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.

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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.

Lighting

More dramatic, cinematic lighting out of the box.

Lighting 1 Lighting 2 Lighting 3 Lighting 4 Lighting 5 Lighting 6

Skin & Texture

Cleaner, more natural-looking skin — especially in close-up portraits.

Skin 1 Skin 2 Skin 3 Skin 4

Anatomy

Cleaner anatomy and more consistent structural detail across a wide range of subjects.

Anatomy 1 Anatomy 2 Anatomy 3 Composition 3

Composition

Improved subject and object placement within scenes, with further work planned for v2.

Composition 1 Composition 2 Anatomy 4

Diversity

More balanced results across ethnic backgrounds, with better representation by default.

Diversity 1 Diversity 2 Diversity 3 Diversity 4

Architecture

Cleaner structural lines and more coherent material rendering.

Architecture 1 Architecture 2

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 (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")

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.

Use with 🤗 Diffusers

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:

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 below.

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 & Commercial Use

Juggernaut Z is released under CC BY-NC 4.0:

• 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.

You are free to fine-tune, merge, build LoRAs, and otherwise modify the model for non-commercial purposes.

For commercial licensing, custom models, business inquiries, or consultation, contact [email protected].