Instructions to use Ccre/Z-Image-Turbo-MXFP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Ccre/Z-Image-Turbo-MXFP8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Ccre/Z-Image-Turbo-MXFP8", 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
- Draw Things
- DiffusionBee
Tested on NVIDIA Blackwell (RTX 5060 Ti). This MXFP8 version offers a ~1.9x speedup over BF16 with very little visual degradation.
See the full forensic benchmarks and 'Difference Maps' at: blackwell-mxfp8-nvfp4
Read the step-by-step installation guide for MXFP8 and NVFP4.
Images created at 12 steps.
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Model tree for Ccre/Z-Image-Turbo-MXFP8
Base model
Tongyi-MAI/Z-Image-Turbo