Text-to-Image
Diffusers
English
Chinese
lora
template:diffusion-lora
flux
flux.1-dev
automotive-design
industrial-design
design
agent-friendly
Instructions to use XonarLabs/OLOID_Framework with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use XonarLabs/OLOID_Framework with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("XonarLabs/OLOID_Framework") prompt = "xonar_ext_SUV_rough, A professional studio photography of a SUV, silver metallic gloss paint, 8k resolution, cinematic lighting, dark grey studio background." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee

- Xet hash:
- 0468dcf0546089b45a9ede6d8b31a531748827a3b937d0df26f3e163962f69bb
- Size of remote file:
- 55.6 kB
- SHA256:
- 39d1d266e24803ce96e0e0fac6a183e6dc661bd3ef2fd4ab77b01b961e1a82a6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.