Palette Transfer

This is the flagship of the ComfyUI-Color_Transfer pack, and the node that makes the README's one-liner true: it "replaces the dominant colors in an image with a target color palette." You give it a photo and a handful of RGB swatches, it re-paints the photo so its colors come from your swatches. Same sofa, five wallpapers - that's the whole use case, and it's the one node that can do it without touching a diffusion model.

How it actually works

Under the hood it's a tidy three-step recipe, all on CPU:

1. Cluster. It runs KMeans on your image's pixels to find the dominant colors - exactly as many as there are in your palette.
2. Match. Each dominant color is snapped to the nearest color in your palette, using a distance metric you choose.
3. Rebuild. Every pixel is replaced with its cluster's assigned palette color. Optional blur smooths out the posterized edges.

The result is intentionally flat - it's a posterized, palette-faithful version of your image, not a subtle tint. That's the honest trade-off of the whole cluster-and-map approach, and the reason you'll usually blend it back into the original if you want a "graded" look rather than a "recolored" look.

The inputs that matter

• image - anything with an IMAGE type: a LoadImage, a VAE-decoded latent, an upscaler output.
• target_colors - a COLOR_LIST, almost always from this pack's ColorPalette node (a plain text list of (R,G,B) tuples).
• color_space (RGB / HSV / LAB) - where clustering and matching happen. LAB is the color-science honest choice and usually looks the most natural; RGB is the plain default; HSV plays better if you care about hue over brightness.
• cluster_method (Kmeans / Mini batch Kmeans) - full KMeans is more accurate, MiniBatch is faster and lighter on memory. At palette sizes you'll realistically use (3–8 colors), the accuracy gap is small.
• distance_method - six options (Euclidean, Manhattan, Cosine Similarity, HSV Distance, RGB Weighted Distance, RGB Weighted Similarity). Euclidean is the sensible default; RGB Weighted Distance weights by human luminance perception (0.299/0.587/0.114), which is the one to try if plain Euclidean keeps picking the wrong swatch.
• gaussian_blur (0–27, default 3) - the one "gotcha" knob: despite the name it applies a median blur (cv2.medianBlur), which is actually the better choice for killing the salt-and-pepper noise posterization creates. Set it to 0 for a clean cut-out look.

The single output is an IMAGE, wired straight into PreviewImage, SaveImage, or on into more post-processing.

Where it fits

Think end-of-workflow, after decode. AI images come out oversaturated and over-contrasty by default (there's a running complaint about that in the photorealism crowd), and this node is a legitimate non-AI way to pull them back into a deliberate palette - brand colors, film-look LUTs you've hand-picked as swatches, a consistent tone across a whole batch so every frame of a set matches. The r/comfyui folks who mention this pack frame it exactly that way: a deterministic color-grading tool for normalizing color across many images, not a style-transfer toy.

Install and gotchas

From ComfyUI Manager search "Color_Transfer", or:

cd ComfyUI/custom_nodes
git clone https://github.com/45uee/ComfyUI-Color_Transfer

Restart, done. No models, no keys. The pack's real dependencies are scikit-learn, opencv-python, and POT; the README specifically calls out running pip install scikit-learn if you hit an import error. scipy and scikit-image come along with ComfyUI itself. Two practical notes: an empty target_colors list passes your image through unchanged (handy as a bypass), and this is a small solo-dev pack, so don't expect a changelog culture - if a knob misbehaves, the source is one file and it's readable.