Instructions to use skaura/ercc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use skaura/ercc with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("skaura/ercc", 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 skaura/ercc: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/skaura/ercc/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://skaura/ercc/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/skaura/ercc/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 7084f719feaa0d33d44f39c4a365ca927897ecc6db06a4de2c087b364611da5b
- Size of remote file:
- 492 MB
- SHA256:
- 19de9a134da5915f185a798af22d8e598d94a6cd860a7c709ed13b6385e4275e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.