| | --- |
| | license: apache-2.0 |
| | library_name: generic |
| | tags: |
| | - motion-generation |
| | - diffusion |
| | - 3d |
| | - humanml3d |
| | - babel |
| | --- |
| | |
| | # FloodDiffusion Downloads |
| |
|
| | This repository contains the datasets, dependencies, and pretrained models for **FloodDiffusion: Tailored Diffusion Forcing for Streaming Motion Generation**. |
| |
|
| | Code repository: [GitHub](https://github.com/ShandaAI/FloodDiffusion) |
| |
|
| | ## Repository Structure |
| |
|
| | The files in this repository are organized to match the directory structure required by FloodDiffusion. |
| |
|
| | ### 1. Model Checkpoints |
| | The `outputs.zip` and `outputs_tiny.zip` archives contains the pretrained model weights. |
| |
|
| | * **Target**: Unzip into your project root. It should create an `outputs/` folder. |
| |
|
| | ``` |
| | outputs/ |
| | βββ vae_1d_z4_step=300000.ckpt # VAE model (1D, z_dim=4) |
| | βββ 20251106_063218_ldf/ |
| | β βββ step_step=50000.ckpt # LDF model checkpoint (HumanML3D) |
| | βββ 20251107_021814_ldf_stream/ |
| | β βββ step_step=240000.ckpt # LDF streaming model checkpoint (BABEL) |
| | βββ 20251217_023720_ldf_tiny/ |
| | β βββ step_step=60000.ckpt # LDF tiny model checkpoint |
| | βββ 20251219_01492_ldf_tiny_stream/ |
| | βββ step_step=200000.ckpt # LDF tiny streaming model checkpoint |
| | ``` |
| |
|
| | ### 2. Datasets |
| | Due to the large number of files, datasets are provided as ZIP archives. |
| |
|
| | * **`HumanML3D.zip`**: Contains the HumanML3D dataset (extracted features and texts). |
| | * **Target**: Unzip into `raw_data/`. It should create `raw_data/HumanML3D/` containing `new_joint_vecs`, `texts`, etc. |
| | * **`BABEL_streamed.zip`**: Contains the BABEL dataset processed for streaming generation. |
| | * **Target**: Unzip into `raw_data/`. It should create `raw_data/BABEL_streamed/`. |
| | |
| | ### 3. Dependencies (`deps.zip`) |
| | * **`deps.zip`**: Contains necessary dependencies like the T5 text encoder, evaluation models (T2M), and GloVe embeddings. |
| | * **Target**: Unzip into your project root. It should create a `deps/` folder. |
| | |
| | ``` |
| | deps/ |
| | βββ t2m/ # Text-to-Motion evaluation models |
| | βββ glove/ # GloVe word embeddings |
| | βββ t5_umt5-xxl-enc-bf16/ # T5 text encoder |
| | ``` |
| | |
| | ## How to Download & Setup |
| | |
| | We recommend using the python script below to automatically download and place files in the correct structure. |
| | |
| | ### Python Script (Recommended) |
| | |
| | Save this as `download_assets.py` in your `FloodDiffusion` project root: |
| | |
| | ```python |
| | from huggingface_hub import hf_hub_download |
| | import zipfile |
| | import os |
| | |
| | REPO_ID = "ShandaAI/FloodDiffusionDownloads" |
| | |
| | def download_extract_zip(filename, target_dir="."): |
| | print(f"Downloading {filename}...") |
| | path = hf_hub_download(repo_id=REPO_ID, filename=filename, repo_type="model") |
| | print(f"Extracting {filename} to {target_dir}...") |
| | with zipfile.ZipFile(path, 'r') as zip_ref: |
| | zip_ref.extractall(target_dir) |
| | |
| | # 1. Download and extract Dependencies (creates ./deps/) |
| | download_extract_zip("deps.zip", ".") |
| | |
| | # 2. Download and extract Datasets (creates ./raw_data/HumanML3D and ./raw_data/BABEL_streamed) |
| | os.makedirs("raw_data", exist_ok=True) |
| | download_extract_zip("HumanML3D.zip", "raw_data") |
| | download_extract_zip("BABEL_streamed.zip", "raw_data") |
| | |
| | # 3. Download Models (creates ./outputs/) |
| | download_extract_zip("outputs.zip", ".") |
| | download_extract_zip("outputs_tiny.zip", ".") |
| | |
| | print("Done! Your project is ready.") |
| | ``` |
| | |
| | ## Data License & Acknowledgements |
| | |
| | This repository provides pre-processed motion features (263-dim) to facilitate the reproduction of FloodDiffusion. |
| | |
| | - **HumanML3D**: The motion features are derived from the [HumanML3D](https://github.com/EricGuo5513/HumanML3D) pipeline, originally built upon [AMASS](https://amass.is.tue.mpg.de/) and [HumanAct12](https://github.com/EricGuo5513/Action2Motion). |
| | - **BABEL**: The streaming motion features are derived from the [BABEL](https://babel.is.tue.mpg.de/) dataset, which also builds upon AMASS. |
| | |
| | **Important Note**: |
| | We only distribute the **extracted motion features and text annotations**, which is standard practice in the research community. We do **not** distribute the raw AMASS data (SMPL parameters/meshes). If you require the raw motion data or plan to use it for commercial purposes, you must register and agree to the licenses on the [AMASS website](https://amass.is.tue.mpg.de/). |
| | |