A Comprehensive Repository for Multi-modal, Generative, and Perceptual Models
| Quick Start | Contributing | License | Citation |
- [2025.12.08] 😊 We provide a refactored version of the Cosmos-Predict2.5 and Cosmos-Transfer2.5 training and inference code, which makes the code more concise and readable and easier to use.
- [2025.11.27] 🎉 We released GigaBrain-0, a novel VLA foundation model empowered by world model-generated data.
- [2025.11.26] 🎉 We released GigaWorld-0, a unified world model framework designed explicitly as a data engine to advance embodied AI.
- [2025.10.29] 😊 We provided a clean PyTorch reproduction of Pi0 and Pi0.5. It supports end-to-end training and inference with better performance and lightweight deployment.
- [2025.10.29] 🎉 We released GigaModels.
GigaModels is an open-source project offering an intuitive, high-performance infrastructure for a wide range of models. This comprehensive toolkit empowers users throughout the entire workflow, from training and inference to deployment and model compression.
We are dedicated to continuously integrating the latest advancements in open-source technology. Exciting updates and innovative features are always on the horizon—stay tuned!
GigaModels can be installed directly from source for the latest updates:
conda create -n giga_models python=3.11.10 conda activate giga_models git clone https://github.com/open-gigaai/giga-models.git cd giga-models pip3 install -e .GigaModels is designed to be very simple to use. You can easily load and utilize the model using load_pipeline or XXPipeline. Here is an example of how to use:
# Load the Grounding DINO model with load_pipeline from PIL import Image from giga_models import load_pipeline image = Image.open(image_path) pipe = load_pipeline('detection/grounding_dino/swint_ogc') pred_boxes, pred_labels, pred_scores = pipe(image, det_labels) # Load the Depth Anything model with DepthAnythingPipeline from giga_models import DepthAnythingPipeline pipe = DepthAnythingPipeline('depth-anything/Depth-Anything-V2-Large-hf').to('cuda') depth_image = pipe(image)More details on using GigaModels can be found in the projects folder.
| Task | Pipeline | Inference | Training | |
|---|---|---|---|---|
| VLA | VLA | |||
| Diffusion | Diffusion | |||
| Vision | Depth Estimation | |||
| Detection | ||||
| Edge Detection | ||||
| Frame Interpolation | ||||
| Image Restoration | ||||
| Keypoints | ||||
| Optical Flow | ||||
| Segmentation | ||||
| Shot Boundary Detection |
We welcome contributions! Please see our Contributing Guide for details.
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
@misc{gigaai2025gigamodels, author = {GigaAI}, title = {GigaModels: A Comprehensive Repository for Multi-modal, Generative, and Perceptual Models}, year = {2025}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/open-gigaai/giga-models}} }