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Correction Training for Diffusion Models
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diffusion modelssample qualityerror correctionguidance
2610.14136
Builder Relevance
2h ago80%
Abstract
The paper introduces a correction step for diffusion models to reduce the training-inference gap and improve sample quality.
Reality Card
Core Claim
The proposed correction step significantly enhances sample quality in diffusion models, achieving performance competitive with classifier-free guidance (CFG).
Method / Result
Substantial improvement in FID scores for unguided and unconditional generation.
Limitations
The paper does not specify authors or provide detailed reproducibility metrics.
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