Instructions to use MickJ/Z-Image-Turbo-fp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MickJ/Z-Image-Turbo-fp8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MickJ/Z-Image-Turbo-fp8", 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
an fp8 version of Tongyi-MAI/Z-Image-Turbo, converted with the checkpoint of the original transformer component with:
python -m sglang.multimodal_gen.tools.convert_hf_to_fp8 \
--model-dir /root/.cache/huggingface/hub/models--Tongyi-MAI--Z-Image-Turbo/snapshots/f332072aa78be7aecdf3ee76d5c247082da564a6/transformer/
--save-dir /root/.cache/huggingface/hub/models--Tongyi-MAI--Z-Image-Turbo-fp8/snapshots/f332072aa78be7aecdf3ee76d5c247082da564a6/transformer/
For SGLang-Diffusion CI usage, guard the --transformer-path server arg feature
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