Instructions to use andrevp/Z-Image-Turbo-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use andrevp/Z-Image-Turbo-MLX-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Z-Image-Turbo-MLX-8bit andrevp/Z-Image-Turbo-MLX-8bit
- Diffusers
How to use andrevp/Z-Image-Turbo-MLX-8bit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("andrevp/Z-Image-Turbo-MLX-8bit", 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
- LM Studio
| { | |
| "quantization": { | |
| "bits": 8, | |
| "group_size": 64, | |
| "skip_components": [ | |
| "vae" | |
| ] | |
| } | |
| } |