Instructions to use Jaytsh/FLUX-B3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jaytsh/FLUX-B3 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", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Jaytsh/FLUX-B3") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 424fa90158251fe9dbe4b137fc42e6198240c567e491d4ea760b0fb392f5f8ee
- Size of remote file:
- 166 MB
- SHA256:
- f8eef65baed0624d21a3ca4748e4090744003eb139e143085f5826549d57e09b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.