11 papers
A Frame is Worth One Token: Efficient Generative World Modeling with Delta Tokens
Tommie Kerssies, Gabriele Berton, Ju He +5
Anticipating diverse future states is a central challenge in video world modeling. Discriminative world models produce a deterministic prediction that implicitly averages over poss…
Autoregressive Image Generation with Masked Bit Modeling
Qihang Yu, Qihao Liu, Ju He +4
This paper challenges the dominance of continuous pipelines in visual generation. We systematically investigate the performance gap between discrete and continuous methods. Contrar…
Grouping First, Attending Smartly: Training-Free Acceleration for Diffusion Transformers
Sucheng Ren, Qihang Yu, Ju He +2
Diffusion-based Transformers have demonstrated impressive generative capabilities, but their high computational costs hinder practical deployment, for example, generating an $8192\…
ReVision: Refining Video Diffusion with Explicit 3D Motion Modeling
Qihao Liu, Ju He, Qihang Yu +2
In recent years, video generation has seen significant advancements. However, challenges still persist in generating complex motions and interactions. To address these challenges,…
Beyond Next-Token: Next-X Prediction for Autoregressive Visual Generation
Sucheng Ren, Qihang Yu, Ju He +3
Autoregressive (AR) modeling, known for its next-token prediction paradigm, underpins state-of-the-art language and visual generative models. Traditionally, a ``token'' is treated…
FlowTok: Flowing Seamlessly Across Text and Image Tokens
Ju He, Qihang Yu, Qihao Liu +1
Bridging different modalities lies at the heart of cross-modality generation. While conventional approaches treat the text modality as a conditioning signal that gradually guides t…
Dictionary-based Framework for Interpretable and Consistent Object Parsing
Tiezheng Zhang, Qihang Yu, Alan Yuille +1
In this work, we present CoCal, an interpretable and consistent object parsing framework based on dictionary-based mask transformer. Designed around Contrastive Components and Logi…
Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens
Dongwon Kim, Ju He, Qihang Yu +4
Image tokenizers form the foundation of modern text-to-image generative models but are notoriously difficult to train. Furthermore, most existing text-to-image models rely on large…
FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
Sucheng Ren, Qihang Yu, Ju He +3
Autoregressive (AR) modeling has achieved remarkable success in natural language processing by enabling models to generate text with coherence and contextual understanding through…
Randomized Autoregressive Visual Generation
Qihang Yu, Ju He, Xueqing Deng +2
This paper presents Randomized AutoRegressive modeling (RAR) for visual generation, which sets a new state-of-the-art performance on the image generation task while maintaining ful…
Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models and Time-Dependent Layer Normalization
Qihao Liu, Zhanpeng Zeng, Ju He +3
This paper presents innovative enhancements to diffusion models by integrating a novel multi-resolution network and time-dependent layer normalization. Diffusion models have gained…