Update README.md

README.md

@@ -7,232 +7,5 @@ license: apache-2.0
     <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>
-
-
-
-
-## 📰 News
-* **[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. 
-
-
-## 🤗 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
-```
-
-
-
-### CLI Inference
-
-```bash
-python -m videollava.serve.cli --model-path "LanguageBind/Video-LLaVA-7B" --file "path/to/your/video.mp4" --load-4bit
-```
-
-```bash
-python -m videollava.serve.cli --model-path "LanguageBind/Video-LLaVA-7B" --file "path/to/your/image.jpg" --load-4bit
-```
-
-
-
-## 🛠️ 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
-**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()
-```
-
-## 🗝️ 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
-[![Star History](https://api.star-history.com/svg?repos=PKU-YuanGroup/Video-LLaVA&type=Date)](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>