Install

OmniParser runs locally. There is no pip-installable package for the full system. You clone the repo, create a conda environment, install Python dependencies, and download model weights from HuggingFace.

Prerequisites

• Python 3.12 (via conda)
• Anaconda or Miniconda
• Hugging Face CLI (pip install huggingface_hub)
• A GPU is strongly recommended for inference speed. CPU-only inference works but is slow.

Step 1: Clone the repository

git clone https://github.com/microsoft/OmniParser.git
cd OmniParser

Step 2: Create and activate the conda environment

conda create -n "omni" python==3.12
conda activate omni

Step 3: Install Python dependencies

pip install -r requirements.txt

Step 4: Download model weights

Download the V2 checkpoints from HuggingFace into a local weights/ folder:

for f in icon_detect/{train_args.yaml,model.pt,model.yaml} icon_caption/{config.json,generation_config.json,model.safetensors}; do
  huggingface-cli download microsoft/OmniParser-v2.0 "$f" --local-dir weights
done
mv weights/icon_caption weights/icon_caption_florence

After this step your weights/ directory should contain:

weights/
  icon_detect/
    model.pt
    model.yaml
    train_args.yaml
  icon_caption_florence/
    config.json
    generation_config.json
    model.safetensors

No downloadable template or starter

OmniParser does not ship a project boilerplate or starter template. The canonical entry points are the demo.ipynb notebook and gradio_demo.py inside the cloned repo.