Text-to-Image
Diffusers
English
image-editing
SVDQuant
Z-Image-Turbo
Diffusion
Quantization
ICLR2025
Instructions to use nunchaku-ai/nunchaku-z-image-turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nunchaku-ai/nunchaku-z-image-turbo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nunchaku-ai/nunchaku-z-image-turbo", 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:
- 562f0ae54e74dc0b154a4b142f91341b25263366410d06082b55b4adf4b359a0
- Size of remote file:
- 3.61 GB
- SHA256:
- a860c9d533b98a461b98bf39cd17ee921ee1417d43dd01cb935050ac1eb8c80a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.