7 papers
Poly-OPD: Heterogeneous Multi-Teacher On-Policy Distillation for Capability-Selectable Flow Models
Siming Fu, Haojun Xu, Ruizhe He +9
Leading open text-to-image models often carry complementary strengths: one may lead on preference-aligned aesthetics while another follows compositional instructions more faithfull…
Drift-AR: Single-Step Visual Autoregressive Generation via Anti-Symmetric Drifting
Zhen Zou, Xiaoxiao Ma, Mingde Yao +3
Autoregressive (AR)-Diffusion hybrid paradigms combine AR's structured semantic modeling with diffusion's high-fidelity synthesis, yet suffer from a dual speed bottleneck: the sequ…
Highly Efficient Test-Time Scaling for T2I Diffusion Models with Text Embedding Perturbation
Hang Xu, Linjiang Huang, Feng Zhao
Test-time scaling (TTS) aims to achieve better results by increasing random sampling and evaluating samples based on rules and metrics. However, in text-to-image(T2I) diffusion mod…
FR-TTS: Test-Time Scaling for NTP-based Image Generation with Effective Filling-based Reward Signal
Hang Xu, Linjiang Huang, Feng Zhao
Test-time scaling (TTS) has become a prevalent technique in image generation, significantly boosting output quality by expanding the number of parallel samples and filtering them u…
InfoScale: Unleashing Training-free Variable-scaled Image Generation via Effective Utilization of Information
Guohui Zhang, Jiangtong Tan, Linjiang Huang +4
Diffusion models (DMs) have become dominant in visual generation but suffer performance drop when tested on resolutions that differ from the training scale, whether lower or higher…
Group Critical-token Policy Optimization for Autoregressive Image Generation
Guohui Zhang, Hu Yu, Xiaoxiao Ma +6
Recent studies have extended Reinforcement Learning with Verifiable Rewards (RLVR) to autoregressive (AR) visual generation and achieved promising progress. However, existing metho…