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--- |
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tags: |
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- text-to-video |
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duplicated_from: cerspense/zeroscope_v2_576w |
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--- |
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# LLM-grounded Video Diffusion Models |
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[Long Lian](https://tonylian.com/), [Baifeng Shi](https://bfshi.github.io/), [Adam Yala](https://www.adamyala.org/), [Trevor Darrell](https://people.eecs.berkeley.edu/~trevor/), [Boyi Li](https://sites.google.com/site/boyilics/home) at UC Berkeley/UCSF. **ICLR 2024**. |
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[Project Page](https://llm-grounded-video-diffusion.github.io/) | [Related Project: LMD](https://llm-grounded-diffusion.github.io/) | [Citation](https://llm-grounded-video-diffusion.github.io/#citation) |
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This model is based on [zeroscope](https://huggingface.co/cerspense/zeroscope_v2_576w) but with additional conditioning from bounding boxes in a [GLIGEN](https://gligen.github.io/) fashion. |
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Similar to [LLM-grounded Diffusion (LMD)](https://llm-grounded-diffusion.github.io/), LLM-grounded Video Diffusion (LVD)'s boxes-to-video stage allows cross-attention-based bounding box conditioning, which uses Zeroscope off-the-shelf. This huggingface model offers an alternative: we train a GLIGEN model (i.e., transformer adapters) with Zeroscope's weights without the temporal transformers blocks on [SA-1B](https://ai.meta.com/datasets/segment-anything/), treating it as a SD v2.1 model that has been fine-tuned to 256x256 resolution. We then merge the adapters into Zeroscope to offer conditioning. The resulting model is in this hugginface model. This can be used with cross-attention-based conditioning or on its own, similar to [LMD+](https://github.com/TonyLianLong/LLM-groundedDiffusion). This can be used with LLM-based text-to-dynamic scene layout generator in LVD, or on its own as a video version of GLIGEN. |
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## Citation (LVD) |
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If you use our work, model, or our implementation in this repo, or find them helpful, please consider giving a citation. |
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``` |
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@article{lian2023llmgroundedvideo, |
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title={LLM-grounded Video Diffusion Models}, |
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author={Lian, Long and Shi, Baifeng and Yala, Adam and Darrell, Trevor and Li, Boyi}, |
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journal={arXiv preprint arXiv:2309.17444}, |
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year={2023}, |
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} |
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@article{lian2023llmgrounded, |
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title={LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models}, |
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author={Lian, Long and Li, Boyi and Yala, Adam and Darrell, Trevor}, |
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journal={arXiv preprint arXiv:2305.13655}, |
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year={2023} |
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} |
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``` |
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## Citation (GLIGEN) |
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The adapters in this model are trained in a mannar similar to training GLIGEN adapters. |
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``` |
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@article{li2023gligen, |
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title={GLIGEN: Open-Set Grounded Text-to-Image Generation}, |
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author={Li, Yuheng and Liu, Haotian and Wu, Qingyang and Mu, Fangzhou and Yang, Jianwei and Gao, Jianfeng and Li, Chunyuan and Lee, Yong Jae}, |
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journal={CVPR}, |
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year={2023} |
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} |
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``` |
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## Citation (ModelScope) |
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ModelScope is LVD's base model. |
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``` |
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@article{wang2023modelscope, |
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title={Modelscope text-to-video technical report}, |
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author={Wang, Jiuniu and Yuan, Hangjie and Chen, Dayou and Zhang, Yingya and Wang, Xiang and Zhang, Shiwei}, |
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journal={arXiv preprint arXiv:2308.06571}, |
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year={2023} |
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} |
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@InProceedings{VideoFusion, |
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author = {Luo, Zhengxiong and Chen, Dayou and Zhang, Yingya and Huang, Yan and Wang, Liang and Shen, Yujun and Zhao, Deli and Zhou, Jingren and Tan, Tieniu}, |
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title = {VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation}, |
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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month = {June}, |
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year = {2023} |
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} |
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``` |
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## LICENSE |
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Zeroscope follows CC-BY-NC 4.0 license. The gligen adapters are trained on SA-1B, which follows [SA-1B license](https://ai.meta.com/datasets/segment-anything/). |
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