ComfyUI-AngelosKar
Productivity custom nodes for ComfyUI: image list scheduler, auto image list loader, Windows folder picker, multi-garment VTON controller, and canvas tag muter/bypasser.
ComfyUI-AngelosKar
A clean, modular custom node pack for ComfyUI focused on automated batch image processing, modern folder selection, multi-garment VTON workflow control, and dynamic canvas tag toggling.
🌟 Features
- Sequential Image Scheduling: Process entire folders chunk-by-chunk using native ComfyUI Run on Change without moving or modifying files.
- VRAM-Safe List Execution: Outputs images as Python lists rather than heavy 4D tensors, enabling ComfyUI list mapping to process images individually.
- Canvas Tag Controllers: Dynamically Bypass or Mute canvas nodes simply by adding tags (e.g.,
[RMBG],[PREVIEW]) to their titles. - Multi-Garment VTON Mode Control: Switch between 1 to 5 garments dynamically with automatic canvas muting for
[G2],[G3],[G4],[G5]. - Native Windows Folder Dialog: Open modern Windows Explorer folder picker directly from the node.
- One-Click Counter Resets: Interactive frontend buttons to instantly reset batch progress without restarting ComfyUI.
📦 Nodes & Categories
1. AngelosKar/Batch
-
📊 AK Image List Scheduler (
AK_ImageListScheduler)- Schedules slice-by-slice image runs across queue executions.
- Inputs:
folder_path,list_size,file_pattern,sorting(natural or alphabetical),load_remainder(on/off),remainder_position(start/end). - Has an interactive 📂 Browse Folder button and 🔄 Reset Counter button.
- Outputs:
folder_path,sorting,file_pattern,start_index,list_size,total_images,run_counter,total_runs,folder_analysis. - Designed to be connected directly to 📦 AK Image List Loader.
-
📦 AK Image List Loader (Scheduler) (
AK_ImageListLoader)- Worker node that receives inputs from the Scheduler and loads the scheduled slice of images.
- Outputs images as an IMAGE list (
OUTPUT_IS_LIST = True), allowing downstream nodes to execute one-by-one with full resolution preservation.
-
🔄 AK Auto Image List Loader (
AK_AutoImageListLoader)- All-in-one standalone node combining both the Scheduler and Loader into a single node.
- Ideal for simple, compact batch workflows.
-
🔄 AK Reset Counters (
AK_ResetCounters)- Utility canvas button. Place it anywhere on the canvas and click 🔄 Reset All Counters to reset all scheduler counters in the active workflow back to 0.
2. AngelosKar/Control
-
🔀 AK Tag Bypasser (Active / Bypass) (
AK_TagBypasser)- Switches all canvas nodes matching a specified tag (e.g.
[RMBG],[UPSCALE],[FACE]) between ACTIVE and BYPASS (passthrough).
- Switches all canvas nodes matching a specified tag (e.g.
-
🔇 AK Tag Muter (Active / Mute) (
AK_TagMuter)- Switches all canvas nodes matching a specified tag (e.g.
[PREVIEW],[SAVE],[EXTRA]) between ACTIVE and MUTE (disabled / skipped).
- Switches all canvas nodes matching a specified tag (e.g.
Canvas Priority Rule: If multiple controllers target the same node, the strict hierarchy is MUTE > BYPASS > ACTIVE.
3. AngelosKar/VTON
- 👔 AK Garment Mode Controller (1-5) (
AK_GarmentModeController)- Designed for multi-garment Virtual Try-On (VTON) workflows.
- Select active garments from 1 Garment up to 5 Garments.
- Automatically mutes nodes tagged with
[G2],[G3],[G4], or[G5]when that garment number is disabled. Nodes without garment tags are left untouched.
4. AngelosKar/Utils
- 📂 AK Folder Path Picker (
AK_FolderPicker)- Interactive node with a 📂 Browse Folder button that opens the native Windows Explorer folder selection dialog and outputs the selected folder path string.
🚀 Installation
Option 1: ComfyUI-Manager (Recommended)
- In ComfyUI, open ComfyUI-Manager.
- Search for
ComfyUI-AngelosKar. - Click Install and restart ComfyUI.
Option 2: Comfy-CLI
comfy node install comfyui-angeloskar
Option 3: Manual Git Clone
- Open a terminal in your
ComfyUI/custom_nodes/directory. - Clone the repository:
git clone https://github.com/AngelosKar-code/ComfyUI-AngelosKar.git - Restart ComfyUI.
🛠 Requirements
- Python >= 3.10
- ComfyUI (frontend v1.x or v2.x)
- Dependencies:
torchnumpyPillowaiohttp
📄 License
This project is licensed under the MIT License.