1 citations · 1 across the 5 of their papers we have counts for
6 papers
An Lower Bound for Stochastic NC-SC Bilevel Optimization with First-order Oracles
Zhihao Gu, Qilong Wu, Junchi Yang
We study the oracle complexity of finding -stationary points of smooth bilevel optimization problems with a nonconvex upper-level objective and a strongly convex lower-level pro…
SGHA: A Single-Loop Fully First-Order Algorithm for Nonconvex-Strongly-Convex Bilevel Optimization
Zhihao Gu, Qilong Wu, Junchi Yang
In this work, we study the oracle complexity of finding an -stationary point for nonconvex-strongly-convex (NC-SC) bilevel optimization using only first-order oracles. Existing…
D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models
Dengyang Jiang, Xin Jin, Dongyang Liu +9
The landscape of high-performance image generation models is currently shifting from the inefficient multi-step ones to the efficient few-step counterparts (e.g, Z-Image-Turbo and…
Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shield
Dongyang Liu, Peng Gao, David Liu +8
Diffusion model distillation has emerged as a powerful technique for creating efficient few-step and single-step generators. Among these, Distribution Matching Distillation (DMD) a…
Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
Image Team, Huanqia Cai, Sihan Cao +21
The landscape of high-performance image generation models is currently dominated by proprietary systems, such as Nano Banana Pro and Seedream 4.0. Leading open-source alternatives,…
Distribution Matching Distillation Meets Reinforcement Learning
Dengyang Jiang, Dongyang Liu, Zanyi Wang +12
Distribution Matching Distillation (DMD) facilitates efficient inference by distilling multi-step diffusion models into few-step variants. Concurrently, Reinforcement Learning (RL)…