RDM trains one-step generators via MMD on large batches and multi-encoder representations, achieving SOTA SW_r14 of 1.30 on ImageNet and distilling FLUX.2 to one-step with gains on GenEval and PickScore.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
MIND uses sliced Wasserstein distance on Inception features to evaluate generative models, matching FID performance with 10x fewer samples and 100x faster computation while being more robust to moment-matching attacks.
citing papers explorer
-
Representation Distribution Matching for One-Step Visual Generation
RDM trains one-step generators via MMD on large batches and multi-encoder representations, achieving SOTA SW_r14 of 1.30 on ImageNet and distilling FLUX.2 to one-step with gains on GenEval and PickScore.
-
MIND: Monge Inception Distance for Generative Models Evaluation
MIND uses sliced Wasserstein distance on Inception features to evaluate generative models, matching FID performance with 10x fewer samples and 100x faster computation while being more robust to moment-matching attacks.