ComfyUI Extension: ComfyUI · Egregora: Divide & Enhance

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Egregora: Divide & Enhance is a small suite of custom nodes that help you split, enhance, and recombine images, plus a clean SDXL prompt mixer that keeps things simple while staying robust with lot´s of customization.

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    README

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    ComfyUI · Egregora: Divide & Enhance 🧩🚀

    ⚡A set of ComfyUI nodes inspired by the Divide & Conquer algorithm, designed to split, enhance, and recombine images for high‑quality upscaling, with prompt mixing and analysis tools for cleaner, sharper results⚡

    <img width="1615" height="789" alt="Suite Preview" src="https://github.com/user-attachments/assets/4cc5d352-75fa-4e80-808d-d6a246adbc4e" /> </center>

    ✨ What is this?

    Egregora: Divide & Enhance is a small suite of custom nodes that help you split, enhance, and recombine images, plus a clean SDXL prompt mixer that keeps things simple while staying robust with lot´s of customization.

    Inspired by Steudio’s Divide & Conquer nodes, adapted and refactored to fit a streamlined upscaling workflow. 🧠✂️🧵

    🧱 Included Nodes

    🚀 Egregora Turbo Prompt

    A minimal, predictable prompt builder:

    • Positive = caption_text + global_positive_prompt (plain concatenation; no weights).
    • Negative = global_negative_prompt only.
    • Blacklist (optional): removes whole‑word, case‑insensitive terms from the positive text before encoding.
    • Outputs SDXL‑compatible CONDITIONING with proper pooled_output.

    Typical wiring

    • caption_text ← captioner (e.g., Florence2)
    • global_positive_prompt ← styles/quality you always want
    • global_negative_prompt ← artifacts to avoid
    • blacklist_words ← optional removals

    🧠 Egregora Algorithm

    Planner for Divide‑and‑Enhance upscaling. Computes tile layout given target size & overlap; supports tile ordering strategies and sensible defaults to avoid seams.

    ✂️ Egregora Divide & Select

    Splits an image/latent according to the planner. You can pass all tiles, or pick specific ones for targeted enhancement.

    🔗 Egregora Combine

    Merges processed tiles back into a seamless image using robust blending (distance‑field / multi‑scale style approaches to avoid borders and repetition).

    👁️ Egregora Preview

    Visual overlay to preview the tile grid, overlaps, and order, verify the plan before you spend compute.

    🔍 Egregora Content Analysis (optional)

    Analyzes complexity/detail to hint at better tile sizes or overlaps for tricky images.

    📦 Installation

    1. Clone into ComfyUI’s custom nodes folder:
    cd ComfyUI/custom_nodes
git clone https://github.com/lucasgattas/comfyui-egregora-divide-and-enhance.git
    1. Ensure the folder contains __init__.py and egregora_divide_and_enhance.py.
    2. Restart ComfyUI. Nodes appear under Egregora/.

    Note: You can rename the Python file; the package __init__.py controls what gets imported.

    🛠️ Minimal Workflow (SDXL)

    Goal: Use Turbo Prompt for text conditioning and keep the rest of your graph simple.

    1. Load Model (SDXL) → CLIP

    2. Egregora Turbo Prompt

      • caption_text = your main description
      • global_positive_prompt = secondary style or theme
      • blacklist_words = clean words from positive prompt (caption and global)

    3. KSampler (Euler/Karras or your favorite)

      • Positive/Negative from Turbo Prompt, model & latent as usual.

    4. VAE Decode → Preview / Save

    Upscaling (Divide & Enhance): Use Algorithm → Divide & Select → [process tiles] → Combine. Start with moderate tile sizes and overlap; preview first.

    📥 Example Workflow (.json)

    <img width="1130" height="742" alt="Captura de tela 2025-08-27 220324" src="https://github.com/user-attachments/assets/91735827-b882-45d7-bf17-d90fd23ed100" />
    • Download: examples/divide_and_enhance_example_workflow.json (included in this repo).
    • Import: ComfyUI → Queue (☰) → Load → pick the JSON, or just drag it inside.

    Prefer a one‑click experience? Run the improved, full tuned upscaler on the cloud with the best settings at (https://egregoralabs.com).

    🎚️ Practical Tips

    • Keep prompts concise; long texts dilute signal.
    • For realism vs. cartoon conflicts, add targeted negatives (e.g., cartoon, plush, chibi) so the portrait holds shape while the global style adds texture/color.
    • Turbo Prompt avoids fragile scheduling and mixes prompts via multiple conditionings (sampler‑level combine).

    🧪 Troubleshooting

    • Flat/grey outputs → raise steps slightly or increase the stronger slider; ensure the CLIP/model are SDXL compatible.
    • Repetitions/Grid looks → verify no external tiling patches are active; reduce extreme overlaps or tile size if using the Divide/Combine flow.
    • Shape/key errors → update to the latest version; Turbo Prompt sets pooled_output and ADM keys; make sure you pass a valid LATENT.

    🧾 Folder Structure

    comfyui-egregora-divide-and-enhance/
├─ __init__.py
├─ egregora_divide_and_enhance.py
├─ README.md  ← you are here
├─ examples/
└─  └─ divide_and_enhance_example_workflow.json

    🙌 Credits

    • Inspired by Steudio’s Divide & Conquer node suite and community best practices around SDXL conditioning and tiling.
    • Built for clarity: minimal controls, sensible defaults, strong integration with ComfyUI.

    📜 License

    GPL‑3.0 — see LICENSE.

    Changelog

    • 0.1.0 — Initial release: Turbo Prompt with latent‑sized ADM; Divide/Select/Combine/Preview/Analysis nodes.