    = {Wang, Zhendong and Jiang, Yifan and Lu, Yadong and Shen, Yelong and He, Pengcheng and Chen, Weizhu and Wang, Zhangyang and Zhou, Mingyuan},
  journal   = {arXiv preprint arXiv:2305.01115},
  year      = {2023},
  url       = {https://arxiv.org/abs/2305.01115}
}
```

## Acknowledgements
We thank [Brooks et al.](https://github.com/timothybrooks/instruct-pix2pix) for sharing the dataset for finetuning Stable Diffusion. 
We also thank [Lvmin Zhang and Maneesh Agrawala
](https://github.com/lllyasviel/ControlNet) for providing the awesome code base ControlNet. 
{}
arxiv:2305.01115 arxiv:2206.02262 region:us
TAGS
#arxiv-2305.01115 #arxiv-2206.02262 #region-us 

## Prompt-Diffusion: In-Context Learning Unlocked for Diffusion Models
Project Page | Paper | GitHub
!Illustration

In-Context Learning Unlocked for Diffusion Models<br>
Zhendong Wang, Yifan Jiang, Yadong Lu, Yelong Shen, Pengcheng He, Weizhu Chen, Zhangyang Wang and Mingyuan Zhou <br>

[//]: # (URL <br>)

Abstract: *We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. 
Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance,
our model automatically understands the underlying task and performs the same task on a new query image following the text guidance.
To achieve this, we propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input.
The diffusion model is trained jointly on six different tasks using these prompts. 
The resulting Prompt Diffusion model becomes the first diffusion-based vision-language foundation model capable of in-context learning. 
It demonstrates high-quality in-context generation for the trained tasks and effectively generalizes to new, unseen vision tasks using their respective prompts.
Our model also shows compelling text-guided image editing results. Our framework aims to facilitate research into in-context learning for computer vision, with code publicly available here.*

!Illustration

## Note
We have made our pretrained model checkpoints available here. For more information on how to use them, please visit our GitHub page at URL 


## Acknowledgements
We thank Brooks et al. for sharing the dataset for finetuning Stable Diffusion. 
We also thank Lvmin Zhang and Maneesh Agrawala
 for providing the awesome code base ControlNet. 
## Prompt-Diffusion: In-Context Learning Unlocked for Diffusion Models
Project Page | Paper | GitHub
!Illustration

In-Context Learning Unlocked for Diffusion Models<br>
Zhendong Wang, Yifan Jiang, Yadong Lu, Yelong Shen, Pengcheng He, Weizhu Chen, Zhangyang Wang and Mingyuan Zhou <br>

[//]: # (URL <br>)

Abstract: *We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. 
Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance,
our model automatically understands the underlying task and performs the same task on a new query image following the text guidance.
To achieve this, we propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input.
The diffusion model is trained jointly on six different tasks using these prompts. 
The resulting Prompt Diffusion model becomes the first diffusion-based vision-language foundation model capable of in-context learning. 
It demonstrates high-quality in-context generation for the trained tasks and effectively generalizes to new, unseen vision tasks using their respective prompts.
Our model also shows compelling text-guided image editing results. Our framework aims to facilitate research into in-context learning for computer vision, with code publicly available here.*

!Illustration ## Note
We have made our pretrained model checkpoints available here. For more information on how to use them, please visit our GitHub page at URL ## Acknowledgements
We thank Brooks et al. for sharing the dataset for finetuning Stable Diffusion. 
We also thank Lvmin Zhang and Maneesh Agrawala
 for providing the awesome code base ControlNet.
TAGS
#arxiv-2305.01115 #arxiv-2206.02262 #region-us 
 ## Prompt-Diffusion: In-Context Learning Unlocked for Diffusion Models
Project Page | Paper | GitHub
!Illustration

In-Context Learning Unlocked for Diffusion Models<br>
Zhendong Wang, Yifan Jiang, Yadong Lu, Yelong Shen, Pengcheng He, Weizhu Chen, Zhangyang Wang and Mingyuan Zhou <br>

[//]: # (URL <br>)

Abstract: *We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. 
Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance,
our model automatically understands the underlying task and performs the same task on a new query image following the text guidance.
To achieve this, we propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input.
The diffusion model is trained jointly on six different tasks using these prompts. 
The resulting Prompt Diffusion model becomes the first diffusion-based vision-language foundation model capable of in-context learning. 
It demonstrates high-quality in-context generation for the trained tasks and effectively generalizes to new, unseen vision tasks using their respective prompts.
Our model also shows compelling text-guided image editing results. Our framework aims to facilitate research into in-context learning for computer vision, with code publicly available here.*

!Illustration ## Note
We have made our pretrained model checkpoints available here. For more information on how to use them, please visit our GitHub page at URL ## Acknowledgements
We thank Brooks et al. for sharing the dataset for finetuning Stable Diffusion. 
We also thank Lvmin Zhang and Maneesh Agrawala
 for providing the awesome code base ControlNet.