Organizations: Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China · Beijing Key Laboratory of Research on Large Models and Intelligent Governance · Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE · Ant Group
We present LLaDA-o, an effective and length-adaptive omni diffusion model for multimodal understanding and generation. LLaDA-o is built on a Mixture of Diffusion (MoD) framework that decouples discrete masked diffusion for text understanding and continuous diffusion for visual generation, while coupling them through a shared, simple, and efficient attention backbone that reduces redundant computation for fixed conditions. Building on MoD, we further introduce a data-centric length adaptation strategy that enables flexible-length decoding in multimodal settings without architectural changes. Extensive experiments show that LLaDA-o achieves state-of-the-art performance among omni-diffusion models on multimodal understanding and generation benchmarks, and reaches 87.04 on DPG-Bench for text-to-image generation, supporting the effectiveness of unified omni diffusion modeling. Code is available at https://github.com/ML-GSAI/LLaDA-o.
Figures & tables
Figure 1: Overview of LLaDA-o’s capabilities. Top: multimodal understanding examples. Middle: text-to-image generation results following complex prompts (see Table 7 for the prompts). Bottom: case by case comparison with existing omni diffusion models, where LLaDA-o achieves stronger understanding performance and generates images with richer fine-grained details following the instructions.
Figure 2: Overview of LLaDA-o: the mixture of diffusion framework.
Figure 3: Implementation of intra-modality bidirectional attention. Yellow blocks indicate unmasked attention, while dashed white boxes denote masked attention. Text sequences are explicitly partitioned into Prompts ( PRM ) and Responses ( RES ) in cases (a–b).
Figure 4: Comparison of inference efficiency on MathVista. We visualize the throughput-accuracy trade-off by varying the confidence threshold for LLaDA-o and the refresh interval ( n ) of Fast-dLLM applied to LLaDA-V. Our approach outperforms LLaDA-V, achieving a 5.9× speedup with comparable performance.
Appendix figures & tables3 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 5: Additional generated samples. We present 12 randomly selected images generated by LLaDA-o. For each sample, the prompt used for generation is shown below the corresponding image. All results are produced under the same setting as in the main paper.
Table 9: Qualitative samples demonstrating variable-length generation. Top: Under a long block length setting ( L=64 ), LLaDA-V generates redundant content for a simple prompt, while LLaDA-o correctly terminates. Bottom: Under a short block length setting ( L=16 ), LLaDA-V produces an overly brief response for a detailed prompt, whereas LLaDA-o dynamically adapts the output length to the user prompt.
Table 10: Qualitative samples demonstrating variable-length generation. We show generations from LLaDA-o for the same image and the same prompt under different block length settings ( L∈{16,32,64,128} ). Despite changing L , the output length remains largely stable, indicating that LLaDA-o can dynamically adapt the output length to the user prompt and image.