Instructions to use sebascorreia/encoding-vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sebascorreia/encoding-vae with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("sebascorreia/encoding-vae", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download mel/mel_config.json from sebascorreia/encoding-vae: direct link, hf CLI and curl.
- Browser
- Download file 187 Bytes
-
https://huggingface.co/sebascorreia/encoding-vae/resolve/b8f812f5aa7bd69352b5560aa6cc4fbc2a6f7dbf/mel/mel_config.json
- Command line
-
hf download hf://sebascorreia/encoding-vae@b8f812f5aa7bd69352b5560aa6cc4fbc2a6f7dbf/mel/mel_config.json
-
curl -L -o mel_config.json https://huggingface.co/sebascorreia/encoding-vae/resolve/b8f812f5aa7bd69352b5560aa6cc4fbc2a6f7dbf/mel/mel_config.json
187 Bytes
| { | |
| "_class_name": "Mel", | |
| "_diffusers_version": "0.20.2", | |
| "hop_length": 512, | |
| "n_fft": 2048, | |
| "n_iter": 32, | |
| "sample_rate": 22050, | |
| "top_db": 80, | |
| "x_res": 256, | |
| "y_res": 256 | |
| } | |