Text-to-Image
Diffusers
Safetensors
English
Flux2KleinPipeline
image-generation
image-editing
flux
flux2
Flux2KleinPipeline
sdnq
4-bit precision
uint4
quantized
Instructions to use WaveCut/FLUX.2-klein-9B-SDNQ-uint4-static with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/FLUX.2-klein-9B-SDNQ-uint4-static with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WaveCut/FLUX.2-klein-9B-SDNQ-uint4-static", 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:
- 406c347c785860b24484d9418b0f4200cb922f29bc733bb8c496f4f9b346b95c
- Size of remote file:
- 6 MB
- SHA256:
- d84411e197a8bd3f644f1a8b20574f22e4b9774d37ed7506adeda610d4e3f231
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