4 papers
Aligning Few-Step Generative Models by Amortizing Sample-based Variational Inference
Jaewoo Lee, Hyeongyu Kang, Dohyun Kim +9
Aligning a few-step generative model is challenging, since existing alignment frameworks typically rely on restrictive assumptions: a tractable likelihood, a specific ODE/SDE solve…
Posterior Inference in Latent Space for Scalable Constrained Black-box Optimization
Kiyoung Om, Kyuil Sim, Taeyoung Yun +2
Optimizing high-dimensional black-box functions under black-box constraints is a pervasive task in a wide range of scientific and engineering problems. These problems are typically…
Diffusion Fine-Tuning via Reparameterized Policy Gradient of the Soft Q-Function
Hyeongyu Kang, Jaewoo Lee, Woocheol Shin +2
Diffusion models excel at generating high-likelihood samples but often require alignment with downstream objectives. Existing fine-tuning methods for diffusion models significantly…
Diffusion Alignment as Variational Expectation-Maximization
Jaewoo Lee, Minsu Kim, Sanghyeok Choi +7
Diffusion alignment aims to optimize diffusion models for the downstream objective. While existing methods based on reinforcement learning or direct backpropagation achieve conside…