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Z-Image De-Turbo De-distilled Model

Z-Image De-Turbo is a de-distilled version of the original Z-Image-Turbo model, designed to overcome turbo distillation limits. It restores full trainability and flexibility, allowing developers to create custom LoRAs and fine-tunes while maintaining the efficient S3-DiT architecture.

De-distilled
LoRA Training
Deep Fine-tuning
ComfyUI
Trainability
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Overview

Z-Image De-Turbo is a de-distilled version of Tongyi-MAI/Z-Image-Turbo, fine-tuned on images generated by Z-Image-Turbo to break down the turbo distillation limitations. This model is specifically designed for training and deep fine-tuning, offering enhanced trainability and flexibility compared to the original turbo model.

Features

  • De-distillation technology breaking original turbo model limitations
  • Direct training capability without adapters
  • LoRA training support while maintaining compatibility with base model
  • Deep fine-tuning support exceeding original turbo model capabilities
  • ComfyUI version and diffusers-based version available
  • CFG normalization support

Images

Z-Image De-Turbo Generation Sample
Sample image generated using Z-Image De-Turbo

Installation

git clone https://huggingface.co/ostris/Z-Image-De-Turbo
pip install -r requirements.txt

Usage

For inference, use low CFG (2.0-3.0) and 20-30 steps. The model supports CFG normalization for better generation results.

Requirements

  • Python 3.8+
  • PyTorch
  • Diffusers library
  • CUDA compatible GPU
  • 16GB+ VRAM recommended for training

Related Links

Training AdapterHuggingFace Model

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