1 citations · 1 across the 4 of their papers we have counts for
10 papers
An Empirical Study of Training Pixel-Space Text-to-Image Diffusion Models
Dengyang Jiang, Ruoyi Du, Zhennan Chen +10
This paper investigates an increasingly important topic in generative modeling: pixel-space diffusion models. Although numerous studies have explored this topic, most focus on smal…
Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
Image Team, Huanqia Cai, Sihan Cao +21
The landscape of high-performance image generation models is currently dominated by proprietary systems, such as Nano Banana Pro and Seedream 4.0. Leading open-source alternatives,…
High-Fidelity Two-Step Image Generation via Teacher-Aligned End-to-End Distillation
Dongyang Liu, Ruoyi Du, David Liu +7
Few-step diffusion distillation has become increasingly mature for 4-8-step generation, yet pushing further to 2 steps remains challenging. In this work, we introduce Z-Image Turbo…
D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models
Dengyang Jiang, Xin Jin, Dongyang Liu +9
The landscape of high-performance image generation models is currently shifting from the inefficient multi-step ones to the efficient few-step counterparts (e.g, Z-Image-Turbo and…
VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning
Zhong-Yu Li, Ruoyi Du, Juncheng Yan +6
Recent progress in diffusion models significantly advances various image generation tasks. However, the current mainstream approach remains focused on building task-specific models…
Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shield
Dongyang Liu, Peng Gao, David Liu +8
Diffusion model distillation has emerged as a powerful technique for creating efficient few-step and single-step generators. Among these, Distribution Matching Distillation (DMD) a…