I think the AI image generation space has quietly hit a point where no single model dominates across every use case and that’s actually a more interesting development than any individual model release. The specialization happening right now is pretty significant. Photorealism is where mystic 2.5 and google imagen 4 have gotten scary good. Skin texture, ambient lighting, the subtle imperfections that make a photo look like a photo rather than a render. Six months ago these outputs would still have obvious tells but now it’s genuinely difficult to distinguish from real photography in a lot of cases. Text rendering in images used to be a running joke but ideogram basically solved it. Legible words on posters, packaging, signage, all stuff that every other model still struggles with. It’s weirdly niche but if you’ve ever needed actual readable typography in a generated image you know how big of a deal this is. Then there’s the stylistic side where flux 2 pro stands out. Not photorealistic, not trying to be. It has this visual personality that feels like an actual art direction decision rather than the default “AI pretty” aesthetic most models default to. And gpt 1.5 introduced conversational image editing which is a completely different paradigm. Instead of regenerating from scratch you describe edits in plain english and it adjusts. Different use case entirely. I’ve been using freepik to access most of these which is convenient but the bigger observation is that we’ve moved past the “which model is best” era into something more like “which model is best for this specific task.” The architectures are optimized for fundamentally different things and people who match the right tool to the right job are getting dramatically better output than those trying to force one model to do everything. Anyone else noticing this specialization trend accelerating? Curious where people think it’s headed. submitted by /u/Legitimate-Run132
Originally posted by u/Legitimate-Run132 on r/ArtificialInteligence