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Image-to-Image vs. Text-to-Image: What Each One Is For

Image-to-image and text-to-image aren't a question of which one is "more advanced" — they're two different input methods that solve two different problems. This piece defines the boundary first, then gives you a concrete rule for which one to use.

The problem with this framing is that a lot of people treat image-to-image and text-to-image as two tiers, as if image-to-image were somehow the "higher" one. It's not a tier difference — it's a difference in input method, and the problems each one solves are different too. Let's lay out the definitions first, then talk about how to choose.

Definitions

Text-to-image: the only input is a text description. Structure, composition, and color are all decided by the model based on the prompt. Its advantage is maximum freedom — no prior constraints of any kind.

Image-to-image: besides the text description, you also supply a reference image, and the model modifies or redraws based on that reference. There's a key parameter concept here — usually called "strength" or "denoising level." The lower the value, the closer the result stays to the original structure of the reference image; the higher the value, the more room the model has to change things, gradually approaching the freedom of pure text-to-image. Understanding this sliding relationship is more useful than just memorizing the two terms.

When to use text-to-image

Starting from scratch, without a clear compositional direction yet, in the exploratory stage where you want to try out multiple possibilities — that's when you use text-to-image. Its value is in "generating possibilities," not "precise control."

For wallpaper content, for instance, in the early style-exploration phase, text-to-image is more efficient — run the same prompt a few times, compare a handful of compositions and tones, land on a direction you like, and then decide whether it's worth refining further.

When to use image-to-image

The advantage here is that whenever you have a specific reference subject that needs to be preserved, image-to-image is essentially the only option. A few typical scenarios:

Keep the composition, change the style: an existing photo you want turned into an illustration style or a different color grade, where the subject's position and composition can't shift
Keep the person, change the scene or outfit: very common when generating avatars and portrait shots — the facial features need to be locked by a reference image, while the scene can be left to the model
Product photo retouching: the most typical case is e-commerce product shots — the product's own shape, logo, and material detail must stay exactly as-is, with only the background, lighting, or scene elements changed. Text-to-image basically cannot guarantee product consistency for this kind of task; it has to go through image-to-image
Local repair: the hand-distortion issue mentioned earlier is solved with image-to-image plus local inpainting, and falls into this same category — detailed methods are in Fixing AI Hand Distortion

A point that often causes confusion

A lot of people get stuck on "does this need count as needing image-to-image." The judgment call is actually simple: do you have a reference image where some part of the content absolutely must be preserved? If yes, that's image-to-image's job. If no — generating from a text description with nothing to preserve — that's text-to-image's job. No need to agonize over which one is "better" — they're not two answers to the same question in the first place.

Practical advice

In practice, the two are frequently used together, not as an either/or choice. Use text-to-image first to explore direction and settle on a rough composition and style; once you've picked a version you like, treat it as the reference image and use image-to-image to refine details and make local changes. This workflow is more efficient than going straight for image-to-image from the start, because image-to-image's results depend heavily on the quality of the reference image itself — pick the wrong reference, and no amount of refinement afterward will save it.

The conclusion, then: text-to-image handles "from nothing to something," image-to-image handles "from something to something refined." Once you're clear on which stage each one is meant to solve, tool choice stops being something to agonize over. The prompt tool entry point on the homepage lets you switch directly between both modes to try them out.