Title: Accelerated Likelihood Maximization for Diffusion-based Versatile Content Generation
Primary area: generative models
Keywords: diffusion models, versatile content generation, inpainting, outpainting

Abstract: Generating diverse, coherent, and plausible content from partially given inputs remains a significant challenge for pretrained diffusion models. 
Existing approaches face clear limitations: training-based approaches offer strong task-specific results but require costly data and computation, and they generalize poorly across tasks. 
Training-free paradigms are more efficient and broadly applicable, but often fail to produce globally consistent results, as they usually enforce constraints only on observed regions.
To address these limitations, we introduce Accelerated Likelihood Maximization (ALM), a novel training-free sampling strategy integrated into the reverse process of diffusion models. 
ALM explicitly optimizes the unobserved regions by jointly maximizing both conditional and joint likelihoods. 
This ensures that the generated content is not only faithful to the given input but also globally coherent and plausible. 
We further incorporate an acceleration mechanism to enable efficient computation. 
Experimental results demonstrate that ALM consistently outperforms state-of-the-art methods in various data domains and tasks, establishing a powerful, training-free paradigm for versatile content generation.

TL;DR: We propose Accelerated Likelihood Maximization, a tailored strategy for versatile content generation that supports various inpainting and outpaintnig scenarios.