Mindket

Juggernaut-XL-v9

Newversion 1 · updated

Overview

Makes photorealistic images from text prompts, as a Stable Diffusion XL fine-tune that the card says runs on 8 GB of video memory. For creators who want photo-style results with existing SDXL tools.

Juggernaut-XL-v9 is a kit in the Marketing & SEO category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page, a zip of its files. Nothing in it runs by itself.

At a glance

You need
  • Python on Windows, macOS or Linux
  • The huggingface_hub package
  • A graphics card with about 8 GB of video memory
  • The diffusers library, or one of the SDXL interfaces listed above
Runs in
  • ComfyUI, Forge, Automatic1111, Fooocus, InvokeAI or SwarmUI, which the card lists as supported
  • The diffusers library in Python, for local runs
  • RunDiffusion's hosted service, which the card offers with a free trial
  • Windows, macOS or Linux with Python, to download the files
Start with
pip install -U huggingface_hub
hf download RunDiffusion/Juggernaut-XL-v9

Platform

WindowsOperating system
macOSOperating system
LinuxOperating system

About

What it is

Juggernaut XL v9 is a text-to-image model from RunDiffusion, fine-tuned from Stable Diffusion XL base 1.0 for photorealism. Version 9 was built with KandooAI and folds in RunDiffusion Photo v2. The card says it improves skin detail, lighting and contrast over version 8.

Who it suits

It suits creators and studios who want photo-style images from SDXL and already use tools from the SDXL family.

IndustryWho uses itHow they use itExample
Photography and artDigital artistsMake photo-style images from a written promptDrafting a portrait with soft window light
Architecture and interiorsDesignersVisualize rooms and buildingsSketching an interior scene from a short description
Food and productMarketing teamsProduce concept images for campaignsCreating a plated-dish concept picture
Local AI hobbyEnthusiastsRun an image model on a home GPUUsing it inside a ComfyUI workflow

You can skip it if you want the newest image architectures, since the card points to other Juggernaut models for frontier work.

Where to use it

  • ComfyUI, Forge, Automatic1111, Fooocus, InvokeAI or SwarmUI, which the card lists as supported
  • The diffusers library in Python, for local runs
  • RunDiffusion's hosted service, which the card offers with a free trial
  • Windows, macOS or Linux with Python, to download the files
Use caseWhoWhat they get back
Photorealistic portraitsArtistsImages with detailed skin and natural lighting
Scenes and interiorsDesignersPictures of buildings, interiors and outdoor scenes
SDXL add-onsPower usersCompatibility with SDXL ControlNets, IP-Adapter and LoRAs

How to set it up

You need Python for the download tool, plus a graphics card. The card says it runs comfortably on 8 GB of video memory.

  • Python on Windows, macOS or Linux
  • The huggingface_hub package
  • A graphics card with about 8 GB of video memory
  • The diffusers library, or one of the SDXL interfaces listed above
  1. Install huggingface_hub.
  2. Download the model files.
  3. Load them in diffusers or in your SDXL interface.
pip install -U huggingface_hub
hf download RunDiffusion/Juggernaut-XL-v9

How to use it

The card offers two routes, a hosted one on RunDiffusion and a local one with diffusers. The local code snippet is cut off in the part shown here, so follow the card for the exact lines. In an interface such as ComfyUI, you load the checkpoint, type a prompt and generate.

Example

You load the checkpoint in a ComfyUI workflow and type a short prompt describing a portrait by a window. The model returns a photo-style image of that scene.

For newcomers and experts

If you are new to this, you describe a picture in words and the model paints it so that it looks like a photograph. A first small try is a short prompt in one of the interfaces listed above.

For people who know the field, this is an SDXL fine-tune, so it works with the SDXL add-ons you may already use, including ControlNets, IP-Adapter, AnimateDiff and regional prompting. The card says newer DiT-based models demand 16 GB of video memory or more, while this one needs about 8 GB.

  1. You write a prompt
  2. Your interface or diffusers loads the SDXL model
  3. The model builds an image from noise
  4. You get a photo-style picture

Good to know

Download and rating figures in the card come from its authors. The card puts the model forward as an SDXL photorealism option.

  • License: CreativeML OpenRAIL-M, so check its terms before commercial use
  • Cost to run: none named for local use, and the hosted service has a free trial
  • Data it reaches: your prompts and generated images, plus the downloaded files
  • Not for: work that needs a newer architecture than SDXL

README and files

README

Juggernaut-XL-v9

Makes photorealistic images from text prompts, as a Stable Diffusion XL fine-tune that the card says runs on 8 GB of video memory. For creators who want photo-style results with existing SDXL tools.

Made by RunDiffusion on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

About

What it is: Juggernaut XL v9 is a text-to-image model from RunDiffusion, fine-tuned from Stable Diffusion XL base 1.0 for photorealism. Version 9 was built with KandooAI and folds in RunDiffusion Photo v2. The card says it improves skin detail, lighting and contrast over version 8.

Who it suits: It suits creators and studios who want photo-style images from SDXL and already use tools from the SDXL family.

Industry | Who uses it | How they use it | Example
Photography and art | Digital artists | Make photo-style images from a written prompt | Drafting a portrait with soft window light
Architecture and interiors | Designers | Visualize rooms and buildings | Sketching an interior scene from a short description
Food and product | Marketing teams | Produce concept images for campaigns | Creating a plated-dish concept picture
Local AI hobby | Enthusiasts | Run an image model on a home GPU | Using it inside a ComfyUI workflow

You can skip it if you want the newest image architectures, since the card points to other Juggernaut models for frontier work.

Where to use it:

  • ComfyUI, Forge, Automatic1111, Fooocus, InvokeAI or SwarmUI, which the card lists as supported
  • The diffusers library in Python, for local runs
  • RunDiffusion's hosted service, which the card offers with a free trial
  • Windows, macOS or Linux with Python, to download the files
Use case | Who | What they get back
Photorealistic portraits | Artists | Images with detailed skin and natural lighting
Scenes and interiors | Designers | Pictures of buildings, interiors and outdoor scenes
SDXL add-ons | Power users | Compatibility with SDXL ControlNets, IP-Adapter and LoRAs

How to set it up: You need Python for the download tool, plus a graphics card. The card says it runs comfortably on 8 GB of video memory.

  • Python on Windows, macOS or Linux
  • The huggingface_hub package
  • A graphics card with about 8 GB of video memory
  • The diffusers library, or one of the SDXL interfaces listed above
  1. Install huggingface_hub.
  2. Download the model files.
  3. Load them in diffusers or in your SDXL interface.
pip install -U huggingface_hub
hf download RunDiffusion/Juggernaut-XL-v9

How to use it: The card offers two routes, a hosted one on RunDiffusion and a local one with diffusers. The local code snippet is cut off in the part shown here, so follow the card for the exact lines. In an interface such as ComfyUI, you load the checkpoint, type a prompt and generate.

Example: You load the checkpoint in a ComfyUI workflow and type a short prompt describing a portrait by a window. The model returns a photo-style image of that scene.

For newcomers and experts: If you are new to this, you describe a picture in words and the model paints it so that it looks like a photograph. A first small try is a short prompt in one of the interfaces listed above.

For people who know the field, this is an SDXL fine-tune, so it works with the SDXL add-ons you may already use, including ControlNets, IP-Adapter, AnimateDiff and regional prompting. The card says newer DiT-based models demand 16 GB of video memory or more, while this one needs about 8 GB.

You write a prompt
Your interface or diffusers loads the SDXL model
The model builds an image from noise
You get a photo-style picture

Good to know: Download and rating figures in the card come from its authors. The card puts the model forward as an SDXL photorealism option.

  • License: CreativeML OpenRAIL-M, so check its terms before commercial use
  • Cost to run: none named for local use, and the hosted service has a free trial
  • Data it reaches: your prompts and generated images, plus the downloaded files
  • Not for: work that needs a newer architecture than SDXL

On Hugging Face, 13 November 2026

  • Task: text to image
  • Library: diffusers
  • Downloads in the last 30 days: 587,793
  • Likes: 521
  • License: creativeml-openrail-m
  • Languages: en
  • Based on: stabilityai/stable-diffusion-xl-base-1.0
  • Quantized versions: 3
  • Fine-tunes: 4
  • Tags: diffusers, stable-diffusion, stable-diffusion-xl, sdxl, text-to-image, photorealistic, photography, cinematic, portrait, juggernaut, rundiffusion, kandooai

Links

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download RunDiffusion/Juggernaut-XL-v9

Then follow the usage in its model card. Nothing in this kit runs by itself.

Files
README.md  5 KB

1 file, 5 KB, downloaded as a zip.

What’s new

  • Version 1 · 2026-11-12

Compare with similar

Listed Hugging Face models in the same category that share the most tags with this one.

ProductDownloads, last 30 daysLikesParametersLicenseLast change
Juggernaut-XL-v9 This page587,793521—creativeml-openrail-m
CyberRealistic XLShares photorealistic · stable-diffusion-xl · text-to-image67,83454—creativeml-openrail-m
Moss Image Beauty LoRAShares photography · portrait · text-to-image5,54622—apache-2.0
Pixel Art XLShares stable-diffusion · text-to-image · diffusers38,781669—creativeml-openrail-m
Flux.2-Klein-9B-MatchingPoseShares text-to-image · diffusers3,31949—apache-2.0
HiDream-I1-FullShares text-to-image · diffusers6951,01517.1Bmit
Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14.

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