Instructions to use stablediffusionapi/agelesnate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stablediffusionapi/agelesnate with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stablediffusionapi/agelesnate", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Download vae/diffusion_pytorch_model.bin from stablediffusionapi/agelesnate: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/stablediffusionapi/agelesnate/resolve/main/vae/diffusion_pytorch_model.bin
- Command line
-
hf download hf://stablediffusionapi/agelesnate/vae/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/stablediffusionapi/agelesnate/resolve/main/vae/diffusion_pytorch_model.bin
167 MB
- Xet hash:
- 25825eea924c108649e85eaa65cf1f23e71bcc6e4748b8394cfa5c5602ee94d1
- Size of remote file:
- 167 MB
- SHA256:
- f34e2597d3fb86b1a92db4969e30d66a7caf7c68135453d721d827a75e233256
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.