Learning to Integrate Diffusion ODEs by Averaging the Derivatives
Paper • 2505.14502 • Published
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("poppuppy/secant-expectation", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]This repository contains the official implementation of the paper: Learning to Integrate Diffusion ODEs by Averaging the Derivatives.
Code: https://github.com/poppuppy/secant_expectation
About
This work introduces an intermediate strategy for accelerating diffusion model inference by learning ODE integration. It uses loss functions derived from the derivative-integral relationship, inspired by Monte Carlo integration and Picard iteration. Referred to as "secant losses," this approach balances performance and cost, leading to great training stability. The secant version of EDM achieves a 10-step FID of 2.14 on CIFAR-10, while the secant version of SiT-XL/2 attains a 4-step FID of 2.27 and an 8-step FID of 1.96 on ImageNet-256x256.
Installation
For installation instructions and how to train and evaluate the models, please refer to the GitHub repository.
Checkpoints
Trained checkpoints for different settings (SDEI, STEE, SDEE, STEE, various step counts) are provided on Hugging Face.
Citation
If you find this work useful in your research, please consider citing:
@article{liu2025learning,
title={Learning to Integrate Diffusion ODEs by Averaging the Derivatives},
author={Liu, Wenze and Yue, Xiangyu},
journal={arXiv preprint arXiv:2505.14502},
year={2025}
}
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