Authors: Shaozhe Hao, Xuantong Liu, Xianbiao Qi, Shihao Zhao, Bojia Zi, Rong Xiao, Kai Han, Kwan-Yee K. Wong
Abstract: We introduce BiGR, a novel conditional image generation model using compact
binary latent codes for generative training, focusing on enhancing both
generation and representation capabilities. BiGR is the first conditional
generative model that unifies generation and discrimination within the same
framework. BiGR features a binary tokenizer, a masked modeling mechanism, and a
binary transcoder for binary code prediction. Additionally, we introduce a
novel entropy-ordered sampling method to enable efficient image generation.
Extensive experiments validate BiGR’s superior performance in generation
quality, as measured by FID-50k, and representation capabilities, as evidenced
by linear-probe accuracy. Moreover, BiGR showcases zero-shot generalization
across various vision tasks, enabling applications such as image inpainting,
outpainting, editing, interpolation, and enrichment, without the need for
structural modifications. Our findings suggest that BiGR unifies generative and
discriminative tasks effectively, paving the way for further advancements in
the field.
Source: http://arxiv.org/abs/2410.14672v1
