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 text_encoder/pytorch_model.bin from jruaechalar/letras0: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/jruaechalar/letras0/resolve/refs%2Fpr%2F1/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://jruaechalar/letras0@refs/pr/1/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jruaechalar/letras0/resolve/refs%2Fpr%2F1/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- 9e06013911eeb7305256703783a78ad4af649f442f7393d553411ca956b3f6e3
- Size of remote file:
- 246 MB
- SHA256:
- 6e39e1ee636b37d4c879c33572869d3b37340befdf520ecb036a45398215de7f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.