Text-to-Image
Diffusers
Safetensors
English
Flux2KleinPipeline
image-generation
image-editing
flux
flux2
Flux2KleinPipeline
sdnq
4-bit precision
float4
dynamic-quantization
svd
quantized
Instructions to use WaveCut/FLUX.2-klein-9B-SDNQ-float4_e4m0fnu-dynamic-th0p01-svd-r128-s32 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-float4_e4m0fnu-dynamic-th0p01-svd-r128-s32 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-float4_e4m0fnu-dynamic-th0p01-svd-r128-s32", 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
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