4 papers
Diffusion Domain Expansion: Learning to Coordinate Pre-trained Diffusion Models
Egor Lifar, Semyon Savkin, Timur Garipov +2
In this paper, we propose Diffusion Domain Expansion (DDE), a method that efficiently extends pre-trained diffusion models to generate larger objects and handle more complex condit…
Flow Map Distillation Without Data
Shangyuan Tong, Nanye Ma, Saining Xie +1
State-of-the-art flow models achieve remarkable quality but require slow, iterative sampling. To accelerate this, flow maps can be distilled from pre-trained teachers, a procedure…
Next Semantic Scale Prediction via Hierarchical Diffusion Language Models
Cai Zhou, Chenyu Wang, Dinghuai Zhang +4
In this paper we introduce Hierarchical Diffusion Language Models (HDLM) -- a novel family of discrete diffusion models for language modeling. HDLM builds on a hierarchical vocabul…
Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps
Nanye Ma, Shangyuan Tong, Haolin Jia +8
Generative models have made significant impacts across various domains, largely due to their ability to scale during training by increasing data, computational resources, and model…