💡 I also have other video-language projects that may interest you ✨.
LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment
Bin Zhu, Bin Lin, Munan Ning, Yang Yan, Jiaxi Cui, HongFa Wang, Yatian Pang, Wenhao Jiang, Junwu Zhang, Zongwei Li, Wancai Zhang, Zhifeng Li, Wei Liu, Li Yuan
Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models
Munan Ning, Bin Zhu, Yujia Xie, Bin Lin, Jiaxi Cui, Lu Yuan, Dongdong Chen, Li Yuan
Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
Peng Jin, Ryuichi Takanobu, Caiwan Zhang, Xiaochun Cao, Li Yuan
- [2023.11.30] 🤝Thanks to the generous contributions of the community, the OpenXLab's demo is now accessible.
- [2023.11.23] We are training a new and powerful model that supports more frames !
- [2023.11.21] 🤝Check out the replicate demo, created by @nateraw, who has generously supported our research!
- [2023.11.20] 🤗Hugging Face demo and all codes & datasets are available now! Welcome to watch 👀 this repository for the latest updates.
Video-LLaVA exhibits remarkable interactive capabilities between images and videos, despite the absence of image-video pairs in the dataset.
- With the binding of unified visual representations to the language feature space, we enable an LLM to perform visual reasoning capabilities on both images and videos simultaneously.
- Extensive experiments demonstrate the complementarity of modalities, showcasing significant superiority when compared to models specifically designed for either images or videos.
- Gradio Web UI
Highly recommend trying out our web demo by the following command, which incorporates all features currently supported by Video-LLaVA. We also provide online demo in Huggingface Spaces.
python -m llava.serve.gradio_web_serverdemo.mp4
- CLI Inference
python -m llava.serve.cli --model-path "LanguageBind/Video-LLaVA-7B" --video-file "path/to/your/video.mp4" --load-4bit
python -m llava.serve.cli --model-path "LanguageBind/Video-LLaVA-7B" --image-file "path/to/your/image.jpg" --load-4bit
- Python >= 3.10
- Pytorch == 2.0.1
- CUDA Version >= 11.7
- Install required packages:
git clone https://github.com/PKU-YuanGroup/Video-LLaVA
cd Video-LLaVA
conda create -n videollava python=3.10 -y
conda activate videollava
pip install --upgrade pip # enable PEP 660 support
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@28fe037d212663c6a24f373b94cc5d478c8c1a1dWe open source all codes. If you want to load the model (e.g. LanguageBind/Video-LLaVA-7B) on local, you can use the following code snippets.
import torch
from llava.constants import X_TOKEN_INDEX, DEFAULT_X_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import tokenizer_X_token, get_model_name_from_path, KeywordsStoppingCriteria
def main():
disable_torch_init()
image = 'llava/serve/examples/extreme_ironing.jpg'
inp = 'What is unusual about this image?'
model_path = 'LanguageBind/Video-LLaVA-7B'
device = 'cuda'
load_4bit, load_8bit = True, False
model_name = get_model_name_from_path(model_path)
tokenizer, model, processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit, load_4bit, device=device)
image_processor = processor['image']
conv_mode = "llava_v1"
conv = conv_templates[conv_mode].copy()
roles = conv.roles
image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values']
if type(image_tensor) is list:
tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor]
else:
tensor = image_tensor.to(model.device, dtype=torch.float16)
key = ['image']
print(f"{roles[1]}: {inp}")
inp = DEFAULT_X_TOKEN['IMAGE'] + '\n' + inp
conv.append_message(conv.roles[0], inp)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['IMAGE'], return_tensors='pt').unsqueeze(0).cuda()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=[tensor, key],
do_sample=True,
temperature=0.2,
max_new_tokens=1024,
use_cache=True,
stopping_criteria=[stopping_criteria])
outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
print(outputs)
if __name__ == '__main__':
main()import torch
from llava.constants import X_TOKEN_INDEX, DEFAULT_X_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import tokenizer_X_token, get_model_name_from_path, KeywordsStoppingCriteria
def main():
disable_torch_init()
video = 'llava/serve/examples/sample_demo_1.mp4'
inp = 'Why is this video funny?'
model_path = 'LanguageBind/Video-LLaVA-7B'
device = 'cuda'
load_4bit, load_8bit = True, False
model_name = get_model_name_from_path(model_path)
tokenizer, model, processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit, load_4bit, device=device)
video_processor = processor['video']
conv_mode = "llava_v1"
conv = conv_templates[conv_mode].copy()
roles = conv.roles
video_tensor = video_processor(video, return_tensors='pt')['pixel_values']
if type(video_tensor) is list:
tensor = [video.to(model.device, dtype=torch.float16) for video in video_tensor]
else:
tensor = video_tensor.to(model.device, dtype=torch.float16)
key = ['video']
print(f"{roles[1]}: {inp}")
inp = DEFAULT_X_TOKEN['VIDEO'] + '\n' + inp
conv.append_message(conv.roles[0], inp)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['VIDEO'], return_tensors='pt').unsqueeze(0).cuda()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=[tensor, key],
do_sample=True,
temperature=0.1,
max_new_tokens=1024,
use_cache=True,
stopping_criteria=[stopping_criteria])
outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
print(outputs)
if __name__ == '__main__':
main()The training & validating instruction is in TRAIN_AND_VALIDATE.md.
- LLaVA The codebase we built upon and it is an efficient large language and vision assistant.
- Video-ChatGPT Great job contributing the evaluation code and dataset.
- LanguageBind An open source five modalities language-based retrieval framework.
- Chat-UniVi This framework empowers the model to efficiently utilize a limited number of visual tokens.
- 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 📝.
@misc{lin2023videollava,
title={Video-LLaVA: Learning United Visual Representation by Alignment Before Projection},
author={Bin Lin and Yang Ye and Bin Zhu and Jiaxi Cui and Munan Ning and Peng Jin and Li Yuan},
year={2023},
eprint={2311.10122},
archivePrefix={arXiv},
primaryClass={cs.CV}
}@misc{zhu2023languagebind,
title={LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment},
author={Bin Zhu and Bin Lin and Munan Ning and Yang Yan and Jiaxi Cui and HongFa Wang and Yatian Pang and Wenhao Jiang and Junwu Zhang and Zongwei Li and Wancai Zhang and Zhifeng Li and Wei Liu and Li Yuan},
year={2023},
eprint={2310.01852},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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