Title: PROOF: Perturbation-Robust Noise Finetune via Optimal Transport Information Bottleneck for Highly-Correlated Asset Generation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Optimal transport, information bottleneck, asset generation

Abstract: The diffusion model has provided a strong tool for implementing text-to-image (T2I) and image-to-image (I2I) generation. Recently, topology and texture control have been popular explorations. Explicit methods consider high-fidelity controllable editing based on external signals or diffusion feature manipulations. The implicit method naively conducts noise interpolation in manifold space. However, they suffer from low robustness of topology and texture under noise perturbations. In this paper, we first propose a plug-and-play perturbation-robust noise finetune (PROOF) module employed by Stable Diffusion to realize a trade-off between content preservation and controllable diversity for highly correlated asset generation. Information bottleneck (IB) and optimal transport (OT) are capable of producing high-fidelity image variations considering topology and texture alignments, respectively. We derive the closed-form solution of the optimal interpolation weight based on optimal-transported information bottleneck (OTIB), and design the corresponding architecture to fine-tune seed noise or inverse noise with around only 14K trainable parameters and 10 minutes of training.  Comprehensive experiments and ablation studies demonstrate that PROOF provides a powerful unified latent manipulation module to efficiently fine-tune the 2D/3D assets with text or image guidance, based on multiple base model architectures.