Text-to-Image
Diffusers
Flux2KleinPipeline
flux
flux2
flux2-klein
torchao
nvfp4
blackwell
image-generation
quantized
Instructions to use joseplcam/FLUX.2-klein-9B-nvfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use joseplcam/FLUX.2-klein-9B-nvfp4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("joseplcam/FLUX.2-klein-9B-nvfp4", 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:
- a1a17c11f5bbe77267ba696c28c44a69756d6fb6e3b1756758b3d039e4ec671c
- Size of remote file:
- 6.4 GB
- SHA256:
- 124c5302ac33ae26d990e18c66981e093dc408c2f1ce1db9267d5fc010bca238
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.