| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| pipeline_tag: text-to-image |
| base_model: Tongyi-MAI/Z-Image |
| tags: |
| - gguf |
| - safetensors |
| - text-to-image |
| - rundiffusion |
| - z-image |
| --- |
| |
| <div align="center"> |
|
|
| <a href="https://www.rundiffusion.com/?utm_source=huggingface&utm_medium=model_card&utm_campaign=juggernaut_z_v1&utm_content=header_logo"> |
| <img src="https://huggingface.co/RunDiffusion/Juggernaut-Z-Image/resolve/main/assets/RD_Mark.png" alt="RunDiffusion" width="110" /> |
| </a> |
|
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| <h1>Juggernaut Z by RunDiffusion</h1> |
|
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| <p><i>A cinematic fine-tune of Z-Image Base — tuned for presentation-ready output.</i></p> |
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| <p> |
| <a href="https://www.rundiffusion.com/juggernaut-z?utm_source=huggingface&utm_medium=model_card&utm_campaign=juggernaut_z_v1&utm_content=cta_primary"><img alt="Try Juggernaut Z" src="https://img.shields.io/badge/%E2%96%B6%20Try%20Juggernaut%20Z-7C3AED?style=for-the-badge&labelColor=7C3AED"></a> <a href="https://www.rundiffusion.com/juggernaut-z-prompt-guide?utm_source=huggingface&utm_medium=model_card&utm_campaign=juggernaut_z_v1&utm_content=prompt_guide_badge"><img alt="Prompt Guide" src="https://img.shields.io/badge/Prompt%20Guide-1f1f23?style=for-the-badge"></a> <a href="https://huggingface.co/Tongyi-MAI/Z-Image"><img alt="Base Model: Z-Image" src="https://img.shields.io/badge/%F0%9F%A4%97%20Base%20Model-Z--Image-FFD21E?style=for-the-badge&labelColor=1f1f23"></a> <img alt="License: CC BY-NC 4.0" src="https://img.shields.io/badge/License-CC%20BY--NC%204.0-2ea44f?style=for-the-badge"> |
| </p> |
|
|
| </div> |
|
|
| <p align="center"> |
| <img src="https://www.rundiffusion.com/images/juggernaut-z/hero-image.jpg" alt="Juggernaut Z hero" /> |
| </p> |
|
|
| > 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. |
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| 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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| --- |
|
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| ## Highlights |
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| - 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 |
|
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| 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). |
|
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| ### Lighting |
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| More dramatic, cinematic lighting out of the box. |
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| ### Skin & Texture |
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| Cleaner, more natural-looking skin — especially in close-up portraits. |
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| ### Anatomy |
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| Cleaner anatomy and more consistent structural detail across a wide range of subjects. |
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| ### Composition |
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| Improved subject and object placement within scenes, with further work planned for v2. |
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| ### Diversity |
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| More balanced results across ethnic backgrounds, with better representation by default. |
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| ### Architecture |
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| Cleaner structural lines and more coherent material rendering. |
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| ## Recommended Settings |
|
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| | Parameter | Default | Range | |
| | --- | --- | --- | |
| | CFG | `6` | `6 – 9` | |
| | Steps | `35` | `25 – 45` | |
|
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| ## Good Fit For |
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| - 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 (bf16) | Original release 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 | |
| | `model_index.json` + `transformer/`, `text_encoder/`, `tokenizer/`, `vae/`, `scheduler/` | 🤗 Diffusers format | Loaded by `DiffusionPipeline.from_pretrained("RunDiffusion/Juggernaut-Z-Image")` | |
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| Use the `.safetensors` variants with the workflow that matches your local inference stack. Use the `.gguf` variants with a GGUF-compatible runtime. Use the Diffusers component layout with the 🤗 Diffusers library — see below. |
|
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| ## Use with 🤗 Diffusers |
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| The repo includes `model_index.json` and the standard 🤗 Diffusers component directories (`transformer/`, `text_encoder/`, `tokenizer/`, `vae/`, `scheduler/`) at the root, exported as a `ZImagePipeline`. Load it with: |
|
|
| ```python |
| from diffusers import DiffusionPipeline |
| import torch |
| |
| pipe = DiffusionPipeline.from_pretrained( |
| "RunDiffusion/Juggernaut-Z-Image", |
| torch_dtype=torch.bfloat16, |
| ).to("cuda") |
| |
| image = pipe( |
| "a cinematic portrait, dramatic lighting", |
| guidance_scale=6.0, |
| num_inference_steps=35, |
| ).images[0] |
| image.save("output.png") |
| ``` |
|
|
| `from_pretrained` only downloads files declared in `model_index.json`, so it will not pull the standalone `.safetensors` / `.gguf` variants at the repo root. Requires a version of `diffusers` that includes `ZImagePipeline` support (verified against `diffusers` 0.37.1 and 0.38.0). Commercial use of the model and its outputs is restricted under CC BY-NC 4.0 — see [License & Commercial Use](#license--commercial-use) below. |
|
|
| ## 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**. |
|
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| ## License & Commercial Use |
|
|
| Juggernaut Z is released under **[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)**: |
|
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| - **BY** — attribute RunDiffusion / Team Juggernaut / KandooAI when sharing output. |
| - **NC** — **non-commercial use only**. You may not use the model — or its outputs in a workflow — for commercial purposes without a license. |
|
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| You are free to fine-tune, merge, build LoRAs, and otherwise modify the model for non-commercial purposes. |
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| **For commercial licensing**, custom models, business inquiries, or consultation, contact **[juggernaut@rundiffusion.com](mailto:juggernaut@rundiffusion.com)**. |
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|
