7 papers
i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models
Boya Zeng, Tianze Luo, Shu Pu +4
Diffusion models have consistently driven progress in text-to-image generation. However, it is challenging to attribute recent progress to specific modeling and data choices: state…
Stronger Normalization-Free Transformers
Mingzhi Chen, Taiming Lu, Jiachen Zhu +2
Although normalization layers have long been viewed as indispensable components of deep learning architectures, the recent introduction of Dynamic Tanh (DyT) has demonstrated that…
SoFlow: Solution Flow Models for One-Step Generative Modeling
Tianze Luo, Haotian Yuan, Zhuang Liu
The multi-step denoising process in diffusion and Flow Matching models causes major efficiency issues, which motivates research on few-step generation. We present Solution Flow Mod…
UEval: A Benchmark for Unified Multimodal Generation
Bo Li, Yida Yin, Wenhao Chai +2
We introduce UEval, a benchmark to evaluate unified models, i.e., models capable of generating both images and text. UEval comprises 1,000 expert-curated questions that require bot…
Memorization in 3D Shape Generation: An Empirical Study
Shu Pu, Boya Zeng, Kaichen Zhou +2
Generative models are increasingly used in 3D vision to synthesize novel shapes, yet it remains unclear whether their generation relies on memorizing training shapes. Understanding…
Generative Modeling of Weights: Generalization or Memorization?
Boya Zeng, Yida Yin, Zhiqiu Xu +1
Generative models have recently been explored for synthesizing neural network weights. These approaches take neural network checkpoints as training data and aim to generate high-pe…