Most bad AI portraits don’t come from the model. They come from vague prompts. After generating a lot of images using newer tools like Image GPT-2 and Google Nano Banana Pro, one thing became very clear, the difference between an average image and a really good one is usually just how you describe it. There’s actually some data around this too. In a lot of prompt testing communities, people have seen that just making prompts more specific can improve results by almost 40–50%, without changing any settings. 1. Start with real detail Instead of writing something like “beautiful woman smiling,” try describing what you actually want to see. “slight smile, eyes looking at the camera” That one small change already gives the model direction. Then layer in realism: - natural skin texture - visible pores - small imperfections This is what removes that overly smooth, plastic look. 2. Control the lighting (this matters more than you think) Lighting alone can completely change the result. Pick one clear style: - soft diffused lighting → clean, natural - window light from the side → adds depth - dramatic side lighting → more cinematic Mixing multiple lighting styles usually confuses the model, and that’s when things start looking off. 3. Push it toward photography AI tends to lean a bit “illustration-like” unless you guide it. Adding small cues helps a lot: - photorealistic - shallow depth of field - film grain - DSLR / mirrorless camera People have noticed that adding camera-related terms can make outputs feel way more consistent and real. 4. Use negative prompts This is something most people skip. But telling the model what you don’t want helps clean up a lot of issues: - deformed eyes or pupils - cartoon / anime / CGI look - duplicate faces - weird distortions Even simple negative prompts can reduce visible errors quite a bit. 5. Be specific with textures This is where realism actually comes from. Instead of generic words: - Skin → pores, fine texture, slight variation

everything is generated from AI, even the music

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