Instructions to use avlp12/CyberRealistic-Z-Image-Turbo-v4-mflux-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use avlp12/CyberRealistic-Z-Image-Turbo-v4-mflux-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir CyberRealistic-Z-Image-Turbo-v4-mflux-4bit avlp12/CyberRealistic-Z-Image-Turbo-v4-mflux-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
CyberRealistic Z-Image Turbo v4.0 โ MLX (4bit)
CyberRealistic Z-Image Turbo by Cyberdelia (v4.0, from the creator's own HF mirror), converted from the ComfyUI single-file checkpoint to the mflux layout and quantized to 4bit for Apple-silicon Macs. Text encoder (Qwen3-4B), VAE, and tokenizer come from the base Tongyi-MAI/Z-Image-Turbo (Apache-2.0).
A photorealism-focused finetune of Z-Image-Turbo: ~9 steps, no CFG, multilingual prompts (Korean included). The 4-bit build runs on a 16 GB Mac.
from mflux.models.z_image.variants import ZImageTurbo
m = ZImageTurbo(model_path="avlp12/CyberRealistic-Z-Image-Turbo-v4-mflux-4bit")
m.generate_image(seed=7, prompt="candid photo of ...", num_inference_steps=9).save(path="out.png")
Used as a built-in model in Alis Studio. License: CreativeML OpenRAIL-M (inherited from the finetune; see the use restrictions). Credit: Cyberdelia (finetune), Tongyi-MAI (base model), mflux (MLX runtime).
- Downloads last month
- 28
Hardware compatibility
Log In to add your hardware
Quantized
Model tree for avlp12/CyberRealistic-Z-Image-Turbo-v4-mflux-4bit
Base model
Tongyi-MAI/Z-Image-Turbo