After exploring the fundamentals of diffusion model sampling, parameterization, and training as explained in Generative AI Research Spotlight: Demystifying Diffusion-Based Models, our team began investigating the internals of these network architectures. This turned out to be a frustrating exercise. Any direct attempt to improve these models tended to worsen the results. They seemed to be in…
]]>With Internet-scale data, the computational demands of AI-generated content have grown significantly, with data centers running full steam for weeks or months to train a single model—not to mention the high inference costs in generation, often offered as a service. In this context, suboptimal algorithmic design that sacrifices performance is an expensive mistake. Much of the recent progress…
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