Text-to-Image
Diffusers
Safetensors
ZImagePipeline
z-image
nunchaku
svdquant
int4
quantization
8-bit precision
Instructions to use lite-infer/z-image-turbo-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/z-image-turbo-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/z-image-turbo-nunchaku-lite-int4_r32-bnb4-text-encoder", torch_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
z-image-turbo-nunchaku-lite-int4_r32-bnb4-text-encoder / transformer /diffusion_pytorch_model.safetensors
- Xet hash:
- 8f3690104fc620919ab1ab9373060000281955f87ef56d7f1f635cb90db0e1af
- Size of remote file:
- 3.63 GB
- SHA256:
- 093a286e6d8849ba15808221c37b43b16e6b5f5b40ecc0b6661e3027c488ec49
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