Instructions to use MLbackup/Loras_2026_Backup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MLbackup/Loras_2026_Backup with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MLbackup/Loras_2026_Backup", 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 Samples/image (80).png from MLbackup/Loras_2026_Backup: direct link, hf CLI and curl.
- Browser
- Download file 4.51 MB
-
https://huggingface.co/MLbackup/Loras_2026_Backup/resolve/main/Samples/image%20(80).png
- Command line
-
hf download 'hf://MLbackup/Loras_2026_Backup/Samples/image (80).png'
-
curl -L -o 'image (80).png' 'https://huggingface.co/MLbackup/Loras_2026_Backup/resolve/main/Samples/image%20(80).png'
4.51 MB
.png)
- Xet hash:
- bbb1df2c59d5fd95133b8968bcea899bee114a20554b38c4b1c10fae82636a5c
- Size of remote file:
- 4.51 MB
- SHA256:
- 47cbdaa3add758f3beade8f1a38e9921d3b04d42fa374caca1dcc9fe080ee7fd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.