Do We Still Need LoRAs When Base Models Are This Powerful?

A few months ago, the answer was obvious. If you wanted consistency, control, or a recognizable style, you trained a LoRA. Base models were good, but not that good. Today, that question feels much less clear. Models like Nano Banana Pro and Seedream 4.5 can generate cinematic images, hold lighting surprisingly well, and respond to prompts with an accuracy that used to require fine-tuning. For many creators, they already feel “good enough.” So it’s fair to ask: If base models are this powerful now, do we still need LoRAs at all? The honest answer is: yes—but not for everyone, and not for the same reasons as before. Why Base Models Feel Like They Replaced LoRAs Modern base models are no longer generic. They’ve been trained on massive, diverse datasets and tuned to respond well to: cinematic framing realistic lighting common art styles popular character archetypes For a large group of creators, this is already enough. If your goal is: exploration one-off images social content concept art general storytelling visuals then base models like Nano Banana Pro or Seedream 4.5 can absolutely carry the workload. In these cases, training a LoRA often feels unnecessary. Why spend time preparing datasets and tuning when the base model already produces strong results with a good prompt? That’s not a misunderstanding—that’s progress . What Base Models Are Actually Optimized For Base models are designed to be: flexible broad responsive to prompts good at “average” cases They are fantastic generalists. But that generality comes with tradeoffs. Base models tend to: drift slightly over multiple generations reinterpret prompts creatively (sometimes too creatively) struggle with very specific, repeated identities prioritize visual appeal over strict consistency Most of the time, this isn’t a problem. Until it is. Where LoRAs Still Matter (A Lot) LoRAs were never really about making images look better . They were about making images behave predictably . That hasn’t changed. LoRAs still matter when you need: the same character across dozens of images a specific art style that shouldn’t drift a brand look that must stay consistent a niche aesthetic not well represented in base models reusable assets for long-term projects If you’ve ever thought: “This looks great… but it doesn’t look like my thing anymore” that’s where LoRAs come in. The Creator Divide: Who Actually Needs LoRAs Today? Not all AI creators are the same anymore. Creators who usually don’t need LoRAs casual creators social media experimenters prompt explorers short-form content makers people who value variety over consistency For these creators, base models are not just sufficient—they’re ideal. Training a LoRA would slow them down without adding much value. Creators who still benefit massively from LoRAs character-driven storytellers game and visual novel creators brand and marketing teams creators building a recognizable visual identity anyone producing multi-image or multi-episode content For these creators, LoRAs are not obsolete—they’re foundational . The Role of LoRAs Has Shifted What has changed is how LoRAs are used . In the past, people trained LoRAs to “fix” weak base models. Today, LoRAs are used to: lock identity stabilize style reduce prompt complexity make workflows repeatable Instead of fighting the base model, LoRAs now constrain it. This is an important shift. Base models handle quality. LoRAs handle intent. The New Hybrid Workflow (Where Things Are Heading) Increasingly, advanced creators use both . A common modern workflow looks like this: use a powerful base model (Nano Banana Pro, Seedream 4.5) for quality and realism layer a LoRA to enforce identity or style rely on prompts for scene and mood rely on LoRAs for consistency This combination gives you: strong visuals predictable behavior less prompt micromanagement In other words: freedom without chaos . Why LoRAs Won’t Disappear (Even as Models Improve) As base models get better, the need for specialization actually increases. The more capable a general model becomes, the more creators want to: differentiate own a look control output over time LoRAs are not a workaround anymore—they’re a creative choice . They let you say: “This is how my work behaves.” That’s not something a general base model can fully replace. So… Do We Still Need LoRAs? Yes—but we need them for different reasons now . If you want speed and variety → base models are enough If you want identity and continuity → LoRAs still matter If you want both → combine them The real mistake is thinking this is an either-or decision. It isn’t. Final Thoughts Powerful base models didn’t kill LoRAs. They freed LoRAs from being a necessity and turned them into a choice. And that’s a good thing. AI creation is no longer about forcing tools to work. It’s about choosing the right level of control for the kind of creator you are. Some creators want exploration. Some want ownership. Some want both. The tools are finally mature enough to support all three.