Views
No views yet
f332072aa78be7aecdf3ee76d5c247082da564a6, Apache-2.0).transformer/ DiT weights (quantized) + config.json
text_encoder/ Qwen3-4B-based text-encoder weights (quantized) + config.json
vae/ VAE weights + config.json
tokenizer/ tokenizer files
scheduler/ scheduler_config.json
quantization.json MLX quantization manifest (bits=8, group_size=32, mode=affine)mzbac/zimage.swift, MIT) and by
MLX-based loaders that understand the quantization.json manifest convention.diffusers / PyTorch — use the upstream bf16 repo for that.WhispallIMG quantize -i <Z-Image-Turbo bf16 dir> -o z-image-turbo-Q8-affine --bits 8 --group-size 32mzbac/zimage.swift).