Instructions to use vistralis/FLUX.2-klein-9b-INT8-transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vistralis/FLUX.2-klein-9b-INT8-transformer with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vistralis/FLUX.2-klein-9b-INT8-transformer", 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
- Xet hash:
- 25082a60256b94585dca082e30f76af0b5724e01e6eb4b5f2a2bb0db93002cb1
- Size of remote file:
- 9.44 GB
- SHA256:
- fc4f91ad7ae1e6d23c591461173328587f02144fb72ed0798ba5dd7eb53d1c08
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.