Instructions to use vistralis/FLUX.2-klein-base-4b-INT8-transformer-quants with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vistralis/FLUX.2-klein-base-4b-INT8-transformer-quants 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-base-4b-INT8-transformer-quants", 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

- Xet hash:
- 4ba1ac96ca71820d717c4d8c703b8974955923cb46b343a767878e85bc245e7a
- Size of remote file:
- 4.84 MB
- SHA256:
- 6edbe3dbcfad556031f96fc20031da7590147699169c22ab04aacd2469f07938
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