Accelerated Likelihood Maximization for Diffusion-based Versatile Content Generation

Generating diverse, coherent, and plausible content from par-tially given inputs remains a fundamental challenge for diffusion models.Existing approaches face clear limitations: training-based approachesoffer strong task-specific results but require costly computation, andthey generalize poorly across tasks. Training-free approaches offer betterefficiency, but they do not explicitly optimize over unobserved variables,leading to globally inconsistent results. To address these limitations, weintroduce Accelerated Likelihood Maximization (ALM), a novel training-free sampling strategy integrated into the reverse diffusion process thatsignificantly extends the applicability of diffusion models beyond simplegeneration tasks. Unlike previous methods that implicitly influence miss-ing regions through pre-generated region constraints, we directly optimizethe unobserved region during the sampling process, enabling globallycoherent and plausible generation. Furthermore, we incorporate an ac-celeration strategy that significantly improves computational efficiencywithout sacrificing performance. Experimental results demonstrate thatALM consistently outperforms state-of-the-art methods in various datadomains and tasks, establishing a powerful paradigm for versatile contentgeneration. Project website: http://hleephilip.github.io/ALM