6 papers
DiverseAR: Boosting Diversity in Bitwise Autoregressive Image Generation
Ying Yang, Zhengyao Lv, Tianlin Pan +5
In this paper, we investigate the underexplored challenge of sample diversity in autoregressive (AR) generative models with bitwise visual tokenizers. We first analyze the factors…
FlowSteer: Guiding Few-Step Image Synthesis with Authentic Trajectories
Lei Ke, Hubery Yin, Gongye Liu +6
With the success of flow matching in visual generation, sampling efficiency remains a critical bottleneck for its practical application. Among flow models' accelerating methods, Re…
ETC: training-free diffusion models acceleration with Error-aware Trend Consistency
Jiajian Xie, Hubery Yin, Chen Li +2
Diffusion models have achieved remarkable generative quality but remain bottlenecked by costly iterative sampling. Recent training-free methods accelerate diffusion process by reus…
TimeMachine: Fine-Grained Facial Age Editing with Identity Preservation
Yilin Mi, Qixin Yan, Zheng-Peng Duan +5
With the advancement of generative models, facial image editing has made significant progress. However, achieving fine-grained age editing while preserving personal identity remain…
Unleashing High-Quality Image Generation in Diffusion Sampling Using Second-Order Levenberg-Marquardt-Langevin
Fangyikang Wang, Hubery Yin, Lei Qian +9
The diffusion models (DMs) have demonstrated the remarkable capability of generating images via learning the noised score function of data distribution. Current DM sampling techniq…
Efficiently Access Diffusion Fisher: Within the Outer Product Span Space
Fangyikang Wang, Hubery Yin, Shaobin Zhuang +7
Recent Diffusion models (DMs) advancements have explored incorporating the second-order diffusion Fisher information (DF), defined as the negative Hessian of log density, into vari…