Merging Two Images with AI: A Practical Prompt-Driven Workflow

kevin bai — Sun, 20 Sep 2026 01:06:55 +0000

Combining two images into one coherent picture used to mean opening Photoshop, stacking layers, and fiddling with masks for an hour. Today, a well-structured prompt and the right model can do the heavy lifting in seconds. Here's the workflow I've settled on after a lot of trial and error.

Why merge images at all?

A few use cases come up constantly:

Before/after comparisons for portfolios and case studies
Product mockups — dropping a logo or label onto a scene photo
Social content — diptychs and blended visuals that stop the scroll
Style transfer experiments — keeping the composition of one photo but borrowing the palette of another

The prompt-driven approach

Multimodal models (GPT-image-style models, Flux Kontext, and img2img pipelines) accept two inputs: your images plus a text instruction. The quality of the merge depends less on the model than on how precisely you describe three things:

What stays fixed. "Keep the left half of image 1 unchanged."
What gets replaced. "Swap the background of image 1 with the scene from image 2."
How the seam should look. "Match lighting and color temperature so the transition is invisible."

A template that works well:

Merge these two images. Keep [subject] from image 1 in the same position.
Replace [element] with [element] from image 2.
Blend the lighting, shadows and color grading so the result looks like a single photograph.
Do not add any new objects or text.

The last line matters more than you'd expect — without it, models love inserting random props.

When a model isn't the right tool

For a pure mechanical merge — overlaying a watermark, stitching two photos side by side, or blending exposures — you don't need a generative model at all. Traditional pixel-level merging is faster and never hallucinates.

For those cases I use AI Image Combiner: upload two images, pick a blend mode or layout, and download the result. No prompt engineering, no surprise artifacts, and it handles transparency properly, which generative models still struggle with. It's become my default for quick merges, and I only reach for a multimodal model when I actually need the output to be reimagined rather than assembled.

A practical decision rule

Need the pixels preserved exactly (watermark, side-by-side, exposure blend)? → Traditional merge tool.
Need the two images to become a new, unified scene? → Multimodal model with a precise prompt.

Mixing the two — merging mechanically first, then feeding the result to a model for atmosphere — covers almost everything I need. Give the prompt template above a try on your next composite; the "no new objects" constraint alone will save you a dozen retries.

What workflows are you using for image merging? I'd love to hear what's working (and what models keep breaking) in the comments.

How AI Manga Translation Keeps the Art Intact

kevin bai — Fri, 21 Aug 2026 12:46:10 +0000

For years, reading manga in languages other than Japanese meant waiting for fan scanlations or official localizations. Both approaches take time, and the gap between a chapter's release and its translation can stretch for weeks. AI is starting to change that rhythm.

The real challenge isn't just converting text. Manga pages are visual: speech bubbles, sound effects, and handwritten notes are woven into the artwork. A good translation has to preserve the layout, the tone, and the art itself. Early machine translation often broke that experience by overlaying clunky text boxes or losing contextual nuance.

Modern AI translation pipelines handle this differently. They detect text regions, translate with context-aware models, and render the result back into the original bubble shapes. The reader gets a translated page that still feels like manga.

One tool built around this workflow is AI Manga Translator. It takes manga, manhwa, and webtoon images, extracts the text, translates it, and redraws the result to match the original panel design. Batch processing and layout retention make it practical for longer series, not just one-off pages.

Of course, AI translation still benefits from human review, especially for creative dialogue and cultural references. But for readers who want near-instant access to untranslated series, or for scanlation teams looking to speed up rough drafts, these tools are becoming a genuine option.