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---
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 (`outputs.zip`)
The `outputs.zip` archive contains the pretrained model weights.
* **Target**: Unzip into your project root. It should create an `outputs/` folder.
```
outputs/
├── vae_1d_z4_step=300000.ckpt # VAE model (1D, z_dim=4)
├── 20251106_063218_ldf/
│ └── step_step=50000.ckpt # LDF model checkpoint (HumanML3D)
└── 20251107_021814_ldf_stream/
└── step_step=240000.ckpt # Streaming LDF model checkpoint (BABEL)
```
### 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/
├── t2m/ # Text-to-Motion evaluation models
├── glove/ # GloVe word embeddings
└── 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", ".")
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/).
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