6 papers
Intern Lumina U2: A Multi-Codebook Diffusion Large Language Model for Omni-Visual Understanding and Image Generation
Qian Yu, Zongyang Qiu, Juncheng Yan +40
Unified multimodal generation covers discrete, continuous, and hybrid paradigms. Discrete approaches with single-codebook visual tokenizers align naturally with text modeling but h…
PulseQuant: Propagation-Guided Subspace Correction for 4-Bit Video Diffusion Transformers
Yutong Wang, Xingtong Ge, Enhuai Liu +4
Quantization errors in video diffusion transformers can be amplified or attenuated by subsequent denoising updates, making local reconstruction error an incomplete predictor of fin…
Rethinking Where to Edit: Task-Aware Localization for Instruction-Based Image Editing
Jingxuan He, Xiyu Wang, Mengyu Zheng +3
Instruction-based image editing (IIE) aims to modify an image according to a natural language instruction. Despite recent advances in diffusion transformers, existing methods often…
VDOT: Efficient Unified Video Creation via Optimal Transport Distillation
Yutong Wang, Haiyu Zhang, Tianfan Xue +4
The rapid development of generative models has significantly advanced image and video applications. Among these, video creation, aimed at generating videos under various conditions…
Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding
Yi Xin, Qi Qin, Siqi Luo +29
We introduce Lumina-DiMOO, an open-source foundational model for seamless multi-modal generation and understanding. Lumina-DiMOO sets itself apart from prior unified models by util…
Lumina-Image 2.0: A Unified and Efficient Image Generative Framework
Qi Qin, Le Zhuo, Yi Xin +20
We introduce Lumina-Image 2.0, an advanced text-to-image generation framework that achieves significant progress compared to previous work, Lumina-Next. Lumina-Image 2.0 is built u…