A comprehensive collection of custom nodes for ComfyUI featuring 22 image manipulation nodes, multiple LLM integrations (Claude, OpenAI, Gemini, Ollama), string utilities, and cloud upload capabilities. Designed to integrate seamlessly with your Isekai Core deployment and enhance your ComfyUI workflows.

Access the documentation here

Features

Image Manipulation (22 Nodes)

Transform and enhance your images with professional-grade effects and transformations using only PIL/Pillow - no additional dependencies required.

Blend Operations: Blend images, adjust colors, apply color ramps, modify levels, split toning
Effects: Blur, sharpen, grain, vignette, chromatic aberration, glare, edge enhancement, color filters, invert, posterize, pixelate
Transformations: Rotate, scale, crop, flip, translate, combined transforms

LLM Integrations (5 Nodes)

Generate captions, titles, descriptions, and local visual quality reports using your preferred AI model.

Ollama: Local LLM integration
Claude: Anthropic's Claude API
OpenAI: GPT models via OpenAI API
Gemini: Google's Gemini API
Visual QA: Local Qwen3-VL image review with a deterministic publication gate

Dataset & String Utilities (4 Nodes)

Powerful text manipulation for dynamic prompts and batch processing.

Dynamic string selection with variables
String concatenation
Tag selection and management
Round-robin item cycling for batch workflows

Cloud Upload (2 Nodes)

Upload your generated images to cloud storage.

Isekai Upload: Direct integration with Isekai platform
S3 Upload: AWS S3 and Cloudflare R2 support

I/O Utilities (3 Nodes)

File operations for workflows.

Compress and save images with quality control
Load text from files
Random line selection from text files

Node Categories

All nodes are organized alphabetically in the ComfyUI menu under the Isekai category:

Dataset (4 nodes)

Concatenate String: Join multiple strings together
Dynamic String: Select strings with variable support
Round Robin: Cycle through items in batch workflows
Tag Selector: Manage and select tags

Image/Blend (5 nodes)

Blend Images: Combine two images with various blend modes (Normal, Multiply, Screen, Add, Subtract, Difference, Lighten, Darken)
Color Adjust: Adjust brightness, contrast, saturation, and sharpness
Color Ramp: Apply gradient mapping for color grading
Levels: Adjust black and white points
Split Toning: Apply separate colors to highlights and shadows

Image/Effects (11 nodes)

Blur: Gaussian and Box blur effects
Chromatic Aberration: RGB channel offset for lens effects
Color Filter: Apply Sepia, Grayscale, or Black & White filters
Edge Enhance: Enhance or detect edges
Glare: Add bloom/glare effects to bright areas
Grain: Add film grain or noise
Invert: Invert image colors
Pixelate: Create mosaic/pixel art effects
Posterize: Reduce color levels for poster effects
Sharpen: Sharpen images with Unsharp Mask
Vignette: Darken edges with customizable radius and softness

Image/Transform (6 nodes)

Crop: Crop images to specified dimensions
Flip: Flip images horizontally, vertically, or both
Rotate: Rotate images by any angle
Scale: Resize images with various resampling methods
Transform: Combined rotate, scale, and translate operations
Translate: Move/shift images

IO (3 nodes)

Compress and Save: Save images with quality control
Load Text: Load text content from files
Random Line From File: Select random lines from text files

LLMs (5 nodes)

Claude: Generate text using Anthropic's Claude API
Gemini: Generate text using Google's Gemini API
Ollama: Generate text using local Ollama models
OpenAI: Generate text using OpenAI's GPT models
Isekai Visual QA: Review anatomy and generation artifacts locally with Qwen3-VL

Ollama response modes

The Ollama node defaults to General, which preserves Ollama's normal text generation behavior. Select Short response for titles and similar one-line outputs. That opt-in mode requests a one-field JSON response, disables model thinking, uses deterministic generation with a 64-token cap and 16,384-token context, and keeps the model loaded in Ollama for 10 minutes.

Isekai Visual QA

Isekai Visual QA sends each input image to a local Ollama multimodal model and passes the original IMAGE through unchanged. Install the default model before using the node:

ollama pull qwen3-vl:8b

The node returns approved, score, and a machine-readable report_json. It keeps Qwen's raw estimate as diagnostic model_score, but computes the gate score locally: start at 100, subtract 25 per major issue and 5 per minor issue, and force zero for any blocking issue. For a batch, the lowest local score must meet approval_threshold, and every image must contain zero blocking issues. Inference and malformed-response errors fail closed with approved = false and an inference_error blocking issue.

The node sends the full frame and four quadrant detail crops together in one Ollama request, so small hands and faces remain inspectable in large outputs. It requests a 16,384-token model context for these five views. Crop boundaries are not treated as defects. Ollama keeps Qwen loaded for 10 minutes after each response to avoid repeated model reloads. This works best with a dedicated or remote Ollama GPU; when Ollama shares ComfyUI's GPU, the model can retain VRAM until the keepalive expires or you run ollama stop qwen3-vl:8b. The default rubric checks malformed hands, fingers, faces and body anatomy; duplication and impossible merges; unintended text or watermarks; broken edges, seams, tiling, halos, banding, noise, and upscale artifacts; and composition and finish. It is a technical-quality gate only: subject matter, mature content, artistic taste, and intentional stylization are outside its scope. You can replace the rubric per workflow. Enable unload_comfy_models to release ComfyUI model memory before Ollama inference; unload failures are reported as runtime warnings and do not hide the QA result.

Upload (2 nodes)

Upload to Isekai: Upload images to Isekai platform. Choose Manual review (the safe default) to place the job in the review queue, or Direct to draft to send it directly to draft creation. Use QA decision follows a connected qa_approved Boolean and goes directly to draft only when it is literally true; missing, false, or invalid values fail closed to manual review. Existing workflows that do not contain this setting continue to use manual review. The node sends the result as the multipart reviewPolicy field (manual_review or direct_to_draft).
Upload to S3: Upload to AWS S3 or Cloudflare R2

Installation

Via ComfyUI Manager (Recommended)

Open ComfyUI Manager
Search for "isekai"
Click Install
Restart ComfyUI

Manual Installation

Clone or download this repository
Place it in ComfyUI/custom_nodes/isekai-comfy-node

Install dependencies:

cd ComfyUI/custom_nodes/isekai-comfy-node
pip install -r requirements.txt

Restart ComfyUI

Support

Issues: Report bugs on GitHub Issues

Development

Project Structure

isekai-comfy-node/
├── __init__.py              # Package entry point
├── config.py                # Configuration management
├── pyproject.toml           # Package metadata for ComfyUI registry
├── README.md                # This file
├── requirements.txt         # Dependencies
│
├── nodes/                   # Node implementations
│   ├── __init__.py
│   ├── base.py              # Base classes and exceptions
│   │
│   ├── image/               # Image manipulation nodes (22 nodes)
│   │   ├── blend/           # Blend operations (5 nodes)
│   │   ├── effects/         # Visual effects (11 nodes)
│   │   └── transform/       # Geometric transforms (6 nodes)
│   │
│   ├── claude_node.py       # Claude API integration
│   ├── gemini_node.py       # Gemini API integration
│   ├── openai_node.py       # OpenAI API integration
│   ├── ollama_summarizer_node.py
│   ├── visual_qa_node.py       # Local multimodal visual quality gate
│   │
│   ├── dynamic_string_node.py
│   ├── concatenate_string_node.py
│   ├── tag_selector_node.py
│   ├── round_robin_node.py
│   │
│   ├── upload_node.py       # Isekai upload
│   ├── s3_upload_node.py    # S3/R2 upload
│   │
│   ├── compress_and_save_node.py
│   ├── load_text_node.py
│   └── random_from_file_node.py
│
└── utils/                   # Shared utilities
    ├── __init__.py
    ├── validation.py        # Input validation
    ├── image_utils.py       # Image tensor/PIL conversion
    ├── cloud_llm_client.py  # Claude/OpenAI/Gemini client
    ├── ollama_client.py     # Ollama API client
    ├── ollama_vision_client.py # Structured Ollama vision client
    └── s3_client.py         # AWS S3/R2 client

Requirements

Python 3.8+
ComfyUI
Pillow >= 10.0.0
torch >= 2.0.0 (typically included with ComfyUI)
numpy >= 1.24.0 (typically included with ComfyUI)
requests >= 2.31.0

Contributing

Contributions are welcome! Please:

Fork the repository
Create a feature branch
Make your changes
Submit a pull request