AI image tools are remarkably good now, and that is exactly why the failures stand out. One wrong choice in how you edit or prompt and the result screams "generated": plastic skin, melting text, lighting that obeys no sun. The good news is that each of these mistakes has a practical fix you can apply in your next edit.
1. Over-smoothing everything into plastic
The mistake: Pushing denoising, "enhance," or beautify until every pore, fabric thread, and surface texture disappears. Perfectly smooth reads as fake because the real world is textured.
The fix: Keep or reintroduce grain. Ask for visible skin texture, woven fabric, or concrete grain in your prompt, and avoid running the same enhancement twice. If the image already looks waxy, regenerate with a lower enhancement strength rather than patching it afterward. A little noise is a feature.
2. Ignoring the light sources
The mistake: Compositing a subject lit from the front into a scene where every shadow falls from a window on the left. Or generating a night scene where faces are evenly bright. Human eyes may not name the problem, but they feel it instantly.
The fix: Before editing, count the light sources in the base image and note their direction, color, and hardness. Then instruct the edit to match: "same warm lamp light from the upper right, soft shadows." When you add an object, ask for its shadow and any colored bounce light. Consistent light is the cheapest realism you can buy.
3. Letting text and fine detail melt
The mistake: Signage, logos, watch dials, keyboard keys, and jewelry are where models give up. Blurry pseudo-letters and six-fingered hands are the classic tells.
The fix: Do not ask the model to render small text or intricate geometry in one pass. Generate the scene without it, then add typography with a real text tool, or regenerate just that region at higher resolution with a short, specific instruction. Zoom in at 200% before you publish and check hands, edges, and any lettering.
4. Changing one thing but breaking the whole frame
The mistake: "Change her jacket to red" also changes her face, the background, and the time of day, because the model reimagines the entire image instead of editing it.
The fix: Edit in smaller, masked steps. One instruction per pass: color first, object second, background third. Keep a reference to the original and compare side by side after each step. If a later edit undid an earlier one, you are doing too much per generation — split it.
5. Matching resolution and style to the destination
The mistake: A dreamy, painterly image posted as a crisp product photo; a low-resolution generation stretched into a banner; mismatched grain between an original photo and an AI-inserted element.
The fix: Decide the target first — thumbnail, print, blog header — and generate or upscale to fit it. Keep one visual language per image: if the photo is documentary-style, the added elements should be too. Upscale once at the end, not repeatedly, and check the result at final display size, not just full screen.
The quick pre-publish check
Run through this before any image goes out:
- Can I find real texture (skin, fabric, surfaces)?
- Do all shadows agree with the light sources?
- Are hands, edges, and lettering clean at 200% zoom?
- Did each edit change only what I asked?
- Does resolution and style match where it will be posted?
Fix those five and your images will stop looking assembled and start looking seen.
For the workflow above — masked edits, texture-preserving passes, and clean upscales — Nano Banana 21 is an AI image generation and editing tool built for exactly this kind of step-by-step editing.
Top comments (0)