Total runs:
173.4K
Run Growth:
-60.1K
Growth Rate:
-34.64%
Sponsored by
Vmake Labs Video Enhancer
RunDiffusion / Juggernaut-Z-Image
Model's Last Updated: May 14 2026
text-to-image
Introduction of Juggernaut-Z-Image
Model Details of Juggernaut-Z-Image
Juggernaut Z by RunDiffusion
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.
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 .
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
- Prompt guide → Juggernaut Z Prompt Guide
- Base model → 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.
Runs of RunDiffusion Juggernaut-Z-Image on huggingface.co
6.4K
Total runs
0
24-hour runs
190
3-day runs
486
7-day runs
-11.3K
30-day runs
More Information About Juggernaut-Z-Image huggingface.co Model
Url of Juggernaut-Z-Image
Juggernaut-Z-Image huggingface.co Url
Provider of Juggernaut-Z-Image huggingface.co
Other API from RunDiffusion
Updated:May 09 2026
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29.1K
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38.97%
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