In this work, we extend diffusion solvers to efficiently handle general noisy (non)linear inverse problems via the approximation of the posterior sampling. Interestingly, the resulting posterior ...

Gunn is part of a research team at UC Berkeley that has developed an undetectable watermark for generative image models. The ...

Check our latest work DeepCache, a training-free and almost loessless method for diffusion model acceleration. It can be viewed as a special pruning technique that dynamically drops deep layers and ...

Abstract: Image denoising is a fundamental problem in computational photography, where achieving high perception with low distortion is highly demanding. Current methods either struggle with ...

The history of human progress is, in many ways, the history of technology’s diffusion. Among these advances, a few—what economists call general-purpose technologies—have had the greatest impact on ...

Abstract: Diffusion models are a type of generative deep learning model that can process medical images more efficiently than traditional generative models. They have been applied to several medical ...