Instructions to use stonesstones/vae_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stonesstones/vae_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="stonesstones/vae_test", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("stonesstones/vae_test", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from stonesstones/vae_test: direct link, hf CLI and curl.
- Browser
- Download file 821 Bytes
-
https://huggingface.co/stonesstones/vae_test/resolve/main/config.json
- Command line
-
hf download hf://stonesstones/vae_test/config.json
-
curl -L -o config.json https://huggingface.co/stonesstones/vae_test/resolve/main/config.json
821 Bytes
| { | |
| "architectures": [ | |
| "VAEModel" | |
| ], | |
| "attn_resolutions": [], | |
| "auto_map": { | |
| "AutoConfig": "configuration_vae.VAEConfig", | |
| "AutoModel": "modeling_vae.VAEModel" | |
| }, | |
| "channels": 128, | |
| "channels_mult": [ | |
| 1, | |
| 1, | |
| 1, | |
| 2, | |
| 2 | |
| ], | |
| "codebook_dim": 0, | |
| "codebook_size": 0, | |
| "decoder_type": "Simple", | |
| "drop_out": 0, | |
| "dropout": 0.0, | |
| "encoder_type": "Simple", | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "in_channels": 3, | |
| "model_type": "vae", | |
| "num_res_blocks": 2, | |
| "out_channels": 3, | |
| "quantizer_type": "VQ", | |
| "resolution": [ | |
| 64, | |
| 64 | |
| ], | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.51.3", | |
| "w_commit": 0, | |
| "w_dino": 0, | |
| "w_kl": 1, | |
| "w_l1": 0.2, | |
| "w_mse": 2, | |
| "w_perceptual": 0, | |
| "z_channels": 64 | |
| } | |