diff-sampler

diff-sampler is an open source toolbox for fast sampling of diffusion models, with various model implementations, numerical-based solvers, time schedules and other features. This repository also includes official implementations of the following works:

• A Geometric Perspective on Diffusion Models, https://arxiv.org/abs/2305.19947
• Fast ODE-based Sampling for Diffusion Models in Around 5 Steps, [CVPR 2024], https://arxiv.org/abs/2312.00094
• On the Trajectory Regularity of ODE-based Diffusion Sampling, [ICML 2024], https://arxiv.org/abs/2405.11326

Requirements

• This repository is mainly built upon EDM. To install the required packages, please refer to the EDM codebase.
• This codebase supports the pre-trained diffusion models from EDM, ADM, Consistency models, LDM and Stable Diffusion. Please refer to the corresponding codebases for package installation, if you want to load their pre-trained diffusion models.

Supported ODE Solvers for Diffusion Models

Name	Max Order	Source	Location
Euler	1	Denoising Diffusion Implicit Models	diff-solvers-main
Heun	2	Elucidating the Design Space of Diffusion-Based Generative Models	diff-solvers-main
DPM-Solver-2	2	DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps	diff-solvers-main
AMED-Solver	2	Fast ODE-based Sampling for Diffusion Models in Around 5 Steps	amed-solver-main
DPM-Solver++	3	DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models	diff-solvers-main
UniPC	3	UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models	diff-solvers-main
DEIS	4	Fast Sampling of Diffusion Models with Exponential Integrator	diff-solvers-main
iPNDM	4	Fast Sampling of Diffusion Models with Exponential Integrator	diff-solvers-main
iPNDM_v	4	The variable-step version of the Adams–Bashforth methods	diff-solvers-main
AMED-Plugin	4	Fast ODE-based Sampling for Diffusion Models in Around 5 Steps	amed-solver-main

Pre-trained Diffusion Models

We perform sampling on a variaty of pre-trained diffusion models from different codebases including EDM, ADM, Consistency models, LDM and Stable Diffusion. The tested pre-trained models are listed below:

Codebase	Dataset	Resolusion	Pre-trained Models	Description
EDM	CIFAR10	32	edm-cifar10-32x32-uncond-vp.pkl
EDM	FFHQ	64	edm-ffhq-64x64-uncond-vp.pkl
EDM	ImageNet	64	edm-imagenet-64x64-cond-adm.pkl
Consistency Models	LSUN_bedroom	256	edm_bedroom256_ema.pt	Pixel-space
ADM	ImageNet	256	256x256_diffusion.pt and 256x256_classifier.pt	Classifier-guidance.
LDM	LSUN_bedroom	256	lsun_bedroom.pt and vq-f4 model	Latent-space
Stable Diffusion	MS-COCO	512	stable-diffusion-v1-4	Classifier-free-guidance

FID Statistics

For facilitating the FID evaluation of diffusion models, we provide our FID statistics of various datasets. They are collected on the Internet or made by ourselves with the guidance of the EDM codebase.

Citation

If you find this repository useful, please consider citing the following paper:

@article{zhou2023fast,
  title={Fast ODE-based Sampling for Diffusion Models in Around 5 Steps},
  author={Zhou, Zhenyu and Chen, Defang and Wang, Can and Chen, Chun},
  journal={arXiv preprint arXiv:2312.00094},
  year={2023}
}

@article{chen2024trajectory,
  title={On the Trajectory Regularity of ODE-based Diffusion Sampling},
  author={Chen, Defang and Zhou, Zhenyu and Wang, Can and Shen, Chunhua and Lyu, Siwei},
  journal={arXiv preprint arXiv:2405.11326},
  year={2024}
}