Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
This repository is the official PyTorch implementation of Cold-Diffusion.
Download the CelebA-HQ and AFHQ dataset. Use the following script to create data and use them as path to data for MNIST, Cifar10 and CelebA.
python create_data.py
Denoise
Training
cd denoising-diffusion-pytorch
python celebA_noise_128.py --time_steps 200 --sampling_routine x0_step_down --save_folder <Path to save model> --data_path <Path to data folder>
python AFHQ_noise_128.py --time_steps 200 --sampling_routine x0_step_down --save_folder <Path to save model> --data_path <Path to data folder>
sampling_routine with estimated noise
python celebA_noise_128_test.py --time_steps 200 --sampling_routine ddim --save_folder <Path to save images> --load_path <Path to load model> --data_path <Path to data folder> --test_type test_sample_and_save_for_fid
python AFHQ_noise_128_test.py --time_steps 200 --sampling_routine ddim --save_folder <Path to save images> --load_path <Path to load model> --data_path <Path to data folder> --test_type test_sample_and_save_for_fid
sampling_routine with fixed noise
python celebA_noise_128_test.py --time_steps 200 --sampling_routine x0_step_down --save_folder <Path to save images> --load_path <Path to load model> --data_path <Path to data folder> --test_type test_sample_and_save_for_fid
python AFHQ_noise_128_test.py --time_steps 200 --sampling_routine x0_step_down --save_folder <Path to save images> --load_path <Path to load model> --data_path <Path to data folder> --test_type test_sample_and_save_for_fid
Deblur
cd deblurring-diffusion-pytorch
Transformation
Training
python mnist_train.py --time_steps 20 --blur_size 11 --blur_std 7.0 --blur_routine 'Constant' --sampling_routine x0_step_down --data_path <Path to data folder> --save_folder <Path to save model>
python cifar10_train.py --time_steps 50 --blur_routine 'Special_6_routine' --sampling_routine x0_step_down --data_path <Path to data folder> --save_folder <Path to save model>
python celebA_128.py --time_steps 200 --blur_size 15 --blur_std 0.01 --blur_routine Exponential_reflect --sampling_routine x0_step_down --data_path <Path to data folder> --save_folder <Path to save model>
Testing
python mnist_test.py --time_steps 20 --blur_size 11 --blur_std 7.0 --blur_routine 'Constant' --sampling_routine 'x0_step_down' --save_folder <Path to save results> --data_path <Path to data folder> --test_type test_data
python cifar10_test.py --time_steps 50 --blur_routine 'Special_6_routine' --sampling_routine 'x0_step_down' --save_folder <Path to save results> --data_path <Path to data folder> --test_type test_data
python celebA_128_test.py --time_steps 200 --blur_size 15 --blur_std 0.01 --blur_routine Exponential_reflect --sampling_routine x0_step_down --save_folder <Path to save results> --data_path <Path to data folder> --test_type test_data
Generation
Training
python celebA_128.py --discrete --time_steps 300 --blur_size 27 --blur_std 0.01 --blur_routine Exponential --sampling_routine x0_step_down --data_path <Path to data folder> --save_folder <Path to save models>
python AFHQ_128.py --discrete --time_steps 300 --blur_size 27 --blur_std 0.01 --blur_routine Exponential --sampling_routine x0_step_down --data_path <Path to data folder> --save_folder <Path to save models>
Sampling with Perfect Symmetry
python celebA_128_test.py --gmm_cluster 1 --noise 0.000 --discrete --time_steps 300 --blur_size 27 --blur_std 0.01 --blur_routine Exponential --sampling_routine x0_step_down --save_folder <Path to save results> --load_path <Path to load models> --data_path <Path to data folder> --test_type train_distribution_mean_blur_torch_gmm_ablation
python AFHQ_128_test.py --gmm_cluster 1 --noise 0.000 --discrete --time_steps 300 --blur_size 27 --blur_std 0.01 --blur_routine Exponential --sampling_routine x0_step_down --save_folder <Path to save results> --load_path <Path to load models> --data_path <Path to data folder> --test_type train_distribution_mean_blur_torch_gmm_ablation
Animorph
Generation
Training
cd demixing-diffusion-pytorch
python AFHQ_128_to_celebA_128.py --time_steps 200 --sampling_routine x0_step_down --save_folder <path to save models> --data_path_start <Path to starting data manifold> --data_path_end <Path to ending data manifold>
Sampling
python AFHQ_128_to_celebA_128_test.py --time_steps 200 --sampling_routine x0_step_down --save_folder <Path to save images> --load_path <Path to load model> --data_path_start <Path to starting data manifold> --data_path_end <Path to ending data manifold> --test_type test_sample_and_save_for_fid
Inpaint
Transformation
Training
cd defading-diffusion-pytorch
python mnist_train.py --time_steps 50 --save_folder <path to save models> --discrete --sampling_routine x0_step_down --train_steps 700000 --blur_std 0.1 --fade_routine Random_Incremental --data_path <Path to data folder>
python cifar10_train.py --time_steps 50 --save_folder <path to save models> --discrete --sampling_routine x0_step_down --train_steps 700000 --blur_std 0.1 --fade_routine Random_Incremental --data_path <Path to data folder>
python celebA_train.py --time_steps 100 --fade_routine Incremental --save_folder <path to save models> --sampling_routine x0_step_down --train_steps 350000 --kernel_std 0.2 --initial_mask 1 --image_size 128 --dataset celebA --data_path <Path to data folder>
Testing
python mnist_test.py --time_steps 50 --save_folder test_mnist --discrete --sampling_routine x0_step_down --kernel_std 0.1 --initial_mask 1 --image_size 28 --fade_routine Random_Incremental --load_path <Path to load model> --data_path <Path to data folder> --test_type test_data
python cifar10_test.py --time_steps 50 --save_folder test_cifar10 --discrete --sampling_routine x0_step_down --kernel_std 0.1 --initial_mask 1 --image_size 32 --fade_routine Random_Incremental --load_path <Path to load model> --data_path <Path to data folder> --test_type test_data
python celebA_test.py --time_steps 100 --fade_routine Incremental --save_folder test_celebA --sampling_routine x0_step_down --kernel_std 0.2 --initial_mask 1 --image_size 128 --dataset celebA --load_path <Path to load model> --data_path <Path to data folder> --test_type test_data
Generation
Training
cd defading-generation-diffusion-pytorch
python celebA_128.py --reverse --kernel_std 0.05 --initial_mask 1 --time_steps 750 --sampling_routine x0_step_down --save_folder <Path to save models> --data_path <Path to data folder>
Sampling
python celebA_constant_128_test.py --noise 0 --reverse --kernel_std 0.05 --initial_mask 1 --time_steps 750 --sampling_routine x0_step_down --save_folder <Path to save images> --data_path <Path to data folder> --load_path <Path to load model> --test_type test_sample_and_save_for_fid
Super-Resolution
Training
cd resolution-diffusion-pytorch
python mnist_train.py --time_steps 3 --resolution_routine 'Incremental_factor_2' --save_folder <Path to save models>
python cifar10_train.py --time_steps 3 --resolution_routine 'Incremental_factor_2' --save_folder <Path to save models>
python celebA_128.py --time_steps 4 --resolution_routine 'Incremental_factor_2' --save_folder <Path to save models>
Testing
python mnist_test.py --time_steps 3 --train_routine 'Final' --sampling_routine 'x0_step_down' --resolution_routine 'Incremental_factor_2' --save_folder <Path to save images> --load_path <Path to load model> --test_type test_data
python cifar10_test.py --time_steps 3 --train_routine 'Final' --sampling_routine 'x0_step_down' --resolution_routine 'Incremental_factor_2' --save_folder <Path to save images> --load_path <Path to load model> --test_type test_data
python celebA_test.py --time_steps 4 --train_routine 'Final' --sampling_routine 'x0_step_down' --resolution_routine 'Incremental_factor_2' --save_folder <Path to save images> --load_path <Path to load model> --test_type test_data
Generation
python celebA_test.py --time_steps 4 --train_routine 'Final' --sampling_routine 'x0_step_down' --resolution_routine 'Incremental_factor_2' --save_folder <Path to save images> --load_path <Path to load model> --test_type test_data
Snowify
cd snowification
Training
python train.py --dataset cifar10 --time_steps 200 --forward_process_type ‘Snow’ --snow_level 3 --exp_name <exp_name> --dataset_folder <path-to-dataset> --random_snow --fix_brightness --sampling_routine x0_step_down
python train.py --dataset celebA --time_steps 200 --forward_process_type ‘Snow’ --snow_level 4 --exp_name <exp_name> --dataset_folder <path-to-dataset> --random_snow --fix_brightness --sampling_routine x0_step_down
Testing
python test.py --dataset cifar10 --time_steps 200 --forward_process_type ‘Snow’ --snow_level 3 --exp_name <exp_name> --dataset_folder <path-to-dataset> --random_snow --fix_brightness --resume_training --sampling_routine x0_step_down --test_type test_data --order_seed 1
python test.py --dataset celebA --time_steps 200 --forward_process_type ‘Snow’ --snow_level 4 --exp_name <exp_name> --dataset_folder <path-to-dataset> --random_snow --fix_brightness --resume_training --sampling_routine x0_step_down --test_type test_data --order_seed 1
Colorization
cd decolor-diffusion
Training
python train.py --dataset cifar10 --time_steps 20 --forward_process_type ‘Decolorization’ --exp_name <exp_name> --decolor_total_remove --decolor_routine ‘Linear’ --dataset_folder <path-to-dataset>
python train.py --dataset celebA --time_steps 20 --forward_process_type ‘Decolorization’ --exp_name <exp_name> --decolor_total_remove --decolor_routine ‘Linear’ --dataset_folder <path-to-dataset>
Testing
python test.py --dataset cifar10 --time_steps 20 --forward_process_type ‘Decolorization’ --exp_name <exp-name> --decolor_total_remove --decolor_routine ‘Linear’ --dataset_folder <path-to-dataset> --sampling_routine x0_step_down --test_type test_data --order_seed 1
python test.py --dataset celebA --time_steps 20 --forward_process_type ‘Decolorization’ --exp_name <exp-name> --decolor_total_remove --decolor_routine ‘Linear’ --dataset_folder <path-to-dataset> --sampling_routine x0_step_down --test_type test_data --order_seed 1
Citation
@misc{bansal2022cold,
title={Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise},
author={Arpit Bansal and Eitan Borgnia and Hong-Min Chu and Jie S. Li and Hamid Kazemi and Furong Huang and Micah Goldblum and Jonas Geiping and Tom Goldstein},
year={2022},
eprint={2208.09392},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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