Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding
- URL: http://arxiv.org/abs/2510.06308v1
- Date: Tue, 07 Oct 2025 17:59:20 GMT
- Title: Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding
- Authors: Yi Xin, Qi Qin, Siqi Luo, Kaiwen Zhu, Juncheng Yan, Yan Tai, Jiayi Lei, Yuewen Cao, Keqi Wang, Yibin Wang, Jinbin Bai, Qian Yu, Dengyang Jiang, Yuandong Pu, Haoxing Chen, Le Zhuo, Junjun He, Gen Luo, Tianbin Li, Ming Hu, Jin Ye, Shenglong Ye, Bo Zhang, Chang Xu, Wenhai Wang, Hongsheng Li, Guangtao Zhai, Tianfan Xue, Bin Fu, Xiaohong Liu, Yu Qiao, Yihao Liu,
- Abstract summary: Lumina-DiMOO is an open-source foundational model for seamless multi-modal generation and understanding.<n>It uses a fully discrete diffusion modeling to handle inputs and outputs across various modalities.<n>It achieves state-of-the-art performance on multiple benchmarks, surpassing existing open-source unified multi-modal models.
- Score: 134.93925077411564
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 utilizing a fully discrete diffusion modeling to handle inputs and outputs across various modalities. This innovative approach allows Lumina-DiMOO to achieve higher sampling efficiency compared to previous autoregressive (AR) or hybrid AR-Diffusion paradigms and adeptly support a broad spectrum of multi-modal tasks, including text-to-image generation, image-to-image generation (e.g., image editing, subject-driven generation, and image inpainting, etc.), as well as image understanding. Lumina-DiMOO achieves state-of-the-art performance on multiple benchmarks, surpassing existing open-source unified multi-modal models. To foster further advancements in multi-modal and discrete diffusion model research, we release our code and checkpoints to the community. Project Page: https://synbol.github.io/Lumina-DiMOO.
Related papers
- Query-Kontext: An Unified Multimodal Model for Image Generation and Editing [53.765351127477224]
Unified Multimodal Models (UMMs) have demonstrated remarkable performance in text-to-image generation (T2I) and editing (TI2I)<n>We introduce Query-Kontext, a novel approach that bridges the VLM and diffusion model via a multimodal kontext'' composed of semantic cues and coarse-grained image conditions encoded from multimodal inputs.<n> Experiments show that our approach matches strong unified baselines and even outperforms task-specific state-of-the-art methods in several cases.
arXiv Detail & Related papers (2025-09-30T17:59:46Z) - Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation [54.588082888166504]
We present Mogao, a unified framework that enables interleaved multi-modal generation through a causal approach.<n>Mogoo integrates a set of key technical improvements in architecture design, including a deep-fusion design, dual vision encoders, interleaved rotary position embeddings, and multi-modal classifier-free guidance.<n>Experiments show that Mogao achieves state-of-the-art performance in multi-modal understanding and text-to-image generation, but also excels in producing high-quality, coherent interleaved outputs.
arXiv Detail & Related papers (2025-05-08T17:58:57Z) - Unified Multimodal Discrete Diffusion [78.48930545306654]
Multimodal generative models that can understand and generate across multiple modalities are dominated by autoregressive (AR) approaches.<n>We explore discrete diffusion models as a unified generative formulation in the joint text and image domain.<n>We present the first Unified Multimodal Discrete Diffusion (UniDisc) model which is capable of jointly understanding and generating text and images.
arXiv Detail & Related papers (2025-03-26T17:59:51Z) - MMGen: Unified Multi-modal Image Generation and Understanding in One Go [60.97155790727879]
We introduce MMGen, a unified framework that integrates multiple generative tasks into a single diffusion model.<n>Our approach develops a novel diffusion transformer that flexibly supports multi-modal output, along with a simple modality-decoupling strategy.
arXiv Detail & Related papers (2025-03-26T15:37:17Z) - Dual Diffusion for Unified Image Generation and Understanding [32.7554623473768]
We propose a large-scale and fully end-to-end diffusion model for multi-modal understanding and generation.<n>We leverage a cross-modal maximum likelihood estimation framework that simultaneously trains the conditional likelihoods of both images and text jointly.<n>Our model attained competitive performance compared to recent unified image understanding and generation models.
arXiv Detail & Related papers (2024-12-31T05:49:00Z) - Unified Discrete Diffusion for Simultaneous Vision-Language Generation [78.21352271140472]
We present a unified multimodal generation model that can conduct both the "modality translation" and "multi-modality generation" tasks.
Specifically, we unify the discrete diffusion process for multimodal signals by proposing a unified transition matrix.
Our proposed method can perform comparably to the state-of-the-art solutions in various generation tasks.
arXiv Detail & Related papers (2022-11-27T14:46:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.