Instructions to use jaruce/output_lr1e4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jaruce/output_lr1e4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jaruce/output_lr1e4", 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-3000/optimizer.bin from jaruce/output_lr1e4: direct link, hf CLI and curl.
- Browser
- Download file 6.88 GB
-
https://huggingface.co/jaruce/output_lr1e4/resolve/main/checkpoint-3000/optimizer.bin
- Command line
-
hf download hf://jaruce/output_lr1e4/checkpoint-3000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/jaruce/output_lr1e4/resolve/main/checkpoint-3000/optimizer.bin
6.88 GB
- Xet hash:
- 8296c63af3ef582ba13a1f135be6741660c893e279f72b79348403b594a5a427
- Size of remote file:
- 6.88 GB
- SHA256:
- a74887c3f340d5cd9b3d8f0d9701a480b249657c67f4588f16128d19119e5375
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.