Latent Flow Matching models exhibit inherent stability to data reduction and model shrinkage due to the flow matching objective, enabling reduced-dataset training and two-stage inference with over 2x speedup while preserving output quality.
When DiT-XL/2, a larger capacity model, is used (row 2), these artifacts disappear and the images appear sharper
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Exploring and Exploiting Stability in Latent Flow Matching
Latent Flow Matching models exhibit inherent stability to data reduction and model shrinkage due to the flow matching objective, enabling reduced-dataset training and two-stage inference with over 2x speedup while preserving output quality.