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BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities

18 Oct 2024arXiv:2410.14672archive 2025-07-28

Shaozhe Hao, Xuantong Liu, Xianbiao Qi, Shihao Zhao, Bojia Zi, Rong Xiao, Kai Han, Kwan-Yee K. Wong

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. We further enable BiGR to perform text-to-image generation, showcasing its potential for broader applications.

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haoosz/BiGR officialmentioned in papermentioned on GitHubpytorchMIT report

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add_weight_decay haoosz/BiGR/train_ddp.py official repository ran MIT (permissive) · 7c91d7cbaabb7970 · report
adjust_mask haoosz/BiGR/apps/inpaint.py official repository ran MIT (permissive) · c115fefb450f7ccc · report
all_reduce_mean haoosz/BiGR/misc.py official repository ran fingerprinted MIT (permissive) · 3dc19396537db789 · report
center_crop_arr haoosz/BiGR/apps/enrich.py official repository ran · our draft was wrong MIT (permissive) · ea73360d2d5d9224 · report
focal_loss haoosz/BiGR/bae/binarylatent.py official repository ran MIT (permissive) · 3164da5cbc56c361 · report
load_pretrain haoosz/BiGR/bae/binaryae.py official repository ran MIT (permissive) · fc854711df9c23ee · report
process haoosz/BiGR/apps/inpaint.py official repository ran MIT (permissive) · 3ebba5eb3f84bf5c · report
rand_brightness haoosz/BiGR/bae/diffaug.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 011230b2b9b8fb6f · report
rand_saturation haoosz/BiGR/bae/diffaug.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 5b0d8787e670fc63 · report
DiffAugment haoosz/BiGR/bae/diffaug.py official repository unverified MIT (permissive) · 1d566d068b0ed0af · report
create_logger haoosz/BiGR/train_ddp.py official repository unverified MIT (permissive) · 8382f7e48cc8a309 · report
get_grad_norm_ haoosz/BiGR/misc.py official repository unverified MIT (permissive) · ba1356e8ceb654d2 · report
get_lr haoosz/BiGR/train_ddp.py official repository unverified MIT (permissive) · 8909297ddc746f30 · report

Tasks

Conditional Image GenerationImage GenerationImage InpaintingText to Image GenerationText-to-Image GenerationZero-shot Generalization

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