This repository serves as a comprehensive survey and resource hub for unified multimodal models, targeting researchers and practitioners in AI. It aims to consolidate advances, challenges, and benchmarks in models capable of understanding and generating across various modalities like text, image, and audio, facilitating exploration and development in this rapidly evolving field.

How It Works

The project categorizes unified multimodal models based on their core architectures: Diffusion-based, Autoregressive (MLLM), and Hybrid approaches. It further classifies these by encoding strategies such as Pixel, Semantic, Learnable Query, and Hybrid encoding. This structured approach allows for a clear understanding of the diverse architectural designs and their trade-offs in handling multimodal data.

Quick Start & Requirements

This repository is a curated list of research papers, datasets, and benchmarks. There are no direct installation or execution commands. Requirements are implicit to the individual models and datasets listed, which may include specific hardware (e.g., GPUs), software environments (e.g., Python versions), and large datasets. Links to official documentation, code repositories (GitHub), and demos are provided for each listed resource.

Highlighted Details

• Comprehensive timeline of unified multimodal models, distinguishing between publicly available and unavailable ones.
• Categorized lists of models based on architecture (Diffusion, MLLM, Hybrid) and encoding strategies.
• Extensive collection of benchmarks for evaluating multimodal understanding, image generation, and interleaved tasks.
• Detailed dataset listings covering multimodal understanding, text-to-image synthesis, and image editing.

Maintenance & Community

The project is actively maintained, with a notable citation to a 2025 arXiv paper by Zhang et al. The README includes a hiring call for researchers interested in multimodal AI, with an email contact provided. Further community or roadmap links are not explicitly mentioned.

Licensing & Compatibility

The repository itself is a collection of links and information, not software with a specific license. The licenses of the individual models and datasets referenced within the repository would need to be checked separately.

Limitations & Caveats

As a survey and resource list, the repository does not provide executable code or pre-trained models directly. The rapid pace of development in unified multimodal models means the information may require frequent updates to remain current.