Uni-Instruct: One-step Diffusion Model through Unified Diffusion Divergence Instruction
Yifei Wang, Weimin Bai, Colin Zhang, Debing Zhang, Weijian Luo, He Sun
2025-05-30
Summary
This paper talks about Uni-Instruct, a new method that improves how AI models generate images and 3D objects from text or on their own by using a special one-step diffusion process.
What's the problem?
The problem is that existing diffusion models, which create images and 3D content, often require many steps to produce good results, making them slow and complicated to use.
What's the solution?
The researchers developed Uni-Instruct, which uses a new theory called diffusion expansion to combine and improve one-step diffusion techniques. This allows the model to generate high-quality images and 3D shapes quickly and efficiently, whether it's given instructions or not.
Why it matters?
This is important because it makes creating detailed images and 3D content faster and easier, which can help artists, designers, and developers make creative projects without needing powerful computers or long wait times.
Abstract
Uni-Instruct unifies and enhances one-step diffusion distillation methods through a novel diffusion expansion theory, achieving state-of-the-art performance in unconditional and conditional image generation and text-to-3D generation.