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AI photo colorization

Palette.fm: The AI Tool That Colorizes Black-and-White Photos Automatically [Updated]

Palette.fm can colorize black-and-white photos in seconds, but its output is a plausible AI interpretation rather than recovered original color. Here is how the workflow, pricing, prompts, privacy, and alternatives work.

By ThatPainter Team Updated 9 min read
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Palette.fm can turn a black-and-white photograph into a plausible color version in seconds. You upload an image, review the automatic result, try different color filters, and refine the output with a description or keywords such as “blue uniform” or “red car.”

That result is an AI interpretation—not proof of the photograph’s original colors. Grayscale pixels usually do not contain enough information to determine whether an object was originally blue, green, brown, or another similarly bright color. For family-history and archival work, keep the untouched scan and label the colorized file as generated.

What is Palette.fm?

Palette.fm is a web-based AI colorization service associated with Swedish machine-learning researcher Emil Wallner. It became widely known after a 2022 report by Ars Technica described a free tool that automatically colorized black-and-white images.

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The original service did more than apply a single sepia-like effect. It generated an image caption, offered preset color filters, and allowed the user to edit the description to guide the next colorization. Palette’s current product page describes a newer generation of the service with automatic colorization, more than 21 color filters, and color adjustment through descriptions or keywords.

Palette has not publicly established in the supplied material that it uses a particular named image-generation model. It is therefore more accurate to describe it as Palette’s proprietary or custom colorization system than to attribute it to a specific backend such as Stable Diffusion.

How Palette.fm colorizes an image

Colorization is an inference problem. The system examines grayscale shapes, edges, textures, apparent objects, and visual context, then predicts RGB colors that would make the image look coherent. Text can add another constraint: if the user says that a jacket was blue, the system has information that the grayscale image itself cannot reliably provide.

Wallner described the earlier system as using one model to generate a description and another to use the image together with that text to create the colorization. The supplied 2022 reporting did not identify the model architecture.

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This distinction matters: Palette.fm does not retrieve hidden color data from a monochrome photograph. It creates a likely-looking color version based on learned visual patterns and any information supplied by the user.

How to colorize a black-and-white photo with Palette.fm

  1. Prepare the image. Use the highest-quality scan or digital file available. Crop distracting borders if appropriate, but keep an untouched master copy separately.
  2. Upload the photograph. Open Palette.fm in a browser and add the black-and-white image. The first preview is intended to show what the system thinks the scene contains.
  3. Review the automatic result. Look for obvious errors in clothing, vehicles, buildings, skin tones, vegetation, skies, and signs. A convincing overall appearance can still hide incorrect individual colors.
  4. Compare filters. Palette’s current site advertises more than 21 color filters. Different filters can produce materially different interpretations, so save or compare promising versions rather than assuming the first output is best.
  5. Guide the result with text. Use a concise description or keywords when you know something about the scene. Examples include “navy blue military uniform,” “dark green foliage,” “cream-colored building,” or “bright red automobile.”
  6. Correct mistaken assumptions. If the system misidentifies an object, revise the description and generate another version. The historical Ars Technica test demonstrated this iterative approach: editing the generated caption improved the output when the tool’s interpretation was wrong.
  7. Export the appropriate version. Use a preview for experimentation and a watermark-free high-resolution output only when the result is worth preserving or sharing.

What does the free version include?

Palette.fm’s current product page says the free plan provides unlimited color previews, but free images are resized to a maximum of 500 × 500 pixels and include a Palette watermark. The site also advertises one free trial credit for a logo-free, full-resolution colorization.

For paid output, Palette says that one credit produces one watermark-free, high-resolution colorization, with output up to 5,000 × 5,000 pixels. The public pricing page displays both annual subscriptions and one-time credit packages. At the time covered by the supplied research, the displayed examples included $72 per year for 480 credits and $49 for 75 credits.

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Prices, credit policies, available plans, and the free-trial offer can change. Check Palette.fm’s live pricing page immediately before purchasing. Do not confuse the consumer website’s credit system with Palette’s separate developer/API plans, which use their own output categories and paid tiers.

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Is Palette.fm accurate?

It can produce a convincing color image, but it cannot normally establish the photograph’s original colors. A grayscale value represents brightness, not a unique hue. Several different colors can have similar grayscale brightness, meaning the original blue shirt and a brown shirt may look almost identical after conversion to monochrome.

Commonly uncertain areas include:

  • clothing and uniforms, especially when historical colors are important;
  • cars, signs, flags, and painted machinery;
  • skin tones, hair, and makeup;
  • grass, trees, and other foliage;
  • sky, water, and shadows;
  • brick, stone, wood, and painted buildings.

A prompt can improve a result when it is based on evidence. A family member may know that a particular dress was blue, or a historical record may identify the colors of a flag. But a prompt can also introduce an unsupported assumption. “Colorized according to a description” is not the same as “original colors recovered.”

For historically significant images, treat the colorized version as an interpretation unless the colors are supported by a color reference, written record, surviving object, or reliable testimony.

How to get better results

Start with a good scan

AI cannot restore information that was lost through a severely blurred, underexposed, damaged, or tiny source image. Scan physical prints at a high resolution, remove dust only from a working copy, and retain the original scan without edits.

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If you are starting with a box of physical prints, a photo scanner for old photos can make the digitization stage easier, particularly when you need to process many images. A scanner is optional for photographs that are already available as high-quality digital files; it is not required by Palette.fm.

Describe known facts, not guesses

Use text to add information the grayscale image cannot reveal. Good guidance is specific and evidence-based:

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  • “The man is wearing a dark navy uniform with a white shirt.”
  • “The automobile is deep green, and the house has a pale yellow exterior.”
  • “Keep the background mostly muted and preserve the dark shadows.”

Do not add details simply because they seem historically typical. A prompt that says “red dress” may produce a more attractive result, but it does not make the dress historically red.

Generate alternatives

Colorization has no single objectively correct answer when the source contains no color evidence. Compare several filters and prompt variations. Keep the versions that are visually useful, but name them clearly—for example, family-photo-colorized-palette-filter-03.jpg—instead of replacing the original scan.

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Inspect faces and important details at full size

Small previews can conceal artifacts or inconsistent color boundaries. Check faces, hands, lettering, uniforms, flags, and other details that matter to the story of the photograph before printing or publishing.

Palette.fm versus Photoshop and other alternatives

Palette.fm is convenient when the primary goal is a fast, browser-based first pass. It requires less editing knowledge and can generate several interpretations through filters and text guidance.

Adobe Photoshop is better suited to a workflow that continues after automatic colorization. Adobe documents a Colorizer Neural Filter that automatically selects hues for black-and-white photos and provides controls for global colors, saturation, and specific areas. Photoshop also supports conventional layer-based, reversible editing, which is useful when you need to correct a jacket, separate a sky from a building, or manually constrain a historically documented color.

If you want a photo editor for refining AI colorization, Photoshop or a comparable layer-based editor can complement Palette.fm rather than replace it. The trade-off is complexity: a dedicated editor generally takes more time to learn and may involve a separate subscription or plan. Adobe’s current trial and pricing details should be checked directly before signing up.

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Fotor also advertises an online AI photo colorizer for black-and-white, faded, yellowed, and vintage photos. Its stated workflow includes standard and advanced modes, PNG or JPG downloads, and batch colorization of up to 50 images, alongside starter credits. Those are Fotor’s current commercial claims and may change.

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On iOS, the App Store listing for Colorize – Restore Old Photos describes one-tap colorization, scratch and damage repair, portrait enhancement, and offline 4× upscaling. Its listing also describes cloud processing and automatic deletion of cloud-processed photos within 24 hours. Those claims apply to that app’s listing and should not be generalized to Palette.fm.

Privacy and ownership before uploading family photos

Palette’s current terms say that users retain ownership of images and videos created through the service and that Palette does not claim ownership of generated content. The terms also place responsibility for submitted content on the user and state that content may be processed to provide the service.

Palette’s privacy policy says personal data may be transferred to and maintained on computers outside the user’s jurisdiction, including processing in the United States. It also describes possible disclosure to service providers, affiliates, law enforcement when legally required, and parties involved in certain business transactions. The policy cautions that no online transmission or electronic storage method is guaranteed to be completely secure.

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These statements make the current official terms and privacy policy more important than older third-party descriptions of how the service handled uploads. Before uploading sensitive family photographs, unpublished historical material, images of living people, or anything covered by a confidentiality obligation, review the current policies and decide whether a cloud service is appropriate.

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A preservation-first workflow

  1. Keep the physical original. AI colorization should not replace the print, negative, slide, or archival scan.
  2. Create a high-quality master scan. Store it in a lossless or minimally compressed format and keep it separate from edited copies.
  3. Make a working copy. Upload only the copy intended for processing. Avoid overwriting the master.
  4. Save the generated result separately. Record that Palette.fm created it, along with the date, filter, prompt, and any known color evidence.
  5. Label uncertainty. Use wording such as “AI-colorized interpretation” when sharing the image in a family archive, exhibition, or historical project.
  6. Preserve the print after scanning. An archival photo storage box made for photographic materials can help organize original prints, but it is secondary to controlling humidity, temperature, light, and handling.

If the originals are fragile or you have a large collection, a professional photo scanning service may be worth considering. Compare its scanning resolution, file formats, handling procedures, return policy, and privacy practices before sending irreplaceable material; no specific provider is recommended here.

Is Palette.fm worth using?

For a quick preview of how an old photograph might look in color, Palette.fm is an accessible option. Its strongest feature is the combination of automatic colorization, multiple filters, and text guidance: you can start with a one-click result and then refine it when the system misunderstands the scene.

It is less suitable as a source of historical color evidence. The free plan’s 500 × 500-pixel limit and watermark may be enough for testing, while serious exports require credits or a paid plan. Users who need precise local corrections may prefer Photoshop or another editor with layers and manual controls.

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The sensible approach is to use Palette.fm as a creative and exploratory step in a preservation workflow—not as a machine that reveals colors hidden inside a grayscale image.

Frequently Asked Questions

Can Palette.fm recover the exact original colors?

Usually no. It predicts plausible colors from grayscale structure, visual context, learned patterns, and optional text. Exact historical colors require independent evidence such as color references, records, surviving objects, or reliable testimony.

Is Palette.fm free?

Palette’s current site advertises unlimited free previews, but those previews are limited to 500 × 500 pixels and include a watermark. It also advertises one free trial credit for a logo-free, full-resolution result. Paid watermark-free outputs use credits, and current pricing should be checked before purchase.

What resolution does a paid Palette.fm image have?

Palette currently says one credit produces a watermark-free high-resolution colorization with output up to 5,000 × 5,000 pixels. Availability and limits can change, so confirm the live product terms.

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Can prompts improve Palette.fm results?

Yes, when the prompt supplies reliable information or corrects a mistaken object interpretation. Prompts can also create unsupported colors, so describe known facts and label the result as an interpretation.

Should I upload sensitive photographs to Palette.fm?

Review Palette’s current terms and privacy policy first. Its policy discusses processing outside the user’s jurisdiction, including the United States, disclosures to service providers and others in specified circumstances, and the limits of online security. For highly sensitive material, consider an offline workflow.

The Bottom Line

Palette.fm is useful for creating fast, visually convincing color interpretations of monochrome photographs. Use its filters and text guidance iteratively, but preserve the original scan and never present an AI-generated color as historically verified without independent evidence.

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