base on 【EMNLP 2024🔥】Video-LLaVA: Learning United Visual Representation by Alignment Before Projection <p align="center"> <img src="https://z1.ax1x.com/2023/11/07/pil4sqH.png" width="150" style="margin-bottom: 0.2;"/> <p> <h2 align="center"> <a href="https://arxiv.org/abs/2311.10122">Video-LLaVA: Learning United Visual Representation by Alignment Before Projection</a></h2> <h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2> <h5 align="center"> [](https://huggingface.co/spaces/LanguageBind/Video-LLaVA) [](https://openxlab.org.cn/apps/detail/jiaxicui/Video-LLaVA) [](https://modelscope.cn/studios/PKU-YuanLab/Video-LLaVA) [](https://replicate.com/nateraw/video-llava) [](https://arxiv.org/abs/2311.10122) <br> [](https://github.com/PKU-YuanGroup/Video-LLaVA/blob/main/LICENSE) [](https://hits.seeyoufarm.com) [](https://github.com/PKU-YuanGroup/Video-LLaVA/issues?q=is%3Aopen+is%3Aissue) [](https://github.com/PKU-YuanGroup/Video-LLaVA/issues?q=is%3Aissue+is%3Aclosed) <br> [](https://twitter.com/_nateraw/status/1726783481248977037) [](https://twitter.com/arankomatsuzaki/status/1726421417963516144) [](https://twitter.com/jesselaunz/status/1726850138776453379) [](https://mp.weixin.qq.com/s/EFqLv_Euf5VU024zOtzkkg) [](https://mp.weixin.qq.com/s/uwaxMu8UbJpcLTXsNJwpVQ) [](https://zhuanlan.zhihu.com/p/668166885) [](https://www.youtube.com/watch?v=EFkN00rGq1U&ab_channel=JesseLau-aTrader) <!--[](https://zhuanlan.zhihu.com/p/668166885)--> </h5> [](https://paperswithcode.com/sota/zeroshot-video-question-answer-on-msrvtt-qa?p=video-llava-learning-united-visual-1) <br> [](https://paperswithcode.com/sota/zeroshot-video-question-answer-on-msvd-qa?p=video-llava-learning-united-visual-1) <br> [](https://paperswithcode.com/sota/zeroshot-video-question-answer-on-tgif-qa?p=video-llava-learning-united-visual-1) <br> <details open><summary>💡 I also have other video-language projects that may interest you ✨. </summary><p> <!-- may --> > [**Open-Sora Plan: Open-Source Large Video Generation Model**](https://arxiv.org/abs/2412.00131) <br> > Bin Lin and Yunyang Ge and Xinhua Cheng and Zongjian Li and Bin Zhu and Shaodong Wang and Xianyi He and Yang Ye and Shenghai Yuan and Liuhan Chen and Tanghui Jia and Junwu Zhang and Zhenyu Tang and Yatian Pang and Bin She and Cen Yan and Zhiheng Hu and Xiaoyi Dong and Lin Chen and Zhang Pan and Xing Zhou and Shaoling Dong and Yonghong Tian and Li Yuan <br> [](https://github.com/PKU-YuanGroup/Open-Sora-Plan) [](https://github.com/PKU-YuanGroup/Open-Sora-Plan) [](https://arxiv.org/abs/2412.00131) <br> > [**MoE-LLaVA: Mixture of Experts for Large Vision-Language Models**](https://arxiv.org/abs/2401.15947) <br> > Bin Lin, Zhenyu Tang, Yang Ye, Jiaxi Cui, Bin Zhu, Peng Jin, Junwu Zhang, Munan Ning, Li Yuan <br> [](https://github.com/PKU-YuanGroup/MoE-LLaVA) [](https://github.com/PKU-YuanGroup/MoE-LLaVA) [](https://arxiv.org/abs/2401.15947) <br> > [**LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment**](https://arxiv.org/abs/2310.01852) <br> > 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 <br> [](https://github.com/PKU-YuanGroup/LanguageBind) [](https://github.com/PKU-YuanGroup/LanguageBind) [](https://arxiv.org/abs/2310.01852) <br> <!-- > [**Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models**](https://arxiv.org/abs/2311.08046) <br> > Munan Ning, Bin Zhu, Yujia Xie, Bin Lin, Jiaxi Cui, Lu Yuan, Dongdong Chen, Li Yuan <br> [](https://github.com/PKU-YuanGroup/Video-Bench) [](https://github.com/PKU-YuanGroup/Video-Bench) [](https://arxiv.org/abs/2311.16103) <br> --> </p></details> ## 📰 News * **[2024.09.25]** 🔥🔥🔥 Our Video-LLaVA has been accepted at EMNLP 2024! We earn the meta score of 4. * **[2024.07.27]** 🔥🔥🔥 A fine-tuned [Video-LLaVA](https://github.com/mfarre/Video-LLaVA-7B-hf-CinePile) focuses on theme exploration, narrative analysis, and character dynamics. Thanks to [@micuelll](https://x.com/micuelll/status/1816851392134586540). , CinePile addresses these overlooked areas with fine-tuning Video-LLaVA in their benchmark. * **[2024.05.15]** 🤝🤝🤝 Thanks to the generous contributions of [@zucchini-nlp](https://github.com/zucchini-nlp), Video-LLaVa now available in the Transformers library! More details [here](https://github.com/PKU-YuanGroup/Video-LLaVA/issues/156). * **[2024.01.27]** 👀👀👀 Our [MoE-LLaVA](https://github.com/PKU-YuanGroup/MoE-LLaVA) is released! A sparse model with 3B parameters outperformed the dense model with 7B parameters. * **[2024.01.17]** 🔥🔥🔥 Our [LanguageBind](https://github.com/PKU-YuanGroup/LanguageBind) has been accepted at ICLR 2024! * **[2024.01.16]** 🔥🔥🔥 We reorganize the code and support LoRA fine-tuning, checking [finetune_lora.sh](scripts/v1_5/finetune_lora.sh). * **[2023.11.30]** 🤝 Thanks to the generous contributions of the community, the [OpenXLab's demo](https://openxlab.org.cn/apps/detail/houshaowei/Video-LLaVA) is now accessible. * **[2023.11.23]** We are training a new and powerful model. * **[2023.11.21]** 🤝 Check out the [replicate demo](https://replicate.com/nateraw/video-llava), created by [@nateraw](https://github.com/nateraw), who has generously supported our research! * **[2023.11.20]** 🤗 [Hugging Face demo](https://huggingface.co/spaces/LanguageBind/Video-LLaVA) and **all codes & datasets** are available now! Welcome to **watch** 👀 this repository for the latest updates. ## 😮 Highlights Video-LLaVA exhibits remarkable interactive capabilities between images and videos, despite the absence of image-video pairs in the dataset. ### 💡 Simple baseline, learning united visual representation by alignment before projection - 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. ### 🔥 High performance, complementary learning with video and image - Extensive experiments demonstrate **the complementarity of modalities**, showcasing significant superiority when compared to models specifically designed for either images or videos. <img src="assets/main.jpg"/> ## 🤗 Demo ### 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](https://huggingface.co/spaces/LanguageBind/Video-LLaVA) in Huggingface Spaces. ```bash python -m videollava.serve.gradio_web_server ``` https://github.com/PKU-YuanGroup/Video-LLaVA/assets/62638829/71ab15ac-105e-4b18-b0b5-e1b35d70607b ### CLI Inference ```bash CUDA_VISIBLE_DEVICES=0 python -m videollava.serve.cli --model-path "LanguageBind/Video-LLaVA-7B" --file "path/to/your/video.mp4" --load-4bit ``` <img src="assets/videocli.gif" width="500" /> ```bash CUDA_VISIBLE_DEVICES=0 python -m videollava.serve.cli --model-path "LanguageBind/Video-LLaVA-7B" --file "path/to/your/image.jpg" --load-4bit ``` <img src="assets/imagecli.gif" width="500" /> ## 🚀 Main Results ### Image understanding <p align="left"> <img src="assets/res_img.jpg" width=80%> </p> ### Video understanding <p align="left"> <img src="assets/res_vi.jpg" width=80%> </p> ## 🛠️ Requirements and Installation * Python >= 3.10 * Pytorch == 2.0.1 * CUDA Version >= 11.7 * Install required packages: ```bash 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@28fe037d212663c6a24f373b94cc5d478c8c1a1d ``` ## 🤖 API > [!Warning] > <div align="left"> > <b> > 🚨 Upgrade transformers for quick access. > </b> > </div> ``` pip install -U transformers ``` If you need to install `av` then do ``` python -m pip install av ``` ``` import av import numpy as np from transformers import VideoLlavaProcessor, VideoLlavaForConditionalGeneration def read_video_pyav(container, indices): frames = [] container.seek(0) start_index = indices[0] end_index = indices[-1] for i, frame in enumerate(container.decode(video=0)): if i > end_index: break if i >= start_index and i in indices: frames.append(frame) return np.stack([x.to_ndarray(format="rgb24") for x in frames]) model = VideoLlavaForConditionalGeneration.from_pretrained("LanguageBind/Video-LLaVA-7B-hf") processor = VideoLlavaProcessor.from_pretrained("LanguageBind/Video-LLaVA-7B-hf") prompt = "USER: <video>Why is this video funny? ASSISTANT:" video_path = "YOUR-LOCAL-VIDEO-PATH" container = av.open(video_path) # sample uniformly 8 frames from the video total_frames = container.streams.video[0].frames indices = np.arange(0, total_frames, total_frames / 8).astype(int) clip = read_video_pyav(container, indices) inputs = processor(text=prompt, videos=clip, return_tensors="pt") # Generate generate_ids = model.generate(**inputs, max_length=80) print(processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]) >>> 'USER: Why is this video funny? ASSISTANT: The video is funny because the baby is sitting on the bed and reading a book, which is an unusual and amusing sight.' ``` <details> <summary>outdated</summary> **We 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. ### Inference for image ```python import torch from videollava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN from videollava.conversation import conv_templates, SeparatorStyle from videollava.model.builder import load_pretrained_model from videollava.utils import disable_torch_init from videollava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria def main(): disable_torch_init() image = 'videollava/serve/examples/extreme_ironing.jpg' inp = 'What is unusual about this image?' model_path = 'LanguageBind/Video-LLaVA-7B' cache_dir = 'cache_dir' device = 'cuda' load_4bit, load_8bit = True, False model_name = get_model_name_from_path(model_path) tokenizer, model, processor, _ = load_pretrained_model(model_path, None, model_name, load_8bit, load_4bit, device=device, cache_dir=cache_dir) 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) print(f"{roles[1]}: {inp}") inp = DEFAULT_IMAGE_TOKEN + '\n' + inp conv.append_message(conv.roles[0], inp) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, 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, 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() ``` ### Inference for video ```python import torch from videollava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN from videollava.conversation import conv_templates, SeparatorStyle from videollava.model.builder import load_pretrained_model from videollava.utils import disable_torch_init from videollava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria def main(): disable_torch_init() video = 'videollava/serve/examples/sample_demo_1.mp4' inp = 'Why is this video funny?' model_path = 'LanguageBind/Video-LLaVA-7B' cache_dir = 'cache_dir' device = 'cuda' load_4bit, load_8bit = True, False model_name = get_model_name_from_path(model_path) tokenizer, model, processor, _ = load_pretrained_model(model_path, None, model_name, load_8bit, load_4bit, device=device, cache_dir=cache_dir) 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) print(f"{roles[1]}: {inp}") inp = ' '.join([DEFAULT_IMAGE_TOKEN] * model.get_video_tower().config.num_frames) + '\n' + inp conv.append_message(conv.roles[0], inp) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, 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, 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() ``` </details> ## 🗝️ Training & Validating The training & validating instruction is in [TRAIN_AND_VALIDATE.md](TRAIN_AND_VALIDATE.md). ## 👍 Acknowledgement * [LLaVA](https://github.com/haotian-liu/LLaVA) The codebase we built upon and it is an efficient large language and vision assistant. * [Video-ChatGPT](https://github.com/mbzuai-oryx/Video-ChatGPT) Great job contributing the evaluation code and dataset. ## 🙌 Related Projects * [LanguageBind](https://github.com/PKU-YuanGroup/LanguageBind) An open source five modalities language-based retrieval framework. * [Chat-UniVi](https://github.com/PKU-YuanGroup/Chat-UniVi) This framework empowers the model to efficiently utilize a limited number of visual tokens. ## 🔒 License * The majority of this project is released under the Apache 2.0 license as found in the [LICENSE](https://github.com/PKU-YuanGroup/Video-LLaVA/blob/main/LICENSE) file. * The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation. ## ✏️ Citation If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:. ```BibTeX @article{lin2023video, title={Video-LLaVA: Learning United Visual Representation by Alignment Before Projection}, author={Lin, Bin and Zhu, Bin and Ye, Yang and Ning, Munan and Jin, Peng and Yuan, Li}, journal={arXiv preprint arXiv:2311.10122}, year={2023} } ``` ```BibTeX @article{zhu2023languagebind, title={LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment}, author={Zhu, Bin and Lin, Bin and Ning, Munan and Yan, Yang and Cui, Jiaxi and Wang, HongFa and Pang, Yatian and Jiang, Wenhao and Zhang, Junwu and Li, Zongwei and others}, journal={arXiv preprint arXiv:2310.01852}, year={2023} } ``` <!----> ## ✨ Star History [](https://star-history.com/#PKU-YuanGroup/Video-LLaVA&Date) ## 🤝 Contributors <a href="https://github.com/PKU-YuanGroup/Video-LLaVA/graphs/contributors"> <img src="https://contrib.rocks/image?repo=PKU-YuanGroup/Video-LLaVA" /> </a> ", Assign "at most 3 tags" to the expected json: {"id":"5183","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"