File size: 2,641 Bytes
2402033 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | ---
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.
|