Emberify

Apply Ember color palettes to any image.

Palette & Docs · embertheme.com

What it does

Emberify recolors images using the Ember palette — 19 carefully chosen colors spanning warm graphite, coral, gold, olive, sage, steel, rose, and mauve.

emberify photo.png -c ember

Output: photo-ember-colorized.png

Install

The recommended way to install emberify is using pipx

pipx install emberify

# With GPU acceleration (Apple Silicon / NVIDIA):
pipx install 'emberify[gpu]'

Usage

# Auto-generated output: filename-{palette}-colorized.ext
emberify photo.jpg -c ember-light

# Custom output path
emberify photo.jpg -c ember -o output.png

# Adjust recolor strength (0.0 = original, 1.0 = full)
emberify photo.jpg -c ember-light -s 0.7

# Aggressive mode with more clusters for better accuracy
emberify photo.jpg -c ember -m aggressive -k 20

# GPU acceleration (requires aggressive mode)
emberify photo.jpg -c ember -m aggressive --use-gpu

Palettes

Modes

Aggressive mode (-m aggressive)

Uses K-means clustering to analyze the image before recoloring.

How it works:

1. K-means finds the 12 dominant colors in your image (configurable with -k)
2. Each dominant color is mapped to its nearest Ember palette color (RGB Euclidean distance)
3. Every pixel is reassigned to its cluster's mapped palette color
4. Strength controls the blend between original and recolored

Result: Colors group naturally — sky areas stay coherent, skin tones stay unified. Best for photos and complex images.

Fast mode (-m fast)

Direct pixel-by-pixel RGB nearest-color mapping — no clustering, no filters. Each pixel independently maps to the closest Ember palette color using Euclidean distance in RGB space.

How it works:

1. For each pixel, compute Euclidean distance to all palette colors in RGB space
2. Replace the pixel with the nearest palette color

Result: Maximum detail preservation — edges, gradients, fine lines stay sharp. Best for anime, illustrations, line art, or when speed matters.

GPU acceleration (--use-gpu)

When --use-gpu is passed, K-means clustering runs on the GPU with smart downsampling — clusters on a 200K-pixel subset, then assigns all pixels vectorized.

Supported backends (auto-detected via pyopencl):

Performance (5304×7952 image, 12 clusters):

Key differences

Showcase

As you can see, when processing a high-resolution image, GPU mode significantly accelerates the process.

This feature has been tested on Apple Silicon GPUs; it should also work on NVIDIA. If you encounter any issues with your GPU, feel free to open an issue.

License

GPL-3.0 — JesusChapman