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imagebind_LLM

README.md

ImageBind-LLM

News

  • [June 5, 2023] Support 3D point cloud input and image generation output. Release stable-version checkpoint.
  • [June 2, 2023] Release fine-tuning code and beta-version checkpoint.
  • [May 29, 2023] Initial release.

Setup

  • Setup up a new conda env. Install ImageBind and other necessary packages.

    # create conda env
conda create -n imagebind_LLM python=3.9 -y
conda activate imagebind_LLM
# install ImageBind
cd ImageBind
pip install -r requirements.txt
# install other dependencies
cd ../
pip install -r requirements.txt

  • Obtain the LLaMA backbone weights using this form. Please note that checkpoints from unofficial sources (e.g., BitTorrent) may contain malicious code and should be used with care. Organize the downloaded file in the following structure

    /path/to/llama_model_weights
├── 7B
│   ├── checklist.chk
│   ├── consolidated.00.pth
│   └── params.json
└── tokenizer.model

  • The current stable version of ImageBind-LLM is built upon Open-Chinese-LLaMA for better multilingual support. The following command downloads a pre-processed delta-version patch and automatically merges it into LLaMA weights:

    python get_chinese_llama.py --llama_dir=/path/to/llama_model_weights

    After running, the Open-Chinese-LLaMA weights will be recovered in /path/to/llama_model_weights:

    /path/to/llama_model_weights
├── 7B
├── 7B_chinese
└── tokenizer.model

  • Other dependent resources will be automatically downloaded at runtime.

Inference

  • Here is a simple script for multi-modal inference with ImageBind-LLM:

    import ImageBind.data as data
import llama


llama_dir = "/path/to/LLaMA"

# checkpoint will be automatically downloaded
model = llama.load("7B", llama_dir, knn=True)
model.eval()

inputs = {}
image = data.load_and_transform_vision_data(["examples/girl.jpg"], device='cuda')
inputs['Image'] = [image, 1]
audio = data.load_and_transform_audio_data(['examples/girl_bgm.wav'], device='cuda')
inputs['Audio'] = [audio, 1]

results = model.generate(
    inputs,
    [llama.format_prompt("Guess the girl's mood based on the background music and explain the reason?")],
    max_gen_len=256
)
result = results[0].strip()
print(result)

  • Powered by the amazing Point-Bind project, ImageBind-LLM can also receive 3D point cloud data. We provide several point cloud samples in examples/.

    inputs = {}
point = data.load_and_transform_point_cloud_data(["examples/airplane.pt"], device='cuda')
inputs['Point'] = [point, 1]

results = model.generate(
    inputs,
    [llama.format_prompt("Describe the 3D object in detail:")],
    max_gen_len=256
)
result = results[0].strip()
print(result)

Demo

Highly recommend trying out our web demo, which incorporates all features currently supported by ImageBind-LLM

  • Run the following command to host the demo locally:

    python gradio_app.py --llama_dir /path/to/llama_model_weights

  • The official online demo will come very soon

Pre-traininig & Fine-tuning

See train.md

Core Contributors

Peng Gao, Jiaming Han, Chris Liu, Ziyi Lin, Renrui Zhang, Ziyu Guo

Acknowledgement