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CVPR 2024: Residual Denoising Diffusion Models
| Date | Stars |
|---|---|
| 2026-07-31 | 579 |
| 2026-08-03 | 579 |
| 2026-08-06 | 579 |
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# Residual Denoising Diffusion Models [paper](https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Residual_Denoising_Diffusion_Models_CVPR_2024_paper.html)|[arxiv](https://arxiv.org/abs/2308.13712)|[youtube](https://www.youtube.com/watch?v=E-ObZs32fEU)|[blog](https://twitter.com/nachifur/status/1762730191707881537)|[中文论文(ao9l)](https://rec.ustc.edu.cn/share/60cb4770-1b6a-11ef-8e9e-332aeb6c199a)|[中文视频](https://cmdr.com.cn/lectureHall/lectureRoomDetail?liveUid=58e63bb51116d7c01f37dfee1354b043)|[中文博客](https://www.zhihu.com/question/645935461/answer/3410873004) This repository is the official implementation of Residual Denoising Diffusion Models. <p align="center"> <a href="https://cvpr.thecvf.com/virtual/2024/poster/31373" target="_blank"> <img width="800" height="400" img align="center" alt="RDDM" src="https://github.com/nachifur/RDDM/blob/main/poster/Jiawei_9969.png" /> </a> </p> ## Requirements To install requirements: ([If an error occurs, you may need to install the packages one by one](https://github.com/nachifur/RDDM/issues/41#issuecomment-2477808693).) ``` conda env create -f install.yaml ``` ## Dataset [Raindrop](https://github.com/rui1996/DeRaindrop) ([test-a for test](https://github.com/rui1996/DeRaindrop)) [GoPro](https://github.com/swz30/MPRNet/blob/main/Deblurring/Datasets/README.md) [ISTD](https://github.com/DeepInsight-PCALab/ST-CGAN) SID-RGB: [kexu](https://kkbless.github.io/) or [download](https://drive.google.com/drive/folders/1-psXDjeW4FiRdLjc9idABsxGPo1Kn1jR) [LOL](https://daooshee.github.io/BMVC2018website/) [CelebA](https://github.com/nachifur/RDDM/issues/8#issuecomment-1978889073) ## Training To train RDDM, run this command: ```train cd experiments/xxxx python train.py ``` or ```train accelerate launch train.py ``` ## Evaluation To evaluate image generation, run: ```eval cd eval/image_generation_eval/ python fid_and_inception_score.py path_of_gen_img ``` For image restoration, MATLAB evaluation codes in `./eval`. ## Pre-trained Models [The pre-trained models (two unets, deresidual+denoising)](https://rec.ustc.edu.cn/share/3d8d9200-4e7e-11ef-b0ee-250e7e41f368) for [partially path-independent generation process](https://github.com/nachifur/RDDM/tree/main/experiments/0_Partially_path-independent_generation). ## Results See Table 3 in main paper. **For image restoration:** [Raindrop](https://rec.ustc.edu.cn/share/c20ea640-4e7e-11ef-b29e-b1b12149494a) [GoPro](https://rec.ustc.edu.cn/share/f9deffc0-4e7e-11ef-b4dd-b51790f24839) [ISTD](https://rec.ustc.edu.cn/share/da867b10-4e7e-11ef-b21d-b3131e611f52) [LOL](https://rec.ustc.edu.cn/share/e9c00ab0-4e7e-11ef-89a0-292c4c37c153) [SID-RGB](https://rec.ustc.edu.cn/share/b213c330-4e7e-11ef-9b3e-957f50ca7d9b) **For image generation (on the CelebA dataset):** We can convert a pre-trained DDIM to RDDM by coefficient transformation (see [1_Image_Generation_convert_pretrained_DDIM_to_RDDM](https://github.com/nachifur/RDDM/tree/main/experiments/1_Image_Generation_convert_pretrained_DDIM_to_RDDM)). ## Experiments https://github.com/nachifur/RDDM/tree/main/experiments [0_**Partially_path-independent**_generation](https://github.com/nachifur/RDDM/tree/main/experiments/0_Partially_path-independent_generation) [1_Image_Generation_convert_pretrained_**DDIM_to_RDDM**](1_Image_Generation_convert_pretrained_DDIM_to_RDDM) [2_**Image_Restoration**_deraing_raindrop_noise1](https://github.com/nachifur/RDDM/tree/main/experiments/2_Image_Restoration_deraing_raindrop_noise1) [3_**Automatic-Objective-Selection-Algorithm**_test_lol_timestep5_1e-2_reinit_weight](https://github.com/nachifur/RDDM/tree/main/experiments/3_AOSA_test_lol_timestep5_1e-2_reinit_weight) [4_**Image_Inpainting**_imgsize64_batch64_pred_res_noise_centermask_wo_mask_wo_input](https://github.com/nachifur/RDDM/tree/main/experiments/4_Image_Inpainting_imgsize64_batch64_pred_res_noise_centermask_wo_mask_wo_input) [5_**Image_translation**_dog_to_cat_wo_input_imgsize64_batch64_pred_res_
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matched fp:6ec8e7122288c53c, llm:Repository description: 'CVPR 2024: Residual Denoising Diffusion Models' (Python). Likely implementation of diffusion models for image generation/denoising from CVPR paper.
matched fp:6ec8e7122288c53c, llm:Repository description: 'CVPR 2024: Residual Denoising Diffusion Models' (Python). Likely implementation of diffusion models for image generation/denoising from CVPR paper.
matched fp:6ec8e7122288c53c, llm:Repository description: 'CVPR 2024: Residual Denoising Diffusion Models' (Python). Likely implementation of diffusion models for image generation/denoising from CVPR paper.