Covariance-Adaptive Sequential Black-box Optimization for Diffusion Targeted Generation

• URL: http://arxiv.org/abs/2406.00812v2
• Date: Sat, 8 Jun 2024 10:28:48 GMT
• Title: Covariance-Adaptive Sequential Black-box Optimization for Diffusion Targeted Generation
• Authors: Yueming Lyu, Kim Yong Tan, Yew Soon Ong, Ivor W. Tsang,
• Abstract summary: We show how to perform user-preferred targeted generation via diffusion models with only black-box target scores of users. Experiments on both numerical test problems and target-guided 3D-molecule generation tasks show the superior performance of our method in achieving better target scores.
• Score: 60.41803046775034
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diffusion models have demonstrated great potential in generating high-quality content for images, natural language, protein domains, etc. However, how to perform user-preferred targeted generation via diffusion models with only black-box target scores of users remains challenging. To address this issue, we first formulate the fine-tuning of the targeted reserve-time stochastic differential equation (SDE) associated with a pre-trained diffusion model as a sequential black-box optimization problem. Furthermore, we propose a novel covariance-adaptive sequential optimization algorithm to optimize cumulative black-box scores under unknown transition dynamics. Theoretically, we prove a $O(\frac{d^2}{\sqrt{T}})$ convergence rate for cumulative convex functions without smooth and strongly convex assumptions. Empirically, experiments on both numerical test problems and target-guided 3D-molecule generation tasks show the superior performance of our method in achieving better target scores.

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