⚡ Illustrious Workflow as of version 18
🛠️ Purpose & Design Philosophy
This workflow is a high-fidelity environment built for Illustrious XL. It prioritizes stability and professional texture over generation speed. It follows an "all-in-one" philosophy: configure your prompts, hit queue, and let the workflow handle the multi-stage refinement from start to finish.
Not for Speed: This is a heavy-duty refinement tool. If you want 2-second previews, use a basic SDXL workflow.
Personal Use: Built for my specific production needs. It is shared as-is for those who want a "set-and-forget" pipeline for Illustrious.
All-in-One Logic: The workflow handles generation, detailing, and upscaling in one continuous pass.
🚀 Key Features & 2026 Logic
Global Model Patching (RescaleCFG): Includes a pre-configured RescaleCFG patch (Multiplier: 0.7) applied globally. This acts as "HDR Insurance," preventing the "deep-fried" or over-saturated look common in high-CFG Illustrious runs.
Detail Daemon Sampler: Integrated to enhance structural depth. In this version, it is tuned to start at 0.4 to preserve the core Illustrious character proportions while sharpening hair and eye details.
Hybrid Upscale Strategy: * Group Bypass Switch: Easily toggle between a Pixel-only (Lanczos) path for flat anime styles and an Upscale-Model path for 2.5D/highly detailed renders.
Ultimate SD Upscale: Re-draws the upscaled canvas at a 0.35 denoise to lock in fine textures.
Power LoRA Loader: Manage multiple Illustrious-specific LoRAs without messy wiring.
Triple Detailer Groups: 3-stage targeted refinement for faces, hands, and clothing using standard detection models.
CivitAI Meta-Sync: Images are saved with full metadata (Model, LoRAs, Sampler info) for automatic site parsing.
⚠️ Disclaimer & Compatibility
Install at Your Own Risk: Custom nodes can break your environment. I am not responsible for troubleshooting your specific installation.
ComfyUI Portable: Built and tested on the Portable version. Desktop app users may face additional hurdles.
The "Your Version" Factor: Your node versions and environment are 99.9% likely to differ from mine.
Nodes 2.0: I do not recommend using Nodes 2.0. It creates unpredictable UI behavior; I will not provide support for issues involving this feature.
🤝 Support & Boundaries
No DMs: DMs are disabled due to repeat spam. Please check the Discussions tab below; most questions have already been answered.
Modifications: You are free to hack this workflow apart. However, you are responsible for fixing it if it breaks.
Custom Requests: I do not make private workflows. If you need a custom solution, post a Bounty on CivitAI. There are many talented creators ready to help you for a fee.
Description
v9e changes:
Small adjustments. Did some more cleaning up of the noodles. The remaining group names should now be fully uncovered and readable.
Added an Upscale>Downscale group before USDU1.
This can help the output come out better, but YMMV.
The default settings will have it run the image through an upscale model and then downscale it to the original image size before feeding the image into USDU.
Alternatively, you could change the Upscale setting to 2 and change the USDU to 1. This would basically make it behave the same as the USDU (No Upscale) node. The output does come out different if you do it this way, but feel free to test it out yourself.
Added FreeU_V2 to the Dynamic Thresholding groups.
I don't recommend using FreeU unless you know what you're doing or are willing to learn about it on your own.
It can help, but it's not a one-size-fits-all solution for every model.
Added Concat Conditionings for the Positive Prompt.
From the ComfyUI wiki: Imagine that you are cooking a dish, "conditioning_to" is the basic recipe, and "conditioning_from" are some additional seasonings or condiments. The ConditioningConcat class is like a tool that helps you add these seasonings to the recipe, making your dish more colorful and rich.
The usual Positive Prompt on the ImpactWildcardEncode node will act as the "conditioning_to" and the text node below is will act as the "conditioning_from".
I tried using this with other Save Image nodes other than Image Saver, but they do not capture the full prompt. Just FYI in case you decide to swap out Image Saver for something else.