6 papers
Incorporating Pre-trained Diffusion Models in Solving the Schrödinger Bridge Problem
Zhicong Tang, Tiankai Hang, Shuyang Gu +2
This paper aims to unify Score-based Generative Models (SGMs), also known as Diffusion models, and the Schrödinger Bridge (SB) problem through three reparameterization techniques:…
Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization
Zhanhao Liang, Yuhui Yuan, Shuyang Gu +5
Generating visually appealing images is fundamental to modern text-to-image generation models. A potential solution to better aesthetics is direct preference optimization (DPO), wh…
DesignDiffusion: High-Quality Text-to-Design Image Generation with Diffusion Models
Zhendong Wang, Jianmin Bao, Shuyang Gu +3
In this paper, we present DesignDiffusion, a simple yet effective framework for the novel task of synthesizing design images from textual descriptions. A primary challenge lies in…
CCA: Collaborative Competitive Agents for Image Editing
Tiankai Hang, Shuyang Gu, Dong Chen +2
This paper presents a novel generative model, Collaborative Competitive Agents (CCA), which leverages the capabilities of multiple Large Language Models (LLMs) based agents to exec…
Improved Noise Schedule for Diffusion Training
Tiankai Hang, Shuyang Gu, Xin Geng +1
Diffusion models have emerged as the de facto choice for generating high-quality visual signals across various domains. However, training a single model to predict noise across var…
Simplified Diffusion Schrödinger Bridge
Zhicong Tang, Tiankai Hang, Shuyang Gu +2
This paper introduces a novel theoretical simplification of the Diffusion Schrödinger Bridge (DSB) that facilitates its unification with Score-based Generative Models (SGMs), addr…