AI, Fix My Messy Whiteboard!

December 2, 2025 · View on GitHub

A local AI utility for cleaning up whiteboard photographs using image-to-image processing models via Replicate.

Before and after comparison

Overview

Whiteboard Processor transforms messy whiteboard photos into clean, professional diagrams. It uses various image generation models through Replicate's API to redraw whiteboard content with:

Clean white backgrounds (no glare or reflections)
Legible, clear text
Professional lines, arrows, and shapes
Preserved layout and spatial relationships

Features

CLI-based workflow - Drop images into a queue folder and process them interactively
Multiple model support - Switch between different image models (FLUX, Qwen, Google Nano-Banana)
Evaluation framework - Compare model outputs side-by-side to find the best results
Batch processing - Process multiple whiteboard images in one run

Installation

cd app
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Create a .env file in the project root with your Replicate API key:

REPLICATE_API_KEY=your_key_here

Usage

# Run the processor
./run.sh

# Or directly with Python
cd app && python process_whiteboard.py

# Specify a model
python process_whiteboard.py -m nano-banana-pro

# List available models
python process_whiteboard.py --list-models

Place whiteboard images in images/originals/queue/<folder-name>/ and run the processor. Enhanced outputs are saved to images/enhanced/.

Models

The tool currently includes configurations for:

nano-banana - Google Gemini 2.5 Flash (default, fast)
nano-banana-pro - Google Gemini 3 Pro (highest quality)
flux-kontext-pro - FLUX Kontext Pro
flux-1.1-pro - FLUX 1.1 Pro
qwen-image-edit - Qwen Image Edit
qwen-image-edit-plus - Qwen Image Edit Plus

Note: Image generation models evolve rapidly. The current model selection will likely become outdated. Swap in newer/better models as they become available on Replicate by editing the MODELS dict in app/process_whiteboard.py.

Evaluation

An evaluation script is included to compare model outputs:

cd eval
python run_eval.py

Place test images in eval/source/ and the script will run them through all configured models, saving results to timestamped folders in eval/runs/.