Inverting the Generation Process of Denoising Diffusion Implicit Models: Empirical Evaluation and a Novel Method
This work provides a more accurate inversion method for DDIM, benefiting applications in image generation and editing that require precise latent variable recovery.
The paper addresses the problem of inverting the DDIM image generation process to recover initial noise maps from generated images. The proposed hybrid method combining gradient descent and fixed-point iteration significantly improves prediction accuracy and reconstruction quality across three datasets.
This paper studies the problem of inverting the DDIM image generation process to recover latent variables, particularly the initial noise map, from a generated image. Existing methods often struggle with accuracy in this task. We propose a novel hybrid approach that combines direct inversion via gradient descent for the first step, followed by a fixed-point method for subsequent steps. Empirical evaluations across three datasets demonstrate that our method significantly improves the prediction of initial latent variables while achieving superior reconstruction accuracy. Additionally, we introduce a new evaluation, called the self-interpolation test, which assesses the quality of images generated from interpolated points between the true and predicted latent maps, offering deeper insights into performance. Our results reveal that while existing methods perform reasonably well in reconstruction, they consistently fail to accurately predict the initial latent variables, resulting in poor performance on the self-interpolation test. In contrast, our method outperforms all others across all metrics, providing valuable insights into diffusion models and enhancing their applications in image generation and editing.