Downloads · 30 days
0
webnn/Z-Image-Turbo
Z-Image-Turbo is a text-to-image model from webnn. Use it when you need an image from a text prompt. The card lists the license as apache-2.0.
This repository provides an optimized ONNX version of Tongyi-MAI/Z-Image-Turbo, specifically tailored for efficient browser execution via WebNN and WebGPU using ONNX Runtime Web.
Downloads · 30 days
0
Access
Public
Updated Sep 10, 2026
Repo size
6.6 GB
Likes
0
Public
Click a slice to open those files.
.onnx_data4.7 GB · 71%
From the Hugging Face model README
This repository provides an optimized ONNX version of Tongyi-MAI/Z-Image-Turbo, specifically tailored for efficient browser execution via WebNN and WebGPU using ONNX Runtime Web.
The end-to-end inference flow runs sequentially across the ONNX sub-models under onnx/ per image generation:
| 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 |
Instructions and scripts for exporting and optimizing the ONNX models from the original PyTorch weights can be found in the model_exporter/README.md.
Experience the model running in the browser: