May 8, 2023, 12:46 a.m. | Jiafeng Mao, Xueting Wang, Kiyoharu Aizawa

cs.CV updates on arXiv.org arxiv.org

Diffusion models have the ability to generate high quality images by
denoising pure Gaussian noise images. While previous research has primarily
focused on improving the control of image generation through adjusting the
denoising process, we propose a novel direction of manipulating the initial
noise to control the generated image. Through experiments on stable diffusion,
we show that blocks of pixels in the initial latent images have a preference
for generating specific content, and that modifying these blocks can
significantly influence …

adjusting arxiv control denoising diffusion diffusion model diffusion models editing generated image image generation images noise novel process quality research synthesis through

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

DevOps Engineer (Data Team)

@ Reward Gateway | Sofia/Plovdiv