Refresh model card: add RD branding, fp16 listing, UTM links, polish copy

README.md

@@ -12,41 +12,46 @@ tags:
 - 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)
+<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="140">
+</a>
 
-</div>
+# Juggernaut Z
 
-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.
+**A polished, cinematic fine-tune of Z-Image Base — by RunDiffusion.**
 
-This repository is intended to host the RunDiffusion release artifacts for Juggernaut Z, including full-precision weights and GGUF quantizations.
+[![Run on RunDiffusion](https://img.shields.io/badge/Run%20it-RunDiffusion-111111?style=for-the-badge)](https://www.rundiffusion.com/juggernaut-z?utm_source=huggingface&utm_medium=model_card&utm_campaign=juggernaut_z_v1&utm_content=cta_primary)
+[![Base Model](https://img.shields.io/badge/%F0%9F%A4%97%20Base%20Model-Z--Image-yellow?style=for-the-badge)](https://huggingface.co/Tongyi-MAI/Z-Image)
+[![Prompt Guide](https://img.shields.io/badge/Prompt%20Guide-RunDiffusion-2f6fed?style=for-the-badge)](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)
 
-![Juggernaut Z Hero](https://www.rundiffusion.com/images/juggernaut-z/hero-image.jpg)
+</div>
 
-## Overview
+---
+
+Juggernaut Z is a fine-tune of **Z-Image Base** by **Team Juggernaut**, trained by **KandooAI**, and released through **RunDiffusion**. It is tuned for a presentation-ready look out of the box — stronger lighting, sharper focus, more refined skin texture, and more cinematic atmosphere.
 
-Juggernaut Z is tuned for a more presentation-ready look out of the box. Relative to Z-Image Base, the emphasis is on:
+This repository hosts the official RunDiffusion release artifacts: full-precision weights, an FP8 variant, and a full set of GGUF quantizations.
 
-- 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
+![Juggernaut Z Hero](https://www.rundiffusion.com/images/juggernaut-z/hero-image.jpg)
 
-## Website Comparisons
+## Highlights
 
-The comparison sets below are taken from the RunDiffusion Juggernaut Z announcement page.
+- 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
 
-**Comparison label:** Left: **Juggernaut Z** · Right: **Z Image Base**
+## Comparisons
 
-### Better Lighting
+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).
 
-More dramatic, cinematic lighting quality out of the box.
+### Lighting
+
+More dramatic, cinematic lighting out of the box.
 
 ![Lighting 1](https://www.rundiffusion.com/images/juggernaut-z/lighting-1.png)
 ![Lighting 2](https://www.rundiffusion.com/images/juggernaut-z/lighting-2.png)
@@ -55,9 +60,9 @@ More dramatic, cinematic lighting quality out of the box.
 ![Lighting 5](https://www.rundiffusion.com/images/juggernaut-z/lighting-5.jpg)
 ![Lighting 6](https://www.rundiffusion.com/images/juggernaut-z/lighting-6.jpg)
 
-### Improved Textures
+### Skin & Texture
 
-Juggernaut Z produces cleaner and more natural-looking skin textures, especially in close-up portraits.
+Cleaner, more natural-looking skin — especially in close-up portraits.
 
 ![Skin 1](https://www.rundiffusion.com/images/juggernaut-z/skin-1.png)
 ![Skin 2](https://www.rundiffusion.com/images/juggernaut-z/skin-2.png)
@@ -66,7 +71,7 @@ Juggernaut Z produces cleaner and more natural-looking skin textures, especially
 
 ### Anatomy
 
-We have worked to improve overall image integrity, resulting in cleaner anatomy and more consistent structural detail across a wide variety of subjects.
+Cleaner anatomy and more consistent structural detail across a wide range of subjects.
 
 ![Anatomy 1](https://www.rundiffusion.com/images/juggernaut-z/anatomy-1.png)
 ![Anatomy 2](https://www.rundiffusion.com/images/juggernaut-z/anatomy-2.png)
@@ -75,7 +80,7 @@ We have worked to improve overall image integrity, resulting in cleaner anatomy
 
 ### Composition
 
-Subject and object placement within scenes improved in version one, with continued improvements planned for version two.
+Improved subject and object placement within scenes, with further work planned for v2.
 
 ![Composition 1](https://www.rundiffusion.com/images/juggernaut-z/composition-1.png)
 ![Composition 2](https://www.rundiffusion.com/images/juggernaut-z/composition-2.png)
@@ -83,7 +88,7 @@ Subject and object placement within scenes improved in version one, with continu
 
 ### Diversity
 
-The model is tuned toward a more balanced result across ethnic backgrounds, with better representation out of the box.
+More balanced results across ethnic backgrounds, with better representation by default.
 
 ![Diversity 1](https://www.rundiffusion.com/images/juggernaut-z/diversity-1.png)
 ![Diversity 2](https://www.rundiffusion.com/images/juggernaut-z/diversity-2.png)
@@ -92,52 +97,51 @@ The model is tuned toward a more balanced result across ethnic backgrounds, with
 
 ### Architecture
 
-Architectural subjects benefit from cleaner structural lines and more coherent material rendering.
+Cleaner structural lines and more coherent material rendering.
 
 ![Architecture 1](https://www.rundiffusion.com/images/juggernaut-z/arch-new-1.jpg)
 ![Architecture 2](https://www.rundiffusion.com/images/juggernaut-z/arch-new-2.jpg)
 
 ## 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`
+| 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 stronger lighting and clearer atmosphere
+- 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
 
-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`
+| 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 |
 
-## 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.
+Use the `.safetensors` variants with the workflow that matches your local inference stack. Use the `.gguf` variants with a GGUF-compatible runtime.
 
 ## 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)
+- **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 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.
+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.