Ai-Art

ControlNet lets you steer Stable Diffusion with spatial inputs: hand-drawn sketches, Canny edge maps, depth images, or OpenPose skeletons. The output then follows your layout, not your prompt alone. You feed a control image next to your text prompt. The model builds artwork that matches the structure of your input. It then fills in texture, lighting, and detail from the prompt. You get pixel-level control that no prompt tweak can match.

The best way to fine-tune Stable Diffusion XL 2.0 is with Low-Rank Adaptation (LoRA) : a small adapter that injects your style or subject without touching the base weights. Instead of retraining the full model, LoRA trains a tiny side network next to the frozen base. The result is a 50 to 300 MB file you can load, swap, and stack at inference, trained on a 12 GB GPU in an afternoon.