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benchmarkxprt/Z-Image-Turbo

sourceHugging Faceapache-2.0updated 12d agoView on Hugging Face

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Model Card

Z-Image-Turbo

This repository provides an optimized ONNX version of [Tongyi-MAI/Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo), specifically tailored for efficient browser execution via WebNN and WebGPU using ONNX Runtime Web.

🔄 Runtime Pipeline

The end-to-end inference flow runs sequentially across the ONNX sub-models under onnx/ per image generation:

Inference Flow

🧩 Sub-model Components

Stage	ONNX Model Artifact	Precision	Key Role
1. Text Encoding	`text_encoder_model_q4f16.onnx`	Q4F16	Encodes text prompts into conditional embeddings
2. Denoising	`transformer_model_q4f16.onnx`	Q4F16	Predicts noise latents across diffusion iterations
	`scheduler_step_model_f16.onnx`	FP16	Computes denoised latents per step
3. Decoding	`vae_pre_process_model_f16.onnx`	FP16	Scales and shifts latents for decoding
	`vae_decoder_model_f16.onnx`	FP16	Reconstructs pixel-space RGB image from latents
4. Safety	`sc_prep_model_f16.onnx`	FP16	Normalizes and pre-processes the image for the safety checker
	`safety_checker_model_f16.onnx`	FP16	Inspects processed image features to ensure content safety

🛠️ Export & Reproduction

Instructions and scripts for exporting and optimizing the ONNX models from the original PyTorch weights can be found in the model_exporter/README.md.

🌐 Live Demo

Experience the model running in the browser:

—Interactive Demo: WebNN Developer Preview - Z-Image-Turbo

📑 Credits

—Original PyTorch Model: Tongyi-MAI/Z-Image-Turbo
—Web Runtime: ONNX Runtime Web / WebNN Developer Preview