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Official PyTorch implementation of the NeurIPS 2022 paper "Improving Diffusion Models for Inverse Problems using Manifold Constraints (https://arxiv.org/abs/2206.00941)"
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# Improving Diffusion Models for Inverse Problems using Manifold Constraints (NeurIPS 2022) Official PyTorch implementation of the NeurIPS 2022 paper "[Improving Diffusion Models for Inverse Problems using Manifold Constraints](https://arxiv.org/abs/2206.00941)". Code modified from [guided-diffusion](https://github.com/openai/guided-diffusion) and [score_sde_pytorch](https://github.com/yang-song/score_sde_pytorch). [](https://arxiv.org/abs/2206.00941) [](https://arxiv.org/abs/2206.00941)    For each task, we additionally provide some re-implementations of diffusion model-based inverse problem solvers. > **Improving Diffusion Models for Inverse Problems using Manifold Constraints**<br> > [Hyungjin Chung](https://www.hj-chung.com/), Hyungjin Chung, [Jong Chul Ye](https://bispl.weebly.com/professor.html) <br> > NeurIPS 2022 > >**Abstract**: <br> Recently, diffusion models have been used to solve various inverse problems in an unsupervised manner with appropriate modifications to the sampling process. However, the current solvers, which recursively apply a reverse diffusion step followed by a projection-based measurement consistency step, often produce sub-optimal results. By studying the generative sampling path, here we show that current solvers throw the sample path off the data manifold, and hence the error accumulates. To address this, we propose an additional correction term inspired by the manifold constraint, which can be used synergistically with the previous solvers to make the iterations close to the manifold. The proposed manifold constraint is straightforward to implement within a few lines of code, yet boosts the performance by a surprisingly large margin. With extensive experiments, we show that our method is superior to the previous methods both theoretically and empirically, producing promising results in many applications such as image inpainting, colorization, and sparse-view computed tomography. ## Getting Started ### Setting the environment Our code was tested on the following environment - Ubuntu 20.04 - CUDA 10.2 - PyTorch 1.6.0 We provide the install script in ```install.sh```. Be sure to have conda ready, as we will be using conda to build the environment. Once ready, simply run the following command: ```bash source install.sh ``` The above command will create a conda environment, and install the dependencies listed in ```requirements.txt```. ### Pretrained checkpoints The install script will automatically download the pre-trained checkpoints and place it in the ```checkpoints``` directory. Alternatively, you may download the pre-trained checkpoints used for each task from the links below. | | FFHQ 256x256 | ImageNet 256x256 | LSUN-bedroom | AAPM | Original repository | |--------------|--------------------------------------------------------------------------|-----------------------------------------------------------------------|---------------------------------------------------------------------------|---------------------------------------------------------------------------|----------------------------------------------------------------| | Inpainting | [Link](https://www.dropbox.com/s/4r8r6o2n1pumzmg/ffhq_10m.pt?dl=1) | [Link](https://www.dropbox.com/s/rtit2qsb353262t/imagenet256.pt?dl=0) | [Link](https://www.dropbox.com/s/57bguxpr6by6l1x/lsun_bedroom.pt?dl=1) | - | [Guided di
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matched fp:9dec909a8baae5f0, llm:Repository description: 'Official PyTorch implementation of the NeurIPS 2022 paper "Improving Diffusion Models for Inverse Problems using Manifold Constraints"'
matched fp:9dec909a8baae5f0, llm:Repository description: 'Official PyTorch implementation of the NeurIPS 2022 paper "Improving Diffusion Models for Inverse Problems using Manifold Constraints"'
matched fp:9dec909a8baae5f0, llm:Repository description: 'Official PyTorch implementation of the NeurIPS 2022 paper "Improving Diffusion Models for Inverse Problems using Manifold Constraints"'