Instructions to use audiohacking/pruna-vaed-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use audiohacking/pruna-vaed-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir pruna-vaed-mlx audiohacking/pruna-vaed-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 845 Bytes
a4fc87f | 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 | {
"variant": "pruna",
"source": "PrunaAI/PrunaVAED",
"latent_channels": 128,
"out_channels": 3,
"patch_size": 4,
"patch_size_t": 1,
"decoder_block_out_channels": [
128,
256,
384,
1024
],
"decoder_layers_per_block": [
4,
6,
4,
2,
2
],
"decoder_spatio_temporal_scaling": [
true,
true,
true,
true
],
"decoder_inject_noise": [
false,
false,
false,
false,
false
],
"decoder_spatial_padding_mode": "zeros",
"decoder_causal": false,
"upsample_factor": [
2,
2,
1,
2
],
"upsample_residual": [
false,
false,
false,
false
],
"upsample_type": [
"spatiotemporal",
"spatiotemporal",
"temporal",
"spatial"
],
"timestep_conditioning": false,
"resnet_norm_eps": 1e-06,
"scaling_factor": 1.0
} |