Text-to-Image
Diffusers
Safetensors
FluxPipeline
flux
nunchaku
svdquant
int4
quantization
8-bit precision
Instructions to use lite-infer/flux.1-krea-dev-nunchaku-lite-int4_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-int4_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-int4_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-int4_r32-bnb4-text-encoder / transformer /diffusion_pytorch_model.safetensors
- Xet hash:
- 90e892d555d91c747181b8afa3ca4b12eb4e2cafa9e757de57b35879d5f8a576
- Size of remote file:
- 7.05 GB
- SHA256:
- 3d71bdfcf458880fea32bbcb9d17854fffd7b26eb10eaf4cd356b0938bb97380
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