Unified Multimodal Large Language Models (MLLMs) offer a promising paradigm for unifying visual understanding and generation, yet they still struggle to follow complex spatial instructions and logical constraints in controllable image generation. To address this gap, we present ATLAS, a unified framework that equips ML...
Junhao Liu, Jianwei Zhang, Tao Huang et al.· arXiv.org· 1 citation
WithEveryone, a unified framework for generating group images up to ten reference identities, is introduced, showing that explicit identity--layout grounding enables identity-preserving generation to scale to larger groups without relying on direct reference-face copying.
Heng-Yuan Xu, Qixun Wang, Yiji Cheng et al.· 0 citations
Twin, a unified continuous token space formed by channel-wise concatenating ViT and VAE features on the same token grid, so the sequence length is unchanged and attention cost does not increase is proposed, which performs competitively on multimodal understanding benchmarks and improves reconstruction fidelity.
Kaixiong Gong, Xin Cai, Bin Lin et al.· arXiv.org· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
