VARGPT is a multimodal large language model designed for unified visual understanding and generation tasks, targeting researchers and developers working with vision-language models. It offers capabilities in image captioning, visual question answering, and text-to-image generation within a single autoregressive framework.

How It Works

VARGPT models understanding and generation as distinct paradigms within a unified architecture. For understanding, it predicts the next token, similar to standard LLMs. For generation, it predicts the next scale, enabling text-to-image synthesis. This dual approach is achieved through a three-stage instruction tuning process.

Quick Start & Requirements

• Install dependencies: pip3 install -r requirements.txt
• Requires PyTorch, Transformers, and other libraries listed in requirements.txt.
• Inference code is available for both understanding and generation tasks.
• Official models and datasets are hosted on Hugging Face.
• See Webpage and ArXiv for more details.

Highlighted Details

• Unified model for visual understanding (captioning, VQA) and generation (text-to-image).
• Leverages a three-stage instruction tuning process.
• Supports multimodal generation with prompts like "Please design a drawing of a butterfly on a flower."
• Evaluation can be performed using lmms-eval with provided scripts.

Maintenance & Community

• The project has released VARGPT-v1.1 with updated code and models.
• Future updates and maintenance will primarily occur in the VARGPT-v1.1 repository.
• Heavily based on LLaVA-1.5, VAR, LLaVA-NeXT, and other established projects.

Licensing & Compatibility

• Licensed under the Apache License.
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

• Visual generation capabilities are currently constrained by the ImageNet dataset (1.28M images); future iterations aim to improve data quality and quantity.