Impressive!
\n","updatedAt":"2025-09-26T03:18:41.116Z","author":{"_id":"63fccdac93b993a4ebd7789a","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63fccdac93b993a4ebd7789a/KRx8vpdoDjsZBRw0j8Vg8.jpeg","fullname":"Jinbin Bai","name":"BryanW","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":10}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8270281553268433},"editors":["BryanW"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/63fccdac93b993a4ebd7789a/KRx8vpdoDjsZBRw0j8Vg8.jpeg"],"reactions":[],"isReport":false,"parentCommentId":"68d4bdc5924e3147f10fd290"}}]},{"id":"68d5edb1d479f2553b133067","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":264},"createdAt":"2025-09-26T01:34:41.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer](https://huggingface.co/papers/2509.16197) (2025)\n* [UniLiP: Adapting CLIP for Unified Multimodal Understanding, Generation and Editing](https://huggingface.co/papers/2507.23278) (2025)\n* [Bifrost-1: Bridging Multimodal LLMs and Diffusion Models with Patch-level CLIP Latents](https://huggingface.co/papers/2508.05954) (2025)\n* [OneCAT: Decoder-Only Auto-Regressive Model for Unified Understanding and Generation](https://huggingface.co/papers/2509.03498) (2025)\n* [NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale](https://huggingface.co/papers/2508.10711) (2025)\n* [UNCAGE: Contrastive Attention Guidance for Masked Generative Transformers in Text-to-Image Generation](https://huggingface.co/papers/2508.05399) (2025)\n* [UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets](https://huggingface.co/papers/2509.14738) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
\nThe following papers were recommended by the Semantic Scholar API
\n- \n
- MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer (2025) \n
- UniLiP: Adapting CLIP for Unified Multimodal Understanding, Generation and Editing (2025) \n
- Bifrost-1: Bridging Multimodal LLMs and Diffusion Models with Patch-level CLIP Latents (2025) \n
- OneCAT: Decoder-Only Auto-Regressive Model for Unified Understanding and Generation (2025) \n
- NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale (2025) \n
- UNCAGE: Contrastive Attention Guidance for Masked Generative Transformers in Text-to-Image Generation (2025) \n
- UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets (2025) \n
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Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and Generation
Abstract
Lavida-O, a unified Masked Diffusion Model, excels in multimodal understanding and generation tasks, including object grounding, image editing, and high-resolution text-to-image synthesis, outperforming existing models with improved efficiency and quality.
We propose Lavida-O, a unified Masked Diffusion Model (MDM) for multimodal understanding and generation. Unlike existing multimodal MDMs such as MMaDa and Muddit which only support simple image-level understanding tasks and low-resolution image generation, Lavida-O presents a single framework that enables image-level understanding, object grounding, image editing, and high-resolution (1024px) text-to-image synthesis. Lavida-O incorporates a novel Elastic Mixture-of-Transformers (Elastic-MoT) architecture that couples a lightweight generation branch with a larger understanding branch, supported by token compression, universal text conditioning and stratified sampling for efficient and high-quality generation. Lavida-O further incorporates planning and iterative self-reflection in image generation and editing tasks, seamlessly boosting generation quality with its understanding capabilities. Lavida-O achieves state-of-the-art performance on a wide range of benchmarks including RefCOCO object grounding, GenEval text-to-image generation, and ImgEdit image editing, outperforming existing autoregressive models and continuous diffusion models such as Qwen2.5-VL and FluxKontext-dev, while offering considerable speedup at inference. These advances establish Lavida-O as a new paradigm for scalable multimodal reasoning and generation.
Community
We propose Lavida-O, a unified Masked Diffusion Model (MDM) for multimodal understanding and generation. Unlike existing multimodal MDMs such as MMaDa and Muddit which only support simple image-level understanding tasks and low-resolution image generation, Lavida-O presents a single framework that enables image-level understanding, object grounding, image editing, and high-resolution (1024px) text-to-image synthesis. Lavida-O incorporates a novel Elastic Mixture-of-Transformers (Elastic-MoT) architecture that couples a lightweight generation branch with a larger understanding branch, supported by token compression, universal text conditioning and stratified sampling for efficient and high-quality generation. Lavida-O further incorporates planning and iterative self-reflection in image generation and editing tasks, seamlessly boosting generation quality with its understanding capabilities. Lavida-O achieves state-of-the-art performance on a wide range of benchmarks including RefCOCO object grounding, GenEval text-to-image generation, and ImgEdit image editing, outperforming existing autoregressive models and continuous diffusion models such as Qwen2.5-VL and FluxKontext-dev, while offering considerable speedup at inference. These advances establish Lavida-O as a new paradigm for scalable multimodal reasoning and generation.
Impressive!
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer (2025)
- UniLiP: Adapting CLIP for Unified Multimodal Understanding, Generation and Editing (2025)
- Bifrost-1: Bridging Multimodal LLMs and Diffusion Models with Patch-level CLIP Latents (2025)
- OneCAT: Decoder-Only Auto-Regressive Model for Unified Understanding and Generation (2025)
- NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale (2025)
- UNCAGE: Contrastive Attention Guidance for Masked Generative Transformers in Text-to-Image Generation (2025)
- UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets (2025)
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