Text-to-Image
Diffusers
Safetensors
FluxPipeline
flux
nunchaku
svdquant
nvfp4
quantization
8-bit precision
Instructions to use lite-infer/flux.1-krea-dev-nunchaku-lite-nvfp4_r32-bnb4-text-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lite-infer/flux.1-krea-dev-nunchaku-lite-nvfp4_r32-bnb4-text-encoder with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lite-infer/flux.1-krea-dev-nunchaku-lite-nvfp4_r32-bnb4-text-encoder", 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
flux.1-krea-dev-nunchaku-lite-nvfp4_r32-bnb4-text-encoder / transformer /diffusion_pytorch_model.safetensors
- Xet hash:
- aec830d684b5d213443cbce935406f8bd9ab7a1f98d5607b00160cc4b75c7f35
- Size of remote file:
- 7.32 GB
- SHA256:
- e8ba2e9be7e4d091625091520123319eb744e4fffcb35e3de1d9f9b47e065f34
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.