Image Generation Fundamentals
Various approaches to AI image creation have emerged, each with distinct characteristics and applications. Understanding these different methodologies helps artists choose the right tool for their specific creative needs.
- Diffusion Models: Currently dominating the field, these models gradually transform random noise into coherent images by iteratively removing noise, creating high-quality and diverse visuals.
- GANs (Generative Adversarial Networks): Two neural networks competing against each other—one creating images and another judging them—resulting in increasingly realistic outputs.
- VAEs (Variational Autoencoders): Neural networks that compress images into a structured latent space and then reconstruct them, enabling controlled generation and editing.
- Autoregressive Models: Systems that generate images pixel by pixel or patch by patch in a sequential manner.
- Flow-based Models: Creating reversible transformations between simple distributions and complex image distributions.
- Text-to-Image: Converting textual descriptions into corresponding visual representations.
- Image-to-Image: Transforming existing images according to textual instructions or reference images.
- Inpainting/Outpainting: Selectively regenerating portions of an image or extending it beyond its original boundaries.
- Style Transfer: Applying the aesthetic characteristics of one image to the content of another.