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[Paper](https://arxiv.org/pdf/2311.18799.pdf), [Project Page](https://github.com/salesforce/LAVIS/tree/main/projects/xinstructblip), [Website](https://artemisp.github.io/X-InstructBLIP-page/), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/projects/xinstructblip/demo/run_demo.ipynb)
> A simple, yet effective, cross-modality framework built atop frozen LLMs that allows the integration of various modalities (image, video, audio, 3D) without extensive modality-specific customization.
* [Model Release] July 2023, released implementation of **BLIP-Diffusion**
- [Paper](https://arxiv.org/abs/2305.06500), [Project Page](https://github.com/salesforce/LAVIS/tree/main/projects/blip-diffusion), [Website](https://dxli94.github.io/BLIP-Diffusion-website/)
+ [Paper](https://arxiv.org/abs/2305.14720), [Project Page](https://github.com/salesforce/LAVIS/tree/main/projects/blip-diffusion), [Website](https://dxli94.github.io/BLIP-Diffusion-website/)
> A text-to-image generation model that trains 20x than DreamBooth. Also facilitates zero-shot subject-driven generation and editing.
* [Model Release] May 2023, released implementation of **InstructBLIP**
[Paper](https://arxiv.org/abs/2305.06500), [Project Page](https://github.com/salesforce/LAVIS/tree/main/projects/instructblip)