SCALE: Improving Guidance-Free Visual Generation via Self-Contrastive Alignment for Likelihood Estimation
Read the original on arXiv Computer Vision →The paper introduces Self-Contrastive Guidance (SCG), a method that contrasts a model’s predictions on real versus self-generated samples to directly guide generation toward the true data manifold. SCG is incorporated into the likelihood objective through reparameterization, resulting in the Self-Contrastive Alignment for Likelihood Estimation (SCALE) framework, which produces SCG-enhanced predictions in a single forward pass. Experiments on class-conditional and text-to-image generation with diffusion and autoregressive models show that SCALE consistently outperforms existing guidance-free baselines and further benefits from combining with Direct Preference Optimization (DPO).
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