7 papers
From Next-Token to Next-Block: A Principled Adaptation Path for Diffusion LLMs
Yuchuan Tian, Yuchen Liang, Shuo Zhang +10
Diffusion Language Models (DLMs) enable fast generation, yet training large DLMs from scratch is costly. As a practical shortcut, adapting off-the-shelf Auto-Regressive (AR) model…
Top 10 Open Challenges Steering the Future of Diffusion Language Model and Its Variants
Yunhe Wang, Kai Han, Huiling Zhen +13
The paradigm of Large Language Models (LLMs) is currently defined by auto-regressive (AR) architectures, which generate text through a sequential ``brick-by-brick'' process. Despit…
Deferred Commitment Decoding for Diffusion Language Models
Yingte Shu, Yuchuan Tian, Chao Xu +2
Diffusion language models (DLMs) have recently emerged as a strong alternative to autoregressive models by enabling parallel text generation. To improve inference efficiency and KV…
Post-Training Quantization for Diffusion Transformer via Hierarchical Timestep Grouping
Ning Ding, Jing Han, Yuchuan Tian +3
Diffusion Transformer (DiT) has now become the preferred choice for building image generation models due to its great generation capability. Unlike previous convolution-based UNet…
U-REPA: Aligning Diffusion U-Nets to ViTs
Yuchuan Tian, Hanting Chen, Mengyu Zheng +3
Representation Alignment (REPA) that aligns Diffusion Transformer (DiT) hidden-states with ViT visual encoders has proven highly effective in DiT training, demonstrating superior c…
DiC: Rethinking Conv3x3 Designs in Diffusion Models
Yuchuan Tian, Jing Han, Chengcheng Wang +3
Diffusion models have shown exceptional performance in visual generation tasks. Recently, these models have shifted from traditional U-Shaped CNN-Attention hybrid structures to ful…