Instructions to use vladmandic/Flux.2-Klein-9B-KV-sdnq-hadamard-uint4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vladmandic/Flux.2-Klein-9B-KV-sdnq-hadamard-uint4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vladmandic/Flux.2-Klein-9B-KV-sdnq-hadamard-uint4", 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
- Xet hash:
- e9ee0c86a4b839290ac8d9f86e895067d161bbc13760ee2e8a2477217ab90af7
- Size of remote file:
- 6.83 GB
- SHA256:
- 8106afa39127feb300d43eeb1c54037e9ad0d265033e6823a1649c2547c3b4fc
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