z-image-burn

Z-Image Turbo (Burn)

Z-Image Turbo model weights converted to Burn's Burnpack format for use with the Burn deep learning framework.

Model Description

Z-Image is a fast image generation model based on flow-matching diffusion, designed for generating high-quality images from text prompts. This repository contains the model weights in Burnpack (.bpk) format, optimized for inference on Apple Silicon (Metal) and other Burn backends.

Files

Additional Required Files

To run Z-Image, you also need:

• Tokenizer: From holgt/qwen3-0.6b-burn (tokenizer.json)
• Text Encoder: From Comfy-Org/z_image_turbo (qwen_3_4b.safetensors)
• Autoencoder: From black-forest-labs/FLUX.1-dev (ae.safetensors)

Usage

With the z-image-app

See the z-image-app for a macOS GUI application.

Rust Code Example

use burn::backend::candle::{Candle, CandleDevice};
use half::bf16;
use qwen3_burn::{Qwen3Config, Qwen3Model, Qwen3Tokenizer};
use z_image::{GenerateFromTextOpts, modules::ae::AutoEncoderConfig, modules::transformer::ZImageModelConfig};

type Backend = Candle<bf16, i64>;

fn main() {
    let device = CandleDevice::metal(0);
    let model_dir = PathBuf::from("./models");

    // Load components
    let tokenizer = Qwen3Tokenizer::from_file(model_dir.join("qwen3-tokenizer.json")).unwrap();

    let mut text_encoder: Qwen3Model<Backend> = Qwen3Config::z_image_text_encoder().init(&device);
    text_encoder.load_weights(model_dir.join("qwen3_4b_text_encoder.safetensors")).unwrap();

    let mut transformer = ZImageModelConfig::default().init(&device);
    transformer.load_weights(model_dir.join("z_image_turbo_bf16.bpk")).unwrap();

    let mut ae = AutoEncoderConfig::flux_ae().init(&device);
    ae.load_weights(model_dir.join("ae.safetensors")).unwrap();

    // Generate image
    let opts = GenerateFromTextOpts {
        prompt: "A beautiful sunset over mountains".to_string(),
        out_path: PathBuf::from("output.png"),
        width: 512,
        height: 512,
    };

    z_image::generate_from_text(&opts, &tokenizer, &text_encoder, &ae, &transformer, &device).unwrap();
}

Requirements

• Apple Silicon Mac with Metal support, or
• CUDA-capable GPU (with appropriate Burn backend)
• ~16GB RAM for 512x512 images
• Rust 2024 edition

License

Apache 2.0

Acknowledgments

• Burn - Deep learning framework
• Comfy-Org - Original Z-Image model
• black-forest-labs - FLUX autoencoder