Instructions to use jmxt/jo-flux with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jmxt/jo-flux 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("jmxt/jo-flux") prompt = "Jo, a 41-year-old father, smiling as he looks out the window and sees planet Earth, in a spaceship, wearing an astronaut orange suit with the NASA symbol. He is looking forward to returning to his family. The ship is entering the atmosphere and there is a reflection of intense light coming through the window. Breathtaking, cinematic, exciting, harmonious, highly detailed, high budget, bokeh, cinemascope, atmospheric, epic, stunning, UHD 8K, 85mm, Fujifilm GFX100 II, soft light, warm composition," image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Welcome to the community
The community tab is the place to discuss and collaborate with the HF community!