Instructions to use jruaechalar/letras0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jruaechalar/letras0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jruaechalar/letras0", 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 jruaechalar/letras0: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/jruaechalar/letras0/resolve/refs%2Fpr%2F1/vae/diffusion_pytorch_model.bin
- Command line
-
hf download hf://jruaechalar/letras0@refs/pr/1/vae/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/jruaechalar/letras0/resolve/refs%2Fpr%2F1/vae/diffusion_pytorch_model.bin
167 MB
- Xet hash:
- 71606cad63d6e8481c838da0032b2c221e93df9ca5b5f0edc3a65379c7e30ecf
- Size of remote file:
- 167 MB
- SHA256:
- eb9ff62d22c52426b0b95618009a63f052c5458a9fbc530de6369ff95473cf1e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.