3 citations · 6 across the 9 of their papers we have counts for
6 papers · 1 filter
SJD++: Improved Speculative Jacobi Decoding for Training-free Acceleration of Discrete Auto-regressive Text-to-Image Generation
Yao Teng, Zhihuan Jiang, Han Shi +6
Large autoregressive models can generate high-quality, high-resolution images but suffer from slow generation speed, because these models require hundreds to thousands of sequentia…
Speculative Jacobi-Denoising Decoding for Accelerating Autoregressive Text-to-image Generation
Yao Teng, Fuyun Wang, Xian Liu +7
As a new paradigm of visual content generation, autoregressive text-to-image models suffer from slow inference due to their sequential token-by-token decoding process, often requir…
Self-NPO: Data-Free Diffusion Model Enhancement via Truncated Diffusion Fine-Tuning
Fu-Yun Wang, Keqiang Sun, Yao Teng +4
Diffusion models have demonstrated remarkable success in various visual generation tasks, including image, video, and 3D content generation. Preference optimization (PO) is a promi…
Personalized Text-to-Image Generation with Auto-Regressive Models
Kaiyue Sun, Xian Liu, Yao Teng +1
Personalized image synthesis has emerged as a pivotal application in text-to-image generation, enabling the creation of images featuring specific subjects in diverse contexts. Whil…
Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation
Yuqing Wang, Zhijie Lin, Yao Teng +4
Autoregressive visual generation models typically rely on tokenizers to compress images into tokens that can be predicted sequentially. A fundamental dilemma exists in token repres…
Exploring Representation-Aligned Latent Space for Better Generation
Wanghan Xu, Xiaoyu Yue, Zidong Wang +6
Generative models serve as powerful tools for modeling the real world, with mainstream diffusion models, particularly those based on the latent diffusion model paradigm, achieving…