AI Workflows Generate Localized Thumbnails From One Master Design

YouTube Studio can associate a different thumbnail with each language version of a video, alongside translated titles, descriptions, subtitles, and audio. The intended result is local presentation: viewers in Spain see Spanish thumbnail text, while viewers in France see French.

The constraint is that translated headlines do not reliably fit the English design. A longer phrase may overflow its original space; another language may require a different font size or layout treatment. ElevenLabs’ examples include localized thumbnails with markedly different text lengths and scripts, as well as a Portuguese translation whose headline extends beyond the original composition.

The practical task is therefore more than translating words inside an image. The localized headline needs to fit the existing thumbnail while leaving its other visual elements intact. ElevenCreative Flows is presented as a way to generate those language variants from one master thumbnail, then check each generated headline before publication.

The workflow begins with the original thumbnail as a reference image and an “Edit image” node using GPT Image 2. The demonstrated setup specifies a 16:9 output, 4K resolution, and high quality—settings selected to retain the thumbnail format while producing a higher-resolution image.

The central decision is encoded in the image-editing prompt. It tells the model to translate the headline into a specified language and replace the original headline, while retaining the surrounding design: font style, weight, colour, size, position, alignment, outline, and effects. It also explicitly bars changes to faces, backgrounds, logos, and other non-text elements.

“Keep everything else identical: same font style, weight, colour, size, position, alignment, outline and effects.”

The prompt asks for phrasing a creator would naturally use in the target language rather than a literal word-for-word rendering. It further asks the model to keep the result roughly as short as the original; if the translation runs longer, it should scale the font down slightly to fit the same box instead of overflowing.

That makes typography and context part of the translation requirement. A headline can be understandable yet still fail as thumbnail copy if it is too long, sounds unnatural, or changes the visual hierarchy. In the French demonstration, the headline changes while the logos remain untranslated, as instructed.

Each target language in the demonstrated flow branches from the original English thumbnail, rather than from an already translated output. The workflow duplicates the image-editing node while keeping it connected to the master reference, then changes the target language in the prompt. The demonstrated branches produce French and Spanish versions of the same asset.

This arrangement gives each language version the same starting composition and avoids using one generated translation as the input for another language edit. The repeated operation is straightforward—duplicate the branch, replace the language, and run it—but it keeps each output tied directly to the source thumbnail.

The flow can be extended to as many languages as needed. ElevenLabs presents this as a way to turn repeated localization into a repeatable constrained-editing process rather than a series of separate design passes.

A polished-looking thumbnail may still contain headline text the creator cannot read. Each generated image can therefore feed into a separate LLM verification node through the “Use in LLM” action.

The verification prompt asks whether the main title text is correctly translated. If it is, the model should reply “yes” and state what the text means on a new line; if not, it should explain why. It also instructs the model not to correct brand names.

The demonstration uses Gemini 3.5 Flash with thinking enabled. For the Spanish thumbnail, the model returns “yes” and says the title means “Swap objects” or “Exchange objects,” matching the English headline. A duplicated verification node connected to the French image also returns “yes” and identifies its meaning as “swapping objects.” The presenter adds that they can personally confirm the French result because they speak French.

The check is deliberately focused on the generated main title and its meaning: a creator receives both a pass-or-fail response and a readable account of what the localized headline says.

For a fixed set of languages, changing the language written into each image prompt is enough. A reusable workflow separates that variable from the translation instruction itself.

A text-input node holds the target language. An LLM node receives the standard prompt with a [LANGUAGE] placeholder and is instructed to return that exact prompt with the placeholder replaced by the text input, and nothing else. With “French” in the text field, it returns the established editing instruction with French inserted; the image node then uses that generated prompt.

The language changes, but the constraints on the image edit stay the same. Users can supply a thumbnail and target languages without rewriting the prompt or changing the flow graph.

In ElevenLabs’ example, a user enters Italian, Portuguese, and German and generates three thumbnail variations. Three language slots are a configuration choice rather than a stated limit. For seven languages, the source recommends duplicating the node flow seven times, running all branches, and downloading the generated images.