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 model_index.json from sebascorreia/encoding-vae: direct link, hf CLI and curl.
- Browser
- Download file 295 Bytes
-
https://huggingface.co/sebascorreia/encoding-vae/resolve/72c5ba4705a5f693d4763b6e7d6b4e43d6c44517/model_index.json
- Command line
-
hf download hf://sebascorreia/encoding-vae@72c5ba4705a5f693d4763b6e7d6b4e43d6c44517/model_index.json
-
curl -L -o model_index.json https://huggingface.co/sebascorreia/encoding-vae/resolve/72c5ba4705a5f693d4763b6e7d6b4e43d6c44517/model_index.json
295 Bytes
| { | |
| "_class_name": "AudioDiffusionPipeline", | |
| "_diffusers_version": "0.20.2", | |
| "mel": [ | |
| "audio_diffusion", | |
| "Mel" | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "DDIMScheduler" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNet2DModel" | |
| ], | |
| "vqvae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |