https://github.com/eliahuhorwitz/conffusion

Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.
https://github.com/eliahuhorwitz/conffusion

computer-vision conformal-prediction deep-learning diffusion diffusion-models generative-model image-generation image-to-image inpainting prediction-intervals pytorch quantile-regression super-resolution uncertainty-estimation uncertainty-quantification

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Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.

• Host: GitHub
• URL: https://github.com/eliahuhorwitz/conffusion
• Owner: eliahuhorwitz
• Created: 2022-11-17T08:38:22.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2022-11-27T19:11:26.000Z (almost 2 years ago)
• Last Synced: 2023-11-07T17:26:09.244Z (about 1 year ago)
• Topics: computer-vision, conformal-prediction, deep-learning, diffusion, diffusion-models, generative-model, image-generation, image-to-image, inpainting, prediction-intervals, pytorch, quantile-regression, super-resolution, uncertainty-estimation, uncertainty-quantification
• Language: Python
• Homepage: https://www.vision.huji.ac.il/conffusion/
• Size: 2.14 MB
• Stars: 119
• Watchers: 5
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Con*ffusion*: Confidence Intervals for Diffusion Models

### Project | Paper 


Official PyTorch Implementation for the "Con*ffusion*: Confidence Intervals for Diffusion Models" paper.  

![](imgs/header.gif)

Con*ffusion*: Given a corrupted input image, our method Con*ffusion*, repurposes a pretrained diffusion model to generate lower and upper bounds around each reconstructed pixel. The true pixel value is guaranteed to fall within these bounds with probability p. We present the bounds for inpainting (the context is dimmed for visualization) and super-resolution. Tighter intervals provide more information; we visualize the normalized interval size, darker values are tighter intervals

___

> **Con*ffusion*: Confidence Intervals for Diffusion Models**


> Eliahu Horwitz, Yedid Hoshen


> http://arxiv.org/abs/2211.09795  


>

>**Abstract:** Diffusion models have become the go-to method for many generative tasks, particularly for image-to-image generation tasks such as super-resolution and inpainting. 

> Current diffusion-based methods do not provide statistical guarantees regarding the generated results, often preventing their use in high-stakes situations. 

> To bridge this gap, we construct a confidence interval around each generated pixel such that the true value of the pixel is guaranteed to fall within the interval with a probability set by the user. 

> Since diffusion models parametrize the data distribution, a straightforward way of constructing such intervals is by drawing multiple samples and calculating their bounds. 

> However, this method has several drawbacks: i) slow sampling speeds ii) suboptimal bounds iii) requires training a diffusion model per task. 

> To mitigate these shortcomings we propose Con*ffusion*, wherein we fine-tune a pre-trained diffusion model to predict interval bounds in a single forward pass. 

> We show that Con*ffusion* outperforms the baseline method while being three orders of magnitude faster.

## Getting Started

### Setup 

1.  Clone the repo:

```bash

git clone https://github.com/eliahuhorwitz/Conffusion.git

cd Conffusion

```

2. Create a new environment and install the libraries:

```bash

python3 -m venv conffusion_venv

source conffusion_venv/bin/activate

pip install -r requirements.txt

```

3. Download the data from the here and extract it into a dir named `datasets` 

4. Run the desired model by following the links below:

### N-Con*ffusion*

> [**Super-resolution**](https://github.com/eliahuhorwitz/Conffusion/tree/main/super_resolution_n_conffusion): N-Con*ffusion* code for super-resolution (also includes DMSB and DMSBA code)

> [**Inpainting**](https://github.com/eliahuhorwitz/Conffusion/tree/main/inpainting_n_conffusion): N-Con*ffusion* code for inpainting (also includes DMSB and DMSBA code)

### G-Con*ffusion*

> [**Super-resolution + Inpainting**](https://github.com/eliahuhorwitz/Conffusion/tree/main/g_conffusion): G-Con*ffusion* code for super-resolution and inpainting

## Citation

If you find this useful for your research, please use the following.

```

@article{horwitz2022conffusion,

  title={Conffusion: Confidence Intervals for Diffusion Models},

  author={Horwitz, Eliahu and Hoshen, Yedid},

  journal={arXiv preprint arXiv:2211.09795},

  year={2022}

}

```

## Acknowledgments

- The SR3 implementation is based on this unofficial implementation of SR3. 

- The Palette implementation is based on this unofficial implementation of Palette.

- The Guided-diffusion implementation is based on this official implementation.

- The implementation of the calibration and evaluation metrics is based on this official implementation of im2im-uq.