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Block verification boosts the efficiency of diffusion models, yielding a surprising 6.3% speedup in inference without additional training.
GMD algorithms, previously seen as a novel generative framework, can be understood as directly targeting fixed points of Wasserstein Gradient Flows, offering a new perspective on their optimization process.
Refining generative models with discriminator guidance provably improves generalization, offering a theoretical justification for techniques like score-based diffusion.
