AI Image Generation for Social Media: What Works and What Looks Cheap

AI image generation for social media follows a different logic than product photography, and the gap is bigger than most people expect. A marketplace listing needs to be honest: the buyer gets a physical object and will compare it against the photo. A feed needs to make someone stop scrolling, and accuracy barely matters there. That gap changes which models you reach for, how you write prompts, and where things go wrong.

I'm an art director, and this is the practical side of it, mistakes included.

A five-point check before your first image

Run through these five and half the usual headaches disappear.

Aspect ratio first. Square 1:1 works for a standard feed post, 4:5 claims more screen space, 9:16 is for Stories and Reels. Generate directly in the ratio you need instead of cropping afterward. Cropping after the fact wrecks the composition, and your subject ends up pushed off to one side.

Resolution matters too, because every platform compresses whatever you upload. Start with more pixels than you think you need. Scaling down is painless; fixing a blurry, low-resolution image after the fact is not.

Text inside the frame is its own problem. If the image needs any text baked in, the model choice narrows fast, since many models still struggle to render legible text. We tested which ones actually hold up, and the results are here.

A feed reads as a whole, not post by post. Five images in five visual styles register instantly as mismatched, and it reads like a clearance bin rather than a brand.

Leave room for copy. Platforms frequently drop a headline or a button over your image. Build in blank space on purpose, or the text ends up sitting across someone's face.

Where the AI gives itself away

Three tells that mark a generated image at a glance.

Lighting that's too clean. Models default to even, flattering studio light without a single awkward shadow. In a feed that reads as stock photography, and people scroll past stock without a second look.

Skin with no texture. Whenever a person's in frame, skin tends to smooth out until it looks like a mannequin. The fix is blunt: ask directly for pores, texture, a bit of natural asymmetry.

Hands and small details going wrong. This is the classic failure, and we broke it down separately. It stands out more in a feed than on a product card, simply because people look harder at people.

I argued with my team about the first point for a while, convinced that flawless lighting was a win. Turned out to be the opposite: a slightly rougher, more lived-in shot pulls more engagement.

Which model for which job

Quick breakdown by task.

Text baked directly into the image: Ideogram 3.0, GPT Image 2.

People in frame, natural skin and expression: Seedream 5.0, Imagen 4.

A product with honest, real-world texture: Nano Banana, Flux 2.

Fast, rough drafts in volume: Z-Image.

None of this is gospel. For your specific feed style the order might flip, and checking takes one evening: pick your typical scene and run it through three models back to back.

Keeping a feed's style consistent

The most common problem once you're posting regularly, and it comes down to three techniques.

A reference frame. Shoot or generate one strong image, then feed it back in as a reference for the next ones. The model picks up the color and mood from it.

A locked prompt block. Keep the part describing light, palette, and overall mood fixed, and only swap out the subject. It sounds tedious. It works.

One seed across a series. If your tool exposes seed control, a series comes out noticeably more even. What a seed actually does gets explained here.

Running AI image generation for social media at volume

Once you're posting ten times a week, one-off generation turns into a part-time job. Here's the order that saves the most time.

Build the content calendar first, then generate everything in one pass. Not "write the post, then make the image," but a batch: ten scenes, one session, one style.

Be ruthless about selection. Generating thirty images to land ten usable ones is normal, not a sign your prompts are weak.

Keep finished images in clearly named folders, because a month later you won't find that one good variant otherwise. File naming sounds like a boring topic, and it is, but it saves real time at scale.

I queue ten scenes at once in Flami, step away, and come back to sort through results. The evening goes to picking, not waiting.

FAQ

What aspect ratio should I use for social media images? Generate directly in the ratio you need: 1:1 for a square feed, 4:5 for taller posts, 9:16 for Stories. Cropping a finished image after export breaks the composition, since the model built it around a different frame.

Which AI model is best for social media images? It depends on the job. Text-heavy images work better with Ideogram or GPT Image, people-focused shots with Seedream or Imagen, product shots with Nano Banana or Flux. There's no single answer; run one scene through a few models and compare.

How do I make a feed look consistent? Keep the part of your prompt describing light and palette fixed, reuse one reference image across the series, and lock the seed if your tool allows it. That's what turns separate posts into something that reads as a series.

Can people tell an image is AI-generated? Often, yes: overly even lighting, plastic-looking skin, and problems with hands or small details are the giveaways. The first two get fixed by prompting for imperfection and texture; the third gets fixed by just rejecting bad frames.

How many images should I generate per post? Budget three to five attempts per usable frame, sometimes more. Planning on one generation per post almost never pans out, so budget for more from the start.

Do I need to edit the image after generating it? Usually, but lightly: cropping to the right format and a touch of color correction so the feed doesn't clash. That's minutes of work, not hours.

Sources

1. Flami: testing text rendering in AI models
2. Flami: why AI models mangle hands and text
3. Flami: seeds and generation repeatability