---
library_name: pytorch
tags:
  - motion-generation
  - text-to-motion
  - diffusion
  - streaming
  - humanml3d
language:
  - en
---

# FloodDiffusion 2

**Efficient and Path Controllable Streaming Motion Generation**

[Code and setup instructions](https://github.com/AlayaLab/FloodDiffusion2)

FloodDiffusion 2 supports continuous text-conditioned motion generation with
Partial Attention, diffusion-compatible geometric supervision, and root-path
control. This release includes six FD2 checkpoints and the paired 263D LDF/VAE
models for first-generation compatibility.

| Checkpoint folder | Configuration | Training step | CFG |
|---|---|---:|---:|
| `humanml3d_fk_60k` | `df_humanml3d_263.yaml` | 60,000 | 4 |
| `humanml3d_path_fk_55k` | `df_humanml3d_263_path.yaml` | 55,000 | 3 |
| `humanml3d_babel_fk_200k` | `df_humanml3d_babel_263.yaml` | 200,000 | 4 |
| `humanml3d_babel_path_200k` | `df_humanml3d_babel_263_path.yaml` | 200,000 | 4 |
| `seed_fk_300k` | `df_seed_138.yaml` | 300,000 | 2 |
| `seed_path_fk_300k` | `df_seed_138_path.yaml` | 300,000 | 2 |
| `ldf_263` | `ldf_263.yaml` | 180,000 | 6 |
| `vae_263` | `vae_263.yaml` | 2,250,000 | — |

Each `checkpoints/<folder>/` contains `model.ckpt`, a matching `config.yaml`,
and normalization statistics under `assets/`. FD2 folders also include the
quadratic FK matrix `assets/W.npy`. Checkpoints retain model, EMA, optimizer and
scheduler state.

## Setup

Clone the code repository and run the following in a Python 3.10+ environment:

```bash
python setup_project.py
```

The command installs Python requirements and downloads the four paper checkpoints
(HumanML3D text/path and SEED text/path), their assets, and the
[shared dependency archive](https://huggingface.co/AlayaLab/FloodDiffusion2/blob/main/deps.zip)
for UMT5, T2M and GloVe. Add `--with-data` to also install the
[prepared datasets](https://huggingface.co/datasets/AlayaLab/FloodDiffusion2-Data)
and paired VAE tokens.

For mesh rendering, obtain SMPL-H from its
[official site](https://mano.is.tue.mpg.de/) and import the licensed neutral model:

```bash
python setup_project.py --smplh /path/to/neutral/model.npz
```

## Evaluation

```bash
python evaluate.py --config configs/df_humanml3d_263.yaml
```

For additional checkpoints, use their bundled `checkpoints/<folder>/config.yaml`.

The selected YAML defines the checkpoint, dataset and evaluation settings.
Use the corresponding configuration for other variants. LDF uses the paired
VAE from this release.

## Dependencies

Third-party weights and datasets retain their respective licenses. SMPL-H is
obtained separately from its official distributor.