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

- Xet hash:
- c57eabde471a02d7ac6c4e0bb3c7290ddf18f74310af6d80785977052e2bcab7
- Size of remote file:
- 2.31 MB
- SHA256:
- 7beadb49c1114fa2371bb0f53b8ef1c88629ecd763376c10725a067540703ec7
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