A cinematic fine-tune of Z-Image Base — tuned for presentation-ready output.
> Juggernaut Z is a fine-tune of **Z-Image Base** by **Team Juggernaut**, trained by **KandooAI**, and released through **RunDiffusion**. It is tuned for stronger lighting, sharper focus, more refined skin texture, and more cinematic atmosphere — out of the box.
This repository hosts the official RunDiffusion release artifacts: full-precision weights, FP16 and FP8 variants, and a full set of GGUF quantizations.
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## Highlights
- More dramatic, cinematic **lighting** out of the box
- Sharper **focus** and a more deliberate camera feel
- Cleaner **portraits** with more natural skin texture
- Improved **anatomy** and structural integrity
- Better representation across **ethnicities** by default
- Tuned for editorial, concept, and cinematic work
## Comparisons
All sets below show **Juggernaut Z (left)** vs **Z-Image Base (right)**. Source: the [RunDiffusion Juggernaut Z announcement](https://www.rundiffusion.com/juggernaut-z?utm_source=huggingface&utm_medium=model_card&utm_campaign=juggernaut_z_v1&utm_content=comparison_source).
### Lighting
More dramatic, cinematic lighting out of the box.
### Skin & Texture
Cleaner, more natural-looking skin — especially in close-up portraits.
### Anatomy
Cleaner anatomy and more consistent structural detail across a wide range of subjects.
### Composition
Improved subject and object placement within scenes, with further work planned for v2.
### Diversity
More balanced results across ethnic backgrounds, with better representation by default.
### Architecture
Cleaner structural lines and more coherent material rendering.
## Recommended Settings
| Parameter | Default | Range |
| --- | --- | --- |
| CFG | `6` | `6 – 9` |
| Steps | `35` | `25 – 45` |
## Good Fit For
- Portraits with cleaner facial detail and stronger focus
- Cinematic scenes with strong lighting and atmosphere
- Concept development and visual exploration
- Editorial and fashion work that benefits from a polished finish
## Files In This Repo
| File | Format | Notes |
| --- | --- | --- |
| `Juggernaut_Z_V1_by_RunDiffusion.safetensors` | safetensors (fp32) | Full-precision weights |
| `Juggernaut_Z_V1_by_RunDiffusion_fp16.safetensors` | safetensors (fp16) | Half-precision |
| `Juggernaut_Z_V1_FP8_e4m3fn.safetensors` | safetensors (fp8 e4m3fn) | Lower VRAM footprint |
| `Juggernaut_Z_V1_by_RunDiffusion_q8_0.gguf` | GGUF · q8_0 | Highest-quality quant |
| `Juggernaut_Z_V1_by_RunDiffusion_q6_k-004.gguf` | GGUF · q6_k | |
| `Juggernaut_Z_V1_by_RunDiffusion_q5_k_m-003.gguf` | GGUF · q5_k_m | |
| `Juggernaut_Z_V1_by_RunDiffusion_q5_k_s-005.gguf` | GGUF · q5_k_s | |
| `Juggernaut_Z_V1_by_RunDiffusion_q4_k_m-002.gguf` | GGUF · q4_k_m | |
| `Juggernaut_Z_V1_by_RunDiffusion_q4_k_s-001.gguf` | GGUF · q4_k_s | Smallest footprint |
Use the `.safetensors` variants with the workflow that matches your local inference stack. Use the `.gguf` variants with a GGUF-compatible runtime.
## Links
- **Run Juggernaut Z on RunDiffusion** → [rundiffusion.com/juggernaut-z](https://www.rundiffusion.com/juggernaut-z?utm_source=huggingface&utm_medium=model_card&utm_campaign=juggernaut_z_v1&utm_content=footer_run)
- **Prompt guide** → [Juggernaut Z Prompt Guide](https://www.rundiffusion.com/juggernaut-z-prompt-guide?utm_source=huggingface&utm_medium=model_card&utm_campaign=juggernaut_z_v1&utm_content=footer_prompt_guide)
- **Base model** → [Tongyi-MAI/Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image)
## Attribution
Juggernaut Z is built on Z-Image Base — credit for the upstream base model belongs to the Z-Image team. This fine-tuned release is by **Team Juggernaut**, with training by **KandooAI**, published by **RunDiffusion**.
## License
Released under the **Apache 2.0** license.
