Toward Guidance-Free AR Visual Generation via Condition Contrastive
  Alignment
Authors: name: Huayu Chen, name: Hang Su, name: Peize Sun, name: Jun Zhu

Classifier-Free Guidance (CFG) is a critical technique for enhancing the
sample quality of visual generative models. However, in autoregressive (AR)
multi-modal generation, CFG introduces design inconsistencies between language
and visual content, contradicting the design philosophy of unifying different
modalities for visual AR. Motivated by language model alignment methods, we
propose Condition Contrastive Alignment (CCA) to facilitate
guidance-free AR visual generation with high performance and analyze its
theoretical connection with guided sampling methods. Unlike guidance methods
that alter the sampling process to achieve the ideal sampling distribution, CCA
directly fine-tunes pretrained models to fit the same distribution target.
Experimental results show that CCA can significantly enhance the guidance-free
performance of all tested models with just one epoch of fine-tuning (sim 1\%
of pretraining epochs) on the pretraining dataset, on par with guided sampling
methods. This largely removes the need for guided sampling in AR visual
generation and cuts the sampling cost by half. Moreover, by adjusting training
parameters, CCA can achieve trade-offs between sample diversity and fidelity
similar to CFG. This experimentally confirms the strong theoretical connection
between language-targeted alignment and visual-targeted guidance methods,
unifying two previously independent research fields. Code and model weights:
https://github.com/thu-ml/CCA.