Bring plausible color to a monochrome record while keeping the original frame in view.
Built for portraits, family groups, and street archives. Color is inferred from context, not recovered as historical fact.
Open the workbenchBring a black-and-white photo into the darkroom
Drop a portrait, group photo, or street scene here to prepare it for colorization.
The color archive
Every Before file below is a real grayscale image on disk. No browser filter is used to manufacture a monochrome example.
Source
Decoded grayscale
Operation
Whole-image color
Control
Before / after
Skin tone study
A portrait makes facial warmth, hair boundaries, and clothing transitions easy to inspect. The comparison keeps the original crop visible so identity changes cannot hide behind color.
Group separation study
Group photographs test whether nearby faces, garments, and the background receive distinct color decisions instead of one broad tint across the frame.
Scene color study
Architecture, sky, road, foliage, and small figures create a scene-level test. Plausible context matters, but the source geometry remains the record to trust.
What the model reads
A useful result must make sense on a face, between neighboring people, and across the wider scene. The original remains the reference at every scale.
Local tonal cues guide complexion, lips, hair, and nearby fabric without a user-controlled strength setting.
Adjacent faces and garments test whether inferred colors stay attached to the correct subject.
Sky, vegetation, masonry, and roads depend on broader context rather than one global tint.
A controlled process
There is no decorative strength slider. The workbench exposes the actual operation: submit one image, compare the whole result, and export it.
Upload a black-and-white or heavily faded PNG, JPG, or WebP image.
The model analyzes people, objects, and scene context with no invented intensity control.
Inspect the original and result across the frame, then download a colorized PNG.
Archive note
Preserve the original monochrome file alongside every colorized version. For research, publication, or restoration records, label the result as AI-generated and verify known colors against primary sources.
The monochrome source remains the authoritative visual record.
Make the use of AI color inference visible wherever the result appears.
Use documented references for uniforms, signs, products, and landmarks.
Review faces, fine edges, and background regions before publishing.
Colorization questions
Practical limits matter most when the source is archival, personal, or historically significant.
Not necessarily. The model infers plausible colors from learned visual patterns. Use documented references when exact uniform, product, building, or clothing colors matter.
The operation returns a full colorized image and is intended to preserve composition and identity. Always compare the result with the source before archival or editorial use.
No. The selected model does not expose a user-facing strength or saturation parameter, so this page does not present a control that the provider cannot honor.
The upload accepts supported image files without a black-and-white gate. Results on already colored or partially faded material may be reinterpreted, so compare the full frame carefully.
