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
- Draw Things
- DiffusionBee
.png)
- Xet hash:
- d6e513939ac1f9be39cf4817ae798ef6db6306b0527ccbdbdd09d96b94a8f50b
- Size of remote file:
- 3.92 MB
- SHA256:
- b4966c5c5f4a0a582cfb2c4d023b6a423f049ee5204e784b2a607d31b374764f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.