Autoguidance consistently improves sample quality and diversity in diffusion models, while early AJEST matches or slightly exceeds it in data efficiency but adds overhead that makes autoguidance or random selection preferable in most cases.
Exposing flaws of generative model evaluation metrics and their un- fair treatment of diffusion models
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Autoguided Online Data Curation for Diffusion Model Training
Autoguidance consistently improves sample quality and diversity in diffusion models, while early AJEST matches or slightly exceeds it in data efficiency but adds overhead that makes autoguidance or random selection preferable in most cases.