# ♿ Colorblind-Safe Data Palette Swapper — Make Any Chart Accessible in One Prompt A fine-tuned LoRA model built on **Qwen-Image-Edit-2511** that transforms the color palette of any data visualization into a colorblind-safe alternative — without changing a single data point, label, axis, or geometric element. Trained on a curated dataset of 34 image pairs spanning line charts, bar charts, heatmaps, stacked charts, and multi-series plots. --- ## ✨ What This Model Does Over 300 million people worldwide live with color vision deficiency, yet the majority of data visualizations — dashboards, reports, research papers, infographics — are built with color palettes that are completely inaccessible to them. This LoRA solves that problem at the image level, with a single prompt. Given any chart image, `make_cb_safe` remaps the color scheme to a perceptually safe palette — blue-yellow, blue-orange, grayscale with texture fills, or a universally accessible multi-color palette — while preserving every data point, label, gridline, and annotation exactly as they are. Perfect for: - ♿ **Accessibility compliance** — make dashboards and reports WCAG-friendly instantly - 📊 **Research & academic publishing** — ensure your figures are readable by all reviewers - 🏫 **Education & e-learning** — produce inclusive visual materials for students with CVD - 🏢 **Enterprise reporting** — convert legacy charts to accessible formats at scale - 🧩 **Neurodiversity support** — clear, high-contrast visuals for low-vision and neurodivergent users --- ## 🚀 Quick Start ### Trigger Word Always include `make_cb_safe` in your prompt to activate the palette-swap behavior. --- ### 🔴🟢 Deuteranopia — Red-Green Deficiency (Most Common) Affects ~6% of males. Red and green appear as similar shades of yellow-brown. ``` make_cb_safe Convert the color scheme of Image 1 to the safe blue-yellow palette in Image 2. All data points and labels must remain exactly as they are. ``` --- ### 🔴 Protanopia — Red Deficiency Affects ~1% of males. Red appears dark, and red-green distinctions are lost. ``` make_cb_safe Apply a protanopia color correction to Image 1 using the reference colors in Image 2. Do not alter any text, data, or geometric structures. ``` --- ### 🔵🟠 Blue-Orange Safe — Deuteranopia + Protanopia Combined Best general-purpose swap for the two most common CVD types. ``` make_cb_safe Replace the confusing red-green hues in Image 1 with the clear blue and orange palette from Image 2, preserving the chart's structure perfectly. ``` --- ### 🔵🟡 Tritanopia — Blue-Yellow Deficiency Affects ~0.01% of population. Blue and yellow are confused with green and red. ``` make_cb_safe Replace the chart colors in Image 1 with the color palette in Image 2, keeping all text, data, and geometric structures absolutely unchanged. ``` --- ### ⬛ Grayscale + Texture — Print & Universal Accessibility Best for printed materials, monochrome displays, or maximum inclusivity. ``` make_cb_safe Replace the chart colors in Image 1 with the grayscale palette in Image 2, and add distinct texture fills to each data area, keeping all text and geometry unchanged. ``` --- ### 🌐 General — Unknown CVD Type Use when you are unsure of the specific deficiency type. ``` make_cb_safe Map the colors of the chart in Image 1 to the accessible palette provided in Image 2 without modifying any geometric or text elements. ``` --- ## 📥 Download **With ModelScope Python SDK:** ```bash pip install modelscope ``` ```python from modelscope import snapshot_download model_dir = snapshot_download('isumenuka/colorblind-charts-v1') ``` **With Git:** ```bash git clone https://www.modelscope.ai/isumenuka/colorblind-charts-v1.git ``` --- ## ⚙️ Training Details | Parameter | Value | |---|---| | Base Model | Qwen-Image-Edit-2511 | | Training Type | LoRA (Image Edit) | | Dataset Size | 34 image pairs | | Trigger Word | `make_cb_safe` | | Train Epochs | — | | Repeat | — | | Learning Rate | — | | Optimizer | — | | Network Rank Dim | — | | Network Alpha | — | | Resolution | — | | Save Precision | — | --- ## 🎨 Sample Output The model accepts a source chart and a reference palette image, then outputs the same chart with all colors remapped to the target palette. Supported transformations include: - **Red-green → Blue-yellow** (deuteranopia safe) - **Red-green → Blue-orange** (deuteranopia + protanopia safe) - **Full color → Grayscale + texture fills** (print and universal accessibility) - **General palette swap** (any source → any accessible target palette) All geometric elements — bars, lines, points, grid, axes, labels — are preserved exactly. Only the color fill values are changed. --- ## 📝 Tips for Best Results - Always start your prompt with `make_cb_safe` - Provide a clean reference palette image as Image 2 — a simple color swatch or example accessible chart works best - For heatmaps, add "without modifying any geometric or text elements" to prevent layout changes - For bar and stacked charts, add "add distinct texture fills" for pattern differentiation alongside color - Use the **Blue-Orange** prompt as your default if unsure — it covers the two most common CVD types - Works across all chart types: line, bar, stacked bar, heatmap, scatter, multi-series --- ## 🌍 Why This Matters This model was built for the **QwenImageLora Challenge on ModelScope — Track 2: AI for Good.** Color vision deficiency affects approximately 1 in 12 men and 1 in 200 women globally. Despite this, most data visualization tools default to red-green color schemes that are invisible to a significant portion of the population. This LoRA is a step toward making data communication genuinely inclusive — without requiring designers or developers to rebuild their pipelines from scratch. --- ## 👤 Author **isumenuka** — [ModelScope Profile](https://www.modelscope.ai/isumenuka) --- ## 📄 License This model is released under the **Apache License 2.0**.