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cs.CV2026
Structured State-Space Regularization for Generation-Friendly Image Tokenization
Jinsung Lee, Jaemin Oh, Namhun Kim +3
Image tokenizers play a central role in modern generative models, where the structure of the latent space critically determines the downstream generation performance. A key but und…
cs.CV2025
Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens
Dongwon Kim, Ju He, Qihang Yu +4
Image tokenizers form the foundation of modern text-to-image generative models but are notoriously difficult to train. Furthermore, most existing text-to-image models rely on large…
cs.CV2024
1.58-bit FLUX
Chenglin Yang, Celong Liu, Xueqing Deng +4
We present 1.58-bit FLUX, the first successful approach to quantizing the state-of-the-art text-to-image generation model, FLUX.1-dev, using 1.58-bit weights (i.e., values in {-1,…