Text-to-Image · AI Image Generation
Text-to-Image
What Is Text-to-Image?
- Recovers images from noise using a diffusion model
- Break the description into parts for consistent results
- Text, hands, and character consistency are still weak points
What text-to-image changes in practice is 'how you source images'. It used to mean digging through paid stock sites for a similar shot, or paying an agency per image and waiting three or four days. Now a training manager can generate ten drafts at their own desk and pick from them. What changed is not drawing skill but the cost and speed of producing drafts. The new bottleneck is the ability to describe in words exactly what image you need.
There are three main ways to use it. The first generates from a sentence alone on a blank canvas. The second, image-to-image, takes an existing photo and changes its composition or style. The third, inpainting, erases part of an image and fills it back in, and it is used surprisingly often for internal materials. To keep the same person across several images, the practical method is to fix the seed value and change only one phrase in the prompt. However, the output is a raster image of around 1024 pixels, so it is a poor fit for logos and infographics that are enlarged significantly or edited piece by piece. Use generation for drafts and an editing tool for the final polish.
Rights issues split into two tracks: copyright in the generated output, and the scope of the usage license. The first is an area where countries differ on how much human creative contribution to recognize. The second is a matter of contract, so reading the terms gives you the answer. Some free plans prohibit commercial use or automatically post results to a public gallery, and an unreleased product concept image has ended up exposed that way. Checking the terms is a legal issue and a security issue at the same time. A single paid account shared by the team, with generation history kept, is easier to manage.
The most common misconception is that 'a picture made by AI has no owner, so I can use it however I like.' If a watermark remnant carried over from training data, a style indistinguishable from a particular artist’s, or a face resembling a real actor ends up on the cover of a promotional piece, the problem spreads beyond copyright to portrait rights and reputation. Leaving Korean text inside the image to the generator is another common trouble spot: garbled characters are hard to fix by editing, so you end up regenerating every image. It is safer to add the text in an editing tool from the start. People also often overlook that the moment you upload a photo of an executive or customer as a reference image, you have handed personal data to an external server.
John KimInstructor John Kim, author of "B2B Marketing Design" and CEO of Nanum Consulting
Seungtae ParkAX instructor who teaches by building AI business development and marketing automation himselfResearch on generative models built up through the late 2010s, and around 2022 a wave of diffusion-based services was released to the public and went mainstream. Copyright over image training data is still under discussion in many countries.
Take a hypothetical case: the training team at Company A, an auto parts manufacturer with 480 employees. Each month they outsourced 12 internal announcement graphics and training notices at KRW 150,000 (≈ US$110) apiece. Every revision added two more days, so notices repeatedly went out only three days before a session.
- When Kim on the training team pulled the last three months of outsourcing records, 31 of the 43 requests turned out to be 'a single background image.' "Let's just generate these ourselves," Kim said, and a two-month pilot began.
- In the first week they entered only vague sentences like 'employees working hard' and didn’t get a single usable shot. Only after they created a template that splits the prompt into four lines for subject, background, lighting, and aspect ratio did the acceptance rate rise noticeably.
- In a six-part safety training series, the person looked different in every installment. Fixing the seed value and changing only the background phrase made all six images match in a single day.
- Park, a manager in Legal, asked that "faces resembling real people and other companies’ logos all be removed," so the internal guide now carries a two-line rule: use image AI freely for internal materials, but create anything distributed externally only on paid accounts.
- Four months later, outsourcing had dropped from 12 requests a month to 3, cutting production costs from KRW 1.8 million (≈ US$1,350) to KRW 450,000 (≈ US$335) a month. With no more waiting on drafts, training announcements went out two weeks before events instead of three days.