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
.png)
- Xet hash:
- b06ed8cfc541ad729ef6a7d7f6e579438a9c2eacd19ced65c23572dd88f7f8ab
- Size of remote file:
- 4.38 MB
- SHA256:
- d796204937c1cf494c901f8dcea2071d351d5bce8cc1c9a2b336a88888ef9748
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.