Face/Detail Enhancement
Specialized tools can improve specific elements of AI-generated images, particularly faces and important details that might lack clarity in the initial generation.
Face restoration models like GFPGAN and CodeFormer can dramatically improve the quality of facial features in portraits, correcting proportions and adding realistic details. These tools use specialized neural networks trained specifically on facial reconstruction, allowing them to infer high-resolution details even from relatively low-quality inputs. Adjustment controls let artists balance accuracy against fidelity to the original image.
Beyond faces, detail enhancement techniques like contrast-adaptive sharpening, guided filtering, and AI-based denoising can selectively improve specific image elements without introducing artifacts. These approaches are particularly valuable for architectural details, text clarity, and complex textures that might appear slightly blurred in raw generations.