Giga-World-1-Toydata

<p align="center"> <img src="assets/main_page.png" width="100%" alt="Giga-World-1 Main Page" /> </p> # Giga-World-1 Example Data This directory provides example assets and toy datasets for testing the GigaWorld-1 inference, data pipeline, and model training workflow. ## Directory Structure ```text example/ ├── infer_assest/ # Inference / rollout assets │ ├── control_video.mp4 │ └── input_image.png ├── toy_datapipeline_dataset/ # Raw LeRobot-format toy dataset │ ├── gt/ │ ├── depth/ │ ├── plucker/ │ ├── sketch/ │ └── labels/ └── toy_train_dataset/ # Model training data ├── nano/ │ ├── dataset_cache.pkl │ └── episode_*.pt └── pro/ ├── dataset_cache.pkl └── episode_*.pt ``` ## Contents ### `infer_assest/` — Inference Assets Assets used for quick testing of the image-to-video inference / rollout pipeline. - `input_image.png`: input image (used as the first frame) for image-to-video generation. - `control_video.mp4`: control video used by the example inference pipelines. ### `toy_datapipeline_dataset/` — Raw LeRobot Dataset A compact robot video dataset shipped in the **raw LeRobot data format**, which can be visualized directly with the [LeRobot](https://github.com/huggingface/lerobot) visualization tools.  Dataset structure: ```text example/toy_datapipeline_dataset/ ├── gt/ # RGB videos (ground truth) │ ├── cam_high/ # head view │ ├── cam_left_wrist/ # left wrist view │ └── cam_right_wrist/ # right wrist view ├── depth/ # Depth Anything V2 outputs │ ├── cam_high/ │ ├── cam_left_wrist/ │ └── cam_right_wrist/ ├── plucker/ # Plücker coordinate control signals (left/right per view) │ ├── episode_000001_left_direction.mp4 │ ├── episode_000001_left_moment.mp4 │ ├── episode_000001_right_direction.mp4 │ └── episode_000001_right_moment.mp4 ├── sketch/ # sketch control signals │ └── cam_high/ └── labels/ ├── data.pkl # per-episode metadata └── config.json ``` Each record in `labels/data.pkl` follows this structure: ```python { "action": List[List[float]], # end-effector / joint actions "data_index": int, "episode_name": str, "cam_high_video_path": str, "cam_left_wrist_video_path": str, "cam_right_wrist_video_path": str, "cam_high_depth_path": str, "cam_left_wrist_depth_path": str, "cam_right_wrist_depth_path": str, "qpos": List[List[float]], # current joint angles "video_height": int, "video_width": int, "video_length": int, "short-prompt": { # from meta/episodes.jsonl "task1": { "start_idx": "0", "end_idx": "299", "description": "put banana into basket" } }, "long-prompt": { # generated by Qwen3-VL on cam_high "long prompt 1": { "start_idx": "0", "end_idx": "299", "caption": "The robot arm reaches toward ..." } } } ``` ### `toy_train_dataset/` — Model Training Data Model training data for the GigaWorld-1 training workflow. This dataset is prepared for directly validating the model training pipeline on a small-scale example. Dataset structure: ```text example/toy_train_dataset/ ├── nano/ # nano-scale toy training split │ ├── dataset_cache.pkl # cached dataset index / metadata for fast loading │ ├── episode_000000_4834c0369d_s000000_e000129_0-129_121_480_1920.pt │ ├── episode_000000_4834c0369d_s000129_e000258_0-129_121_480_1920.pt │ └── ... └── pro/ # pro-scale toy training split ├── dataset_cache.pkl # cached dataset index / metadata for fast loading ├── episode_000000_453e4c570c_s000000_e000129_0-129_121_480_1920.pt ├── episode_000001_7473d389a6_s000000_e000129_0-129_121_480_1920.pt ├── episode_000002_8436313f94_s000000_e000129_0-129_121_480_1920.pt └── ... ``` - `nano/`: a smaller toy training split for quick debugging and smoke tests. - `pro/`: a larger toy training split for validating the full training data loader. - `dataset_cache.pkl`: cached metadata / dataset index used by the training pipeline. - `episode_*.pt`: preprocessed training samples. The filename records the source episode id, segment range, frame range, and spatial resolution. ## Usage Use these files with the main GigaWorld-1 repository scripts. Example paths: ```bash EXAMPLE_ROOT=/path/to/example INFER_ASSETS=$EXAMPLE_ROOT/infer_assest TOY_PIPELINE_DATASET=$EXAMPLE_ROOT/toy_datapipeline_dataset TOY_TRAIN_DATASET=$EXAMPLE_ROOT/toy_train_dataset ``` Refer to the main project README for detailed commands covering data preparation, inference, visualization, and training. ## 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 example data is released under the Apache License 2.0 unless otherwise specified.