Introduces a training-free inference-time method for aesthetic refinement in unconditional diffusion models using degradation concept vectors, bottleneck patching, and classifier-free guidance to steer away from degraded outputs.
Unsupervised discovery of semantic latent directions in diffusion models
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Introduces 9 synthetic annotation tasks and benchmarks for behavioral cloning, finding hierarchical skill learning, scaling benefits, effective multi-task pretraining, and shared internal representations of task phases and mistakes.
Semantic similarity between texts is measured by the Jeffreys divergence between the image distributions induced by conditioning a diffusion model on each text, computed via Monte-Carlo sampling of the reverse-time SDEs.
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Conjuring Semantic Similarity
Semantic similarity between texts is measured by the Jeffreys divergence between the image distributions induced by conditioning a diffusion model on each text, computed via Monte-Carlo sampling of the reverse-time SDEs.