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DiScoFormer is a single transformer model that estimates both the density and score of a distribution from a set of data points in one forward pass without retraining, overcoming the trade-off between generalizability and accuracy of previous methods.
This paper argues that vanilla conditional diffusion models fundamentally fail at compositional generation when the target distribution is out-of-distribution, due to score estimation error, and that inference-time corrections cannot fully compensate.