这是资源清单仓库,不含可运行代码,需按各模型条目跳转到对应 GitHub 仓库安装
Awesome-Unified-Multimodal-Models
ATH-MaaS更新放缓汇总统一多模态模型的开源资源清单,涵盖图文生成、视觉语言模型等方向,方便快速查找和对比各类多模态大模型。
Awesome Unified Multimodal Models
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统一多模态模型的论文与数据集索引,帮你快速对比选型
汇总统一多模态模型的开源资源清单,涵盖图文生成、视觉语言模型等方向,方便快速查找和对比各类多模态大模型。
核心能力
- 按 Diffusion、MLLM AR、AR+Diffusion 三类架构整理模型
- 汇总理解、图像生成、交错图文三类评测基准
- 收录多模态理解、文生图、图像编辑等代表性数据集
- 附综述论文与 2023–2025 模型时间线图
容易踩的坑
暂无 release 数据(可能没有发版,或尚未同步)。
用户点评
登录用户的真实使用记录,可按版本和硬件筛选还没有人点评。第一个说出真实体验的人最有价值。

Figure 1: Timeline of Publicly Available and Unavailable Unified Multimodal Models. The models are categorized by their release years, from 2023 to 2025. Models underlined in the diagram represent any-to-any multimodal models, capable of handling inputs or outputs beyond text and image, such as audio, video, and speech. The timeline highlights the rapid growth in this field.
🔥 We are hiring!
We are looking for both interns and full-time researchers to join our team, focusing on multimodal understanding, generation, reasoning, AI agents, and unified multimodal models. If you are interested in exploring these exciting areas, please reach out to us at [email protected].
👉 What is This Repo for?
This repository provides a comprehensive collection of resources related to unified multimodal models, featuring:
- A survey of advances, challenges, and timelines for unified models
- Categorized lists of diffusion-based, autoregressive (MLLM), and hybrid architectures for unified image–text understanding and generation
- Benchmarks for evaluating multimodal comprehension, image generation, and interleaved image–text tasks
- Representative datasets covering multimodal understanding, text-to-image synthesis, image editing, and interleaved interactions
Designed to help researchers and practitioners explore, compare, and build state-of-the-art unified multimodal systems.
Awesome Papers & Datasets
- Text-and-Image Unified Models
- Any-to-Any Multimodal models
- Benchmark for Evaluation
- Dataset
- Applications and Opportunities
Text-and-Image Unified Models

Figure 2: Classification of Unified Multimodal Understanding and Generation Models. The models are divided into three main categories based on their backbone architecture: Diffusion, MLLM (AR), and MLLM (AR + Diffusion). Each category is further subdivided according to the encoding strategy employed, including Pixel Encoding, Semantic Encoding, Learnable Query Encoding, and Hybrid Encoding. We illustrate the architectural variations within these categories and their corresponding encoder-decoder configurations.
Diffusion
| Name | Title | Venue | Date | Code | Demo |
|---|---|---|---|---|---|
| UniModel | UniModel: A Visual-Only Framework for Unified Multimodal Understanding and Generation | arXiv | 2025/11/21 | - | - |
| Lavida-O | Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and Generation | arXiv | 2025/09/24 | Github | - |
| Muddit | Muddit: Liberating Generation Beyond Text-to-Image with a Unified Discrete Diffusion Model | arXiv | 2025/05/23 | Github | - |
| FUDOKI | FUDOKI: Discrete Flow-based Unified Understanding and Generation via Kinetic-Optimal Velocities | arXiv | 2025/05/20 | - | - |
| MMaDA | MMaDA: Multimodal Large Diffusion Language Models | arXiv | 2025/05/21 | Github | Demo |
| UniDisc | Unified Multimodal Discrete Diffusion | arXiv | 2025/03/20 | Github | - |
| Dual Diffusion | Dual Diffusion for Unified Image Generation and Understanding | arXiv | 2024/12/31 | Github | - |
MLLM AR
b-1: Pixel Encoding
b-2: Semantic Encoding
b-3: Learnable Query Encoding
b-4: Hybrid Encoding (Pseduo)
| Name | Title | Venue | Date | Code | Demo |
|---|---|---|---|---|---|
| Skywork UniPic | Skywork UniPic: Unified Autoregressive Modeling for Visual Understanding and Generation | arXiv | 2025/08/05 | Github | Demo |
| MindOmni | MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO | arXiv | 2025/05/13 | Github | Demo |
| UniFluid | Unified Autoregressive Visual Generation and Understanding with Continuous Tokens | arXiv | 2025/03/17 | - | - |
| OmniMamba | OmniMamba: Efficient and Unified Multimodal Understanding and Generation via State Space Models | arXiv | 2025/03/11 | Github | - |
| Janus-Pro | Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling | arXiv | 2025/01/29 | Github | Demo |
| Janus | Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation | arXiv | 2024/10/17 | Github | Demo |
b-5: Hybrid Encoding (Joint)
| Name | Title | Venue | Date | Code | Demo |
|---|---|---|---|---|---|
| Show-o2 | Show-o2: Improved Native Unified Multimodal Models | arXiv | 2025/06/15 | Github | - |
| UniToken | UniToken: Harmonizing Multimodal Understanding and Generation through Unified Visual Encoding | CVPRW | 2025/04/06 | Github | - |
| VARGPT-v1.1 | VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via Iterative Instruction Tuning and Reinforcement Learning | arXiv | 2025/04/03 | Github | - |
| ILLUME+ | ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement | arXiv | 2025/04/02 | Github | - |
| SemHiTok | SemHiTok: A Unified Image Tokenizer via Semantic-Guided Hierarchical Codebook for Multimodal Understanding and Generation | arXiv | 2025/03/06 | - | - |
| VARGPT | VARGPT: Unified Understanding and Generation in a Visual Autoregressive Multimodal Large Language Model | arXiv | 2025/01/21 | Github | - |
| TokenFlow | TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation | CVPR | 2024/12/04 | Github | - |
| MUSE-VL | MUSE-VL: Modeling Unified VLM through Semantic Discrete Encoding | arXiv | 2024/11/26 | - | - |
MLLM AR-Diffusion
c-1: Pxiel Encoding
| Name | Title | Venue | Date | Code | Demo |
|---|---|---|---|---|---|
| TUNA | Tuna: Taming Unified Visual Representations for Native Unified Multimodal Models | arXiv | 2025/12/01 | Github | - |
| LMFusion | LMFusion: Adapting Pretrained Language Models for Multimodal Generation | arXiv | 2024/12/19 | - | - |
| MonoFormer | MonoFormer: One Transformer for Both Diffusion and Autoregression | arXiv | 2024/09/24 | Github | - |
| Show-o | Show-o: One Single Transformer to Unify Multimodal Understanding and Generation | ICLR | 2024/08/22 | Github | Demo |
| Transfusion | Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model | ICLR | 2024/08/20 | Github | - |
c-2: Hybrid Encoding (Pseduo)
| Name | Title | Venue | Date | Code | Demo |
|---|---|---|---|---|---|
| EMMA | EMMA: Efficient Multimodal Understanding, Generation, and Editing with a Unified Architecture | arXiv | 2025/12/15 | Github | - |
| HBridge | HBridge: H-Shape Bridging of Heterogeneous Experts for Unified Multimodal Understanding and Generation | arXiv | 2025/11/25 | - | - |
| LightFusion | LightFusion: A Light-weighted, Double Fusion Framework for Unified Multimodal Understanding and Generation | arXiv | 2025/10/27 | Github | - |
| BAGEL | Emerging Properties in Unified Multimodal Pretraining | arXiv | 2025/05/20 | Github | Demo |
| Mogao | Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation | arXiv | 2025/05/08 | - | - |
| JanusFlow | JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation | arXiv | 2024/11/12 | Github | Demo |