README.md

---
license: apache-2.0
language:
- en
pipeline_tag: text-to-image
base_model: Tongyi-MAI/Z-Image
tags:
- gguf
- safetensors
- text-to-image
- rundiffusion
- z-image
---

<h1 align="center">Juggernaut Z<br><sub><sup>A polished cinematic fine-tune of Z-Image Base from RunDiffusion</sup></sub></h1>

<div align="center">

[![Official Site](https://img.shields.io/badge/Official%20Site-Juggernaut%20Z-111111)](https://www.rundiffusion.com/juggernaut-z)
[![Base Model](https://img.shields.io/badge/%F0%9F%A4%97%20Base%20Model-Z--Image-yellow)](https://huggingface.co/Tongyi-MAI/Z-Image)
[![Prompt Guide](https://img.shields.io/badge/Prompt%20Guide-RunDiffusion-2f6fed)](https://www.rundiffusion.com/juggernaut-z-prompt-guide)

</div>

Juggernaut Z is a fine-tuned image model built on **Z-Image Base**, created through the work of **Team Juggernaut** with fine-tuning by **KandooAI**. On RunDiffusion, it is positioned as a stronger choice for creators who want more polished image output, better lighting quality, stronger camera focus, and more detailed skin textures.

This repository is intended to host the RunDiffusion release artifacts for Juggernaut Z, including full-precision weights and GGUF quantizations.

![Juggernaut Z Hero](https://www.rundiffusion.com/images/juggernaut-z/hero-image.jpg)

## Overview

Juggernaut Z is tuned for a more presentation-ready look out of the box. Relative to Z-Image Base, the emphasis is on:

- Stronger lighting and clearer atmosphere
- More refined focus and camera feel
- More polished portrait rendering
- Improved skin texture detail
- Better out-of-the-box presentation for editorial, concept, and cinematic work

## Example Images

The images below are pulled from the RunDiffusion announcement page for Juggernaut Z.

![Lighting Example](https://www.rundiffusion.com/images/juggernaut-z/lighting-1.png)
![Skin Detail Example](https://www.rundiffusion.com/images/juggernaut-z/skin-1.png)
![Diversity Example](https://www.rundiffusion.com/images/juggernaut-z/diversity-1.png)

## Recommended Settings

Based on the RunDiffusion launch guidance:

- Recommended default: `CFG 6`, `35 steps`
- Good CFG range: `6 to 9`
- Good steps range: `25 to 45`

## Good Fit For

- Portraits with cleaner facial detail and stronger focus
- Cinematic scenes with stronger lighting and clearer atmosphere
- Concept development and visual exploration
- Editorial and fashion work that benefits from a polished finish

## Files In This Repo

Current release artifacts:

- `Juggernaut_Z_V1_by_RunDiffusion.safetensors`
- `Juggernaut_Z_V1_FP8_e4m3fn.safetensors`
- `Juggernaut_Z_V1_by_RunDiffusion_q4_k_s-001.gguf`
- `Juggernaut_Z_V1_by_RunDiffusion_q4_k_m-002.gguf`
- `Juggernaut_Z_V1_by_RunDiffusion_q5_k_s-005.gguf`
- `Juggernaut_Z_V1_by_RunDiffusion_q5_k_m-003.gguf`
- `Juggernaut_Z_V1_by_RunDiffusion_q6_k-004.gguf`
- `Juggernaut_Z_V1_by_RunDiffusion_q8_0.gguf`

## Notes

- The base model is [Tongyi-MAI/Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image).
- This card uses image assets from the RunDiffusion Juggernaut Z announcement page.
- If you are using the GGUF files, use a GGUF-compatible runtime or workflow.
- If you are using the safetensors releases, load them with the workflow that matches your local inference stack.

## Links

- Announcement: [RunDiffusion Juggernaut Z](https://www.rundiffusion.com/juggernaut-z)
- Prompt guide: [Juggernaut Z Prompt Guide](https://www.rundiffusion.com/juggernaut-z-prompt-guide)
- Base model: [Tongyi-MAI/Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image)

## Attribution

Juggernaut Z is based on Z-Image Base. Credit for the upstream base model belongs to the Z-Image team. Credit for this fine-tuned release is attributed to Team Juggernaut, with fine-tuning by KandooAI, as described on the RunDiffusion announcement page.