EasyInv: Toward Fast and Better DDIM Inversion

2024-08-09

arXiv.org

AI Summary

This work addresses the inefficiency and poor robustness of iterative noise optimization in DDIM-based image inversion. We propose EasyInv, an efficient latent-state-driven inversion method. Its core innovation is a latent-state temporal weighted aggregation mechanism that explicitly amplifies the information weight of the initial latent variable, thereby completely eliminating the need for iterative noise optimization. EasyInv is implemented with only four lines of code and integrates seamlessly into the standard DDIM framework without architectural modifications. Extensive experiments demonstrate that EasyInv achieves inversion quality comparable to or better than conventional DDIM inversion, while accelerating inference by approximately 3×. Moreover, it exhibits exceptional robustness under low-precision models and resource-constrained settings—outperforming existing methods in stability and efficiency.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

This paper introduces EasyInv, an easy yet novel approach that significantly advances the field of DDIM Inversion by addressing the inherent inefficiencies and performance limitations of traditional iterative optimization methods. At the core of our EasyInv is a refined strategy for approximating inversion noise, which is pivotal for enhancing the accuracy and reliability of the inversion process. By prioritizing the initial latent state, which encapsulates rich information about the original images, EasyInv steers clear of the iterative refinement of noise items. Instead, we introduce a methodical aggregation of the latent state from the preceding time step with the current state, effectively increasing the influence of the initial latent state and mitigating the impact of noise. We illustrate that EasyInv is capable of delivering results that are either on par with or exceed those of the conventional DDIM Inversion approach, especially under conditions where the model's precision is limited or computational resources are scarce. Concurrently, our EasyInv offers an approximate threefold enhancement regarding inference efficiency over off-the-shelf iterative optimization techniques. It can be easily combined with most existing inversion methods by only four lines of code. See code at https://github.com/potato-kitty/EasyInv.

Problem

• Improves DDIM Inversion efficiency and performance
• Enhances inversion accuracy via refined noise approximation
• Boosts inference speed 3x over iterative methods

Innovation

• Refined strategy for approximating inversion noise
• Methodical aggregation of latent state steps
• Threefold enhancement in inference efficiency