Post-Processing & Enhancement
AI generation is often just the first step in creating finished artwork. Post-processing techniques help refine, enhance, and personalize raw AI outputs to achieve professional-quality results and unique artistic expression.
AI upscaling technologies increase image resolution while intelligently enhancing details, allowing for large prints or close examination without quality loss.
- Real-ESRGAN: General-purpose upscaler with excellent detail preservation.
- ESRGAN: Earlier version still valuable for certain image types.
- SwinIR: Transformer-based upscaler with superior handling of complex textures.
- LDSR (Latent Diffusion Super Resolution): Uses diffusion models for natural detail enhancement.
- ScuNET: Specialized in preserving sharp edges and fine structures.
- 4x-UltraSharp: Optimized for maximum sharpness and detail recovery.
- Waifu2x: Originally designed for anime but effective for various stylized imagery.
Different upscalers have distinct characteristics that make them suitable for particular image types. Photorealistic content often benefits from different upscalers than illustrated or painted styles. Many artists use multiple upscaling passes with different algorithms for different image elements, combining the strengths of various approaches.
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.
AI-generated art often reaches its full potential when combined with traditional digital art tools, creating powerful hybrid workflows that leverage the strengths of both approaches.
For many professional artists, the most effective approach combines the speed and ideation strengths of AI generation with the precise control and personal expression of manual editing. This hybrid workflow uses AI to quickly generate concepts or base elements, then refines and personalizes them through traditional digital art techniques.
Photoshop integration has become particularly robust with plugins like Automatic1111's Photoshop plugin, Neural Filters, and third-party extensions that enable generating and editing content directly within familiar creative environments. These tools allow for selective regeneration of image areas, style transfer, and seamless compositing of AI elements with traditional digital art.
Similar integration exists for other creative platforms, with GIMP extensions bringing AI capabilities to open-source editing, Krita plugins supporting digital painters, Blender add-ons for 3D integration, and After Effects tools for animation and motion graphics. These bridges between traditional and AI-powered workflows allow artists to maintain their established techniques while embracing new creative possibilities.