Stable and Near-Reversible Diffusion ODE Solvers for Image Editing

2026-05-19 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, extended

Summary

This paper introduces Explicit and Effectively Symmetric (EES) Runge-Kutta methods as stable, near-reversible ODE solvers for inversion-based diffusion model image editing. While algebraically reversible solvers like EDICT, BDIA, BELM, and Rex eliminate inversion error, they often suffer from numerical instability and quality degradation during large semantic or visual edits. The authors demonstrate that this trade-off between exact reversibility and numerical stability manifests as a balance between background preservation and prompt alignment in image editing. EES schemes, combined with vector-field smoothing strategies like Smooth Diffusion, improve edit fidelity and maintain stability under significant edits, largely retaining the background-preservation benefits of reversible solvers. Experiments on the PIE-Bench dataset, using Stable Diffusion v1.5 and SDXL, show EES methods are competitive for small edits and the most robust for large edits, outperforming other (near) reversible solvers.

Key takeaway

For research scientists developing or deploying diffusion-based image editing systems, you should prioritize the numerical stability offered by near-reversible EES solvers, particularly for applications involving substantial image transformations. While exactly reversible methods excel at background preservation for minor edits, EES schemes, especially when paired with vector-field smoothing, provide superior robustness and edit fidelity under challenging, large-deviation scenarios, ensuring more consistent and higher-quality outputs.

Key insights

Near-reversible EES solvers offer superior stability for diffusion image editing, balancing background preservation and prompt alignment.

Principles

• Exact reversibility does not guarantee numerical stability.
• Numerical stability is crucial for large semantic edits.
• Vector-field smoothing improves near-reversible solver performance.

Method

The proposed method combines near-reversible EES Runge-Kutta schemes with vector-field smoothing strategies, applying them to the diffusion probability-flow ODE in a half-logSNR x-parameterization with lambda-uniform discretization.

In practice

• Use EES solvers for robust image editing, especially large edits.
• Apply Smooth Diffusion or NPI to enhance EES performance.
• Consider lambda-uniform discretization for better reversibility.

Topics

• Diffusion Model Inversion
• Near-Reversible ODE Solvers
• EES Runge-Kutta Methods
• Numerical Stability
• Text-Guided Image Editing

Code references

• zituitui/BELM

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.