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Dynamic Classifier-Free Diffusion Guidance via Online Feedback
Pinelopi Papalampidi, Olivia Wiles, Ira Ktena +5
Classifier-free guidance (CFG) is a cornerstone of text-to-image diffusion models, yet its effectiveness is limited by the use of static guidance scales. This "one-size-fits-all" a…
Evaluating Model Bias Requires Characterizing its Mistakes
Isabela Albuquerque, Jessica Schrouff, David Warde-Farley +3
The ability to properly benchmark model performance in the face of spurious correlations is important to both build better predictors and increase confidence that models are operat…
Evaluating Numerical Reasoning in Text-to-Image Models
Ivana Kajić, Olivia Wiles, Isabela Albuquerque +4
Text-to-image generative models are capable of producing high-quality images that often faithfully depict concepts described using natural language. In this work, we comprehensivel…