Juggernaut Pro FLUX: The best AI image generation model for photorealistic quality
Meet Juggernaut Pro FLUX, the flagship model in a new family of AI image generators that creates stunningly realistic visuals. Check out what it can do and how to get started with this game-changing model.
Introduction
Today, in partnership with RunDiffusion, we're excited to announce the launch of Juggernaut FLUX, a family of models designed to take photorealistic image generation to the next level. These models, available on our platform, combine cutting-edge technology with careful fine-tuning to deliver unprecedented levels of detail, sharpness, and realism.
Starting with Juggernaut Pro FLUX, this model is the result of extensive research and development aimed at addressing common limitations in current image generation models. It maintains the creative flexibility of the FLUX architecture while dramatically enhancing image quality, fixing known issues, and expanding the boundaries of what's possible with current AI generation technologies.
The Pro version comes as the flagship in a thoughtfully designed ecosystem of complementary models:
- For those seeking different balances of performance and capability, the Juggernaut Base FLUX provides a solid foundation with most of the quality improvements while maintaining complete compatibility with existing FLUX ecosystem assets.
- If speed is a priority, the Juggernaut Lightning FLUX offers reduced inference times through advanced model distillation techniques, without much impact on visual quality. This also translates into lower costs and more efficient workflows.
- For maximum flexibility, RunDiffusion Photo FLUX LoRA allows you to inject the core photorealistic enhancements into any existing workflow or base model, making it possible to experience the benefits without completely changing your established pipeline.
Each variant maintains the core improvements while optimizing for specific workflow requirements.
What makes Juggernaut Pro FLUX special?
Juggernaut Pro FLUX has been developed with a clear goal: to maintain the essence of the FLUX Dev model while eliminating its primary limitations. This approach preserves the familiarity of FLUX Dev but introduces critical improvements in color, clarity, sharpness, and especially skin details. One of the biggest improvements is removing the persistent "wax effect" in FLUX Dev and improving depth of field and blur for more realistic results. The result is a model that produces consistently superior images, with a notably more natural appearance and less "plastic" feel than even Black Forest Lab's FLUX Pro, especially in the representation of people and organic textures.
Non-destructive training approach
Unlike many fine-tuned models that sacrifice the original model's capabilities for new features, Juggernaut Pro FLUX was trained using a specialized non-destructive methodology. This approach preserves the core strengths of the FLUX architecture while enhancing its performance in targeted areas.
The result is a model that feels familiar to FLUX users but delivers consistently superior results across various prompts and settings. This careful balance ensures that the model remains versatile while offering substantial improvements in quality.
Enhanced visual fidelity
The model introduces improvements to several aspects of image quality:
- Superior sharpness and detail: Images show markedly improved fine details, particularly in textures, facial features, and complex patterns.
- Optimized contrast: Better dynamic range leads to more vivid images without sacrificing realism.
- Improved focus: Reduced issues with blurry elements, even in complex compositions.
- Reduced common artifacts: Known issues in FLUX models, including the "chin problem" that often affected portrait generation, are less pronounced in Juggernaut Pro FLUX.
- Natural textures: Eliminates the waxy or plastic appearance that characterize official models, delivering more realistic skin tones and organic surfaces.
High-resolution support
Juggernaut Pro FLUX excels at generating high-resolution images, supporting impressive dimensions such as 1536×1536. It even enables interesting aspect ratios like 600×2048 without the quality degradation typically seen in such extreme formats.
This capability makes the model particularly valuable for creating images intended for professional use, where resolution requirements may exceed the limitations of standard generative models.
Optimal generation parameters
- juggernaut
- flux
- rundiffusion
- pro
- photorealism
Getting the best results from Juggernaut Pro FLUX requires understanding its optimal parameters. Through extensive testing, we've identified configuration settings that consistently produce exceptional images.
Recommended samplers
- Euler Beta: This sampler provides the most balanced results across different types of prompts and has become our go-to recommendation for most use cases.
CFG scale settings
Juggernaut Pro FLUX responds differently to CFG values compared to some other models:
- 2.5: Ideal for photorealistic results with natural compositions.
- 3.5: Better for artistic interpretations with more creative freedom.
- 3.0: Recommended when using the Beta schedule type.
These values are starting points and can be adjusted based on your specific needs. Lower values tend to produce more creative but potentially less prompt-aligned results, while higher values strengthen adherence to the prompt at the potential cost of some natural aesthetics.
Step count
Optimal results typically emerge in the 25-35 step range. Unlike some models that require 50+ steps for quality outputs, Juggernaut Pro FLUX achieves excellent results with fewer steps, improving generation efficiency without sacrificing quality.
Getting started with Juggernaut Pro FLUX
Integrating Juggernaut Pro FLUX into your workflows is straightforward using our API. Here's a basic example to get you started:
[
{
"taskType": "imageInference",
"taskUUID": "f47ac10b-58cc-4372-a567-0e02b2c3d479",
"positivePrompt": "macro photography, blooming rose, natural light, in the style of Nathan Wirth, UHD, photorealistic, shot on a Sony A7III",
"height": 1536,
"width": 1024,
"model": "rundiffusion:130@100",
"steps": 33,
"scheduler": "Euler Beta",
"CFGScale": 3,
"numberResults": 4
}
]const { Runware } = require('@runware/sdk-js');
async function generateImages(prompt, numberOfImages = 4) {
const runware = new Runware({ apiKey: '<API_KEY>' });
try {
const images = await runware.requestImages({
positivePrompt: prompt,
model: 'rundiffusion:130@100',
width: 832,
height: 1216,
steps: 33,
scheduler: 'Euler Beta',
CFGScale: 3,
numberResults: numberOfImages,
});
console.group('Image Generation Results:');
images.forEach((image, imgNO) => {
console.group(`Image ${imgNO + 1}:`);
console.log('Task UUID:', image.taskUUID);
console.log('Image UUID:', image.imageUUID);
console.log('Image URL:', image.imageURL);
console.log('Seed:', image.seed);
console.groupEnd();
});
console.groupEnd();
return images;
} catch (err) {
console.error('Error generating images:', err.error.message);
} finally {
await runware.disconnect();
}
}
const prompt = 'macro photography, blooming rose, natural light, in the style of Nathan Wirth, UHD, photorealistic, shot on a Sony A7III';
generateImages(prompt, 1);import asyncio
from runware import Runware, IImageInference
async def generate_images(prompt, number_of_images=4):
api_key = "API KEY Here"
runware = Runware(api_key=api_key)
try:
await runware.connect()
request_image = IImageInference(
positivePrompt=prompt,
model="rundiffusion:130@100",
width=832,
height=1216,
steps=33,
scheduler="Euler Beta",
CFGScale=3,
numberResults=number_of_images,
)
images = await runware.imageInference(requestImage=request_image)
print("Image Generation Results:\n")
for imgNO, image in enumerate(images, 1):
print(f"Image {imgNO}:")
print(f" Task UUID: {image.taskUUID}")
print(f" Image UUID: {image.imageUUID}")
print(f" Image URL: {image.imageURL}")
print(f" Seed: {image.seed}")
return images
except Exception as e:
print(f"Error generating images:\n{str(e)}")
return False
if __name__ == "__main__":
prompt = "macro photography, blooming rose, natural light, in the style of Nathan Wirth, UHD, photorealistic, shot on a Sony A7III"
asyncio.run(generate_images(prompt, 1))#!/bin/bash
uuid=$(openssl rand -hex 16)
taskUUID="${uuid:0:8}-${uuid:8:4}-4${uuid:12:3}-a${uuid:17:3}-${uuid:20:12}"
curl -k -X POST "https://api.runware.ai/v1" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer <API_KEY>" \
-d '[{
"taskType": "imageInference",
"taskUUID": "'$taskUUID'",
"model": "rundiffusion:130@100",
"positivePrompt": "macro photography, blooming rose, natural light, in the style of Nathan Wirth, UHD, photorealistic, shot on a Sony A7III",
"width": 832,
"height": 1216,
"steps": 33,
"scheduler": "Euler Beta",
"CFGScale": 3,
"numberResults": 4
}]'You can find complete documentation and additional examples in our API reference. You will need an API key to access this model - if you don't have one yet, you can sign up here.
Juggernaut FLUX ecosystem
While Juggernaut Pro FLUX stands as our flagship offering, it's actually part of a broader ecosystem of models designed to provide flexibility for different workflows and use cases.
Juggernaut Lightning FLUX
This distilled version of the model is designed for applications where speed is critical. Key features include:
- Reduced inference times through advanced model distillation techniques.
- Substantial inference cost savings due to lower resource requirements.
- Preserves most of the quality improvements found in Juggernaut Base FLUX.
- Ideal for real-time applications, interactive experiences, and high-throughput workflows.
- juggernaut
- flux
- rundiffusion
- schnell
- Lightning
- photorealism
Juggernaut Lightning FLUX will be perfect for developers who need to generate images at scale or integrate high-quality image generation into responsive applications where wait times would disrupt the user experience.
Juggernaut Base FLUX
This full fine-tune model serves as an excellent alternative to FLUX Dev with improvements:
- Full compatibility with existing FLUX Dev LoRAs and LyCORIS models.
- Enhanced sharpness, contrast, and detail rendering.
- Provides a solid foundation for further customization through additional LoRAs.
- juggernaut
- flux
- rundiffusion
- photorealism
Juggernaut Base FLUX is particularly valuable for users who work extensively with existing FLUX ecosystem LoRAs and want improved baseline quality without disrupting their established workflows.
RunDiffusion Photo FLUX LoRA
This specialized LoRA adds photorealistic qualities to any base model you apply it to. Key features include:
- Enables high-resolution generation similar to the full model.
- Highly adjustable through weight settings (recommended starting point: 0.66 to 0.75).
- Perfect for enhancing existing workflows without switching base models.
- rundiffusion
- photo
- flux
- pro
- photorealism
RunDiffusion Photo FLUX LoRA is ideal for users who have established pipelines with other FLUX base models, but want to incorporate some of the photorealistic enhancements.
Conclusion
Juggernaut Pro FLUX represents a remarkable advancement in image generation technology, offering levels of quality, control, and versatility never seen before. Its availability on our platform ensures that you're experiencing the model with optimal infrastructure and support.
We invite you to explore these models through our Playground and API. Whether you're a creative professional, developer, or AI enthusiast, the entire Juggernaut FLUX ecosystem opens new possibilities for creating stunning visual content. Start creating today and discover how these technical improvements translate into tangible benefits in your projects.