Instructions to use Croscot/carritos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Croscot/carritos with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Croscot/carritos", 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 checkpoint-2000/optimizer.bin from Croscot/carritos: direct link, hf CLI and curl.
- Browser
- Download file 910 MB
-
https://huggingface.co/Croscot/carritos/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://Croscot/carritos/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Croscot/carritos/resolve/main/checkpoint-2000/optimizer.bin
910 MB
- Xet hash:
- 27f5471860203144ba0871abf11ba9d42fb7201ef5f6488b14c44e43e54234ba
- Size of remote file:
- 910 MB
- SHA256:
- fba8d55b46b485d0642c70881210a2c36a2714dc11b7c6c6c7f936267c959154
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.