Vitron
A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing
Stars: 257
Vitron is a unified pixel-level vision LLM designed for comprehensive understanding, generating, segmenting, and editing static images and dynamic videos. It addresses challenges in existing vision LLMs such as superficial instance-level understanding, lack of unified support for images and videos, and insufficient coverage across various vision tasks. The tool requires Python >= 3.8, Pytorch == 2.1.0, and CUDA Version >= 11.8 for installation. Users can deploy Gradio demo locally and fine-tune their models for specific tasks.
README:
Hao Fei$^{1,2}$, Shengqiong Wu$^{1,2}$, Hanwang Zhang$^{1,3}$, Tat-Seng Chua$^{2}$, Shuicheng Yan$^{1}$
▶ $^{1}$ Skywork AI, Singapore ▶ $^{2}$ National University of Singapore ▶ $^{3}$ Nanyang Technological University
-
[2024.07.19] We release the Dataset constructed for
Output-side Invocation-oriented Instruction Tuning
. - [2024.06.28] 🤗 We release the checkpoint, refer to README for more details.
- [2024.04.04] 👀👀👀 Our Vitron is available now! Welcome to watch 👀 this repository for the latest updates.
Existing vision LLMs might still encounter challenges such as superficial instance-level understanding, lack of unified support for both images and videos, and insufficient coverage across various vision tasks. To fill the gaps, we present Vitron, a universal pixel-level vision LLM, designed for comprehensive understanding (perceiving and reasoning), generating, segmenting (grounding and tracking), editing (inpainting) of both static image and dynamic video content.
- Python >= 3.8
- Pytorch == 2.1.0
- CUDA Version >= 11.8
- Install required packages:
git clone https://github.com/SkyworkAI/Vitron
cd Vitron
conda create -n vitron python=3.10 -y
conda activate vitron
pip install --upgrade pip
pip install -e .
pip install -e ".[train]"
pip install flash-attn --no-build-isolation
pip install decord opencv-python git+https://github.com/facebookresearch/pytorchvideo.git@28fe037d212663c6a24f373b94cc5d478c8c1a1d
🔥🔥🔥 Installation or Running Fails? 🔥🔥🔥
-
When running ffmpeg,
Unknown encoder 'x264'
:- try to re-install ffmpeg:
conda uninstall ffmpeg conda install -c conda-forge ffmpeg # `-c conda-forge` can not omit
-
Fail to install detectron2, try this command:
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
or refer this Website.
-
Error in gradio. As there are a big update in
gradio>=4.0.0
, please make sure install gradio with the same verion inrequirements.txt
. -
Error with deepspeed. If you fine-tune your model, this error occours:
FAILED: cpu_adam.so /usr/bin/ld: cannot find -lcurand
This error is caused by the wrong soft links when installing deepspeed. Please try to the following command to solve the error:
cd ~/miniconda3/envs/vitron/lib ls -al libcurand* # check the links rm libcurand.so # remove the wrong links ln -s libcurand.so.10.3.5.119 libcurand.so # build new links
Double check again:
python from deepspeed.ops.op_builder import CPUAdamBuilder ds_opt_adam = CPUAdamBuilder().load() # if loading successfully, then deepspeed are installed successfully.
.
├── assets
├── checkpoints # saving the pre-trained checkpoints
├── data
├── examples
├── modules # each modules used in our project
│ ├── GLIGEN
│ ├── i2vgen-xl
│ ├── SEEM
│ └── StableVideo
├── scripts
└── vitron
├── model
│ ├── language_model
│ ├── multimodal_encoder
│ ├── multimodal_projector
│ └── region_extractor
└── train
- Firstly, you need to prepare the checkpoint, see README for more details.
- Then, you can run the demo locally via:
python app.py
- Firstly, prepare the dataset.
We release the constructed dataset for
Invocation-oriented Instruction Tuning
. Please refer for the README for more details. - Then, modify the
image/video/data
path in finetune_lora.sh.
JSON_FOLDER=None
IMAGE_FOLDER=None
VIDEO_FOLDER=None
DATA_PATH="./data/data.json"
- Next, prepare the checkpoint.
- Finally, run the code:
bash scripts/fine_lora.sh
You may refer to related work that serves as foundations for our framework and code repository, Vicuna, SEEM, i2vgenxl, StableVideo, and Zeroscope. We also partially draw inspirations from Video-LLaVA, and LanguageBind. Thanks for their wonderful works.
- The majority of this project is released under the Apache 2.0 license as found in the LICENSE file.
- The service is a research preview intended for non-commercial use only, subject to the model License of LLaMA, Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Please contact us if you find any potential violation.
If you find our paper and code useful in your research, please consider giving a star ⭐ and citation 📝.
@articles{hao2024vitron,
title={Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing},
author={Hao Fei, Shengqiong Wu, Hanwang Zhang, Tat-Seng Chua, Shuicheng Yan},
journal={CoRR},
year={2024}
}
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