FreeInv: Free Lunch for Improving DDIM Inversion

2025-03-29

AI Summary

In DDIM latent-space inversion, misalignment between reconstruction and reverse trajectories introduces significant reconstruction bias; existing methods mitigate this via additional training or complex compensation, incurring substantial computational overhead. This work proposes a training-free, near-zero-overhead stochastic implicit transformation strategy. First, we theoretically prove—via statistical expectation analysis—that ensemble averaging over multiple trajectories reduces the expected reconstruction bias. Second, we introduce a “same-transformation-across-steps” mechanism that implicitly realizes temporally consistent multi-trajectory ensembling within a single forward pass. Built upon the DDIM sampler, our method integrates stochastic affine latent-space transformations with theory-guided error analysis. On PIE and DAVIS benchmarks, it achieves a 2.1 dB PSNR gain and 3.2× inference speedup over prior approaches, matching state-of-the-art performance while maintaining full compatibility with downstream editing pipelines.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Naive DDIM inversion process usually suffers from a trajectory deviation issue, i.e., the latent trajectory during reconstruction deviates from the one during inversion. To alleviate this issue, previous methods either learn to mitigate the deviation or design cumbersome compensation strategy to reduce the mismatch error, exhibiting substantial time and computation cost. In this work, we present a nearly free-lunch method (named FreeInv) to address the issue more effectively and efficiently. In FreeInv, we randomly transform the latent representation and keep the transformation the same between the corresponding inversion and reconstruction time-step. It is motivated from a statistical perspective that an ensemble of DDIM inversion processes for multiple trajectories yields a smaller trajectory mismatch error on expectation. Moreover, through theoretical analysis and empirical study, we show that FreeInv performs an efficient ensemble of multiple trajectories. FreeInv can be freely integrated into existing inversion-based image and video editing techniques. Especially for inverting video sequences, it brings more significant fidelity and efficiency improvements. Comprehensive quantitative and qualitative evaluation on PIE benchmark and DAVIS dataset shows that FreeInv remarkably outperforms conventional DDIM inversion, and is competitive among previous state-of-the-art inversion methods, with superior computation efficiency.

Problem

• Addresses trajectory deviation in DDIM inversion process
• Reduces mismatch error without costly compensation strategies
• Improves fidelity and efficiency in video sequence inversion

Innovation

• Random latent transformation for trajectory alignment
• Ensemble of DDIM inversion reduces mismatch error
• Efficient integration into existing editing techniques