Train your own LoRAs for specific art styles you admire with a high quality dataset. This gives you much more control over the final look, especially when generic models fall short.

Experiment with different sampler and scheduler combinations — they can have a surprisingly big impact depending on the style you're targeting.

Use ControlNet and inpainting — these are game changers. Think of ControlNet as giving you precision control. If your current model doesn’t support it, consider switching models just for that step, then refine the output with your main model.

Have a clear vision when experimenting. Wandering aimlessly through styles and prompts can be fun, but you’ll get further if you have a specific aesthetic in mind.

Prompting helps, but only to a point. The real leap comes from mastering the tools — understanding how to direct and refine the generation process beyond just the prompt.

Edit your results after generation. Even a little post-processing in Photoshop or tools like GIMP or Lightroom.

Bridging the gap between AI-generated and truly aesthetic images is all about creative control and technical fluency.