Giga-World-1

Giga-World-1

<p align="center"> <img src="assets/main_page.png" width="100%" alt="Giga-World-1 Main Page" /> </p>

Directory Structure

model/
├── README.md
├── assets/
│   └── main_page.png
├── before_stage1/                       # Base checkpoints before stage-1 training
│   ├── Wan2p1_1p3B-FunContro-GigaRobo-alpha-diffusers/
│   ├── Wan2p1_1p3B-FunControl-diffusers/
│   └── Wan2p2_5B-FunControl-diffusers/
├── stage1/                              # Stage-1 fine-tuned checkpoints
│   ├── nano/                            # small (1.3B) variant
│   └── pro/                             # large (5B)   variant
└── stage2_distill/                      # Stage-2 distilled checkpoints
    └── ...

Training pipeline overview

<p align="center"> <img src="assets/training_pipeline.svg" width="100%" alt="Giga-World-1 training pipeline" /> </p>

Model Overview

Stage-1 checkpoint structure

Each Stage-1 variant (nano / pro) contains two released artifacts: a full Diffusers-format checkpoint and a scene LoRA checkpoint.

stage1/{nano,pro}/
├── Giga-World-1-*-stage1_final-diffusers/         # full Diffusers checkpoint
│   ├── model_index.json                           # Diffusers pipeline index
│   ├── transformer/                               # DiT / video transformer weights
│   ├── vae/                                       # VAE weights
│   ├── text_encoder/                              # text encoder weights
│   ├── tokenizer/                                 # tokenizer files
│   ├── scheduler/                                 # scheduler config
│   ├── image_encoder/                             # image encoder weights
│   └── image_processor/                           # image preprocessing config
└── Giga-World-1-*-stage1_scene_lora/              # scene LoRA checkpoint
    ├── pytorch_lora_weights.safetensors           # LoRA weights for inference
    ├── transformer_full/                          # full transformer export
    ├── transformer_partial.pth                    # partial transformer checkpoint
    ├── pytorch_model/                             # training checkpoint shards
    ├── distributed_checkpoint/                    # distributed training checkpoint
    ├── scheduler.bin                              # training scheduler state
    ├── latest                                     # latest checkpoint pointer
    ├── zero_to_fp32.py                            # ZeRO checkpoint conversion script
    └── random_states_*.pkl                        # training random states

Quick Start

Hugging Face Repository

https://huggingface.co/GigaAI-Research/Giga-World-1

SDK Download

# Install Hugging Face Hub
pip install huggingface_hub

# Download the model snapshot via Hugging Face Hub
from huggingface_hub import snapshot_download

model_dir = snapshot_download(repo_id='GigaAI-Research/Giga-World-1')

Git Download

git lfs install
git clone https://huggingface.co/GigaAI-Research/Giga-World-1

Acknowledgements

We sincerely thank the open-source community and the projects that make this work possible.

<p align="center"> <a href="https://github.com/huggingface/diffusers"> <img src="https://img.shields.io/badge/Diffusers-Hugging%20Face-FFD21E?logo=huggingface&logoColor=black" alt="Diffusers" /> </a> <a href="https://github.com/huggingface/lerobot"> <img src="https://img.shields.io/badge/LeRobot-Hugging%20Face-FFD21E?logo=huggingface&logoColor=black" alt="LeRobot" /> </a> <a href="https://huggingface.co/"> <img src="https://img.shields.io/badge/Hugging%20Face-Models-FFD21E?logo=huggingface&logoColor=black" alt="Hugging Face" /> </a> <a href="https://modelscope.cn/"> <img src="https://img.shields.io/badge/ModelScope-Community-624AFF" alt="ModelScope" /> </a> <a href="https://pytorch.org/"> <img src="https://img.shields.io/badge/PyTorch-Framework-EE4C2C?logo=pytorch&logoColor=white" alt="PyTorch" /> </a> </p>

Thanks also to many other open-source contributors for their tools, models, and community support.

License

This model is released under the Apache License 2.0 unless otherwise specified.