Other
Transformers
Safetensors
ldf_motion
feature-extraction
text-to-motion
motion-generation
diffusion-forcing
humanml3d
computer-animation
custom_code
Instructions to use ShandaAI/FloodDiffusionTiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShandaAI/FloodDiffusionTiny with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ShandaAI/FloodDiffusionTiny", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| exp_name: ldf | |
| seed: 1234 | |
| debug: false | |
| train: false | |
| save_dir: ./outputs | |
| resume_ckpt: null | |
| test_ckpt: "model.safetensors" | |
| test_vae_ckpt: "vae.safetensors" | |
| test_vae: | |
| target: ldf_models.vae_wan_1d.VAEWanModel | |
| ema_decay: 0.99 | |
| params: | |
| input_dim: 263 | |
| z_dim: 4 | |
| test_setting: | |
| render: false | |
| simple: true | |
| recover_dim: 263 | |
| val_repeat: 1 | |
| model: | |
| target: ldf_models.diffusion_forcing_wan_tiny.DiffForcingWanModel | |
| ema_decay: 0.99 | |
| params: | |
| model_name: "google/umt5-base" | |
| input_dim: 4 | |
| noise_steps: 10 | |
| hidden_dim: 256 | |
| ffn_dim: 1024 | |
| freq_dim: 64 | |
| num_heads: 8 | |
| num_layers: 8 | |
| time_embedding_scale: 1.0 | |
| chunk_size: 5 | |
| use_text_cond: True | |
| text_len: 128 | |
| drop_out: 0.1 | |
| cfg_scale: 5.0 | |
| prediction_type: "vel" | |
| causal: False | |