Title: Reversing the DDIM Generation Pipeline: A New Approach and Comprehensive Empirical Analysis

Abstract:

This study addresses the challenge of reversing the Denoising Diffusion Implicit Model (DDIM) generation trajectory to retrieve latent variables, with a specific focus on reconstructing the original noise map from a produced image. Current techniques frequently exhibit limitations regarding precision in this domain. To overcome these hurdles, we present a novel hybrid strategy that employs gradient descent for direct inversion during the initial step, transitioning to a fixed-point method for all subsequent iterations.

We conducted extensive empirical assessments across three distinct datasets, which confirmed that our proposed technique substantially enhances the accuracy of initial latent variable prediction while simultaneously delivering superior reconstruction fidelity. Furthermore, we propose a new metric termed the "self-interpolation test." This evaluation measures the quality of images synthesized from interpolated points situated between the ground-truth and predicted latent maps, thereby providing a more nuanced understanding of model performance.

Our findings indicate a distinct divergence in capabilities: while established methods generally achieve acceptable reconstruction results, they consistently struggle with the accurate prediction of initial latent variables, leading to subpar outcomes in the self-interpolation test. Conversely, our approach surpasses existing solutions across every evaluated metric. These results not only offer significant insights into the mechanics of diffusion models but also expand their potential utility in image generation and editing tasks.

Source: arXiv Generated at: 2026-06-03 00:00:00 UTC