Fine-tuning is supervised model adaptation on curated examples so behavior aligns more closely with domain-specific tasks.

Related AI terms: DreamBooth and Subject-Driven Generation.

Multimodal AI combines understanding and generation across different modalities, enabling richer interfaces and cross-media reasoning.

Multi-image Conditioning uses several images as control inputs for one generation task, improving consistency across outputs. It extends single Reference Image workflows in Text-to-Image Generation.

A Diffusion Model creates images through iterative denoising steps conditioned on prompts and controls. It is the backbone of many Text-to-Image Generation systems and can be steered by Classifier-Free Guidance (CFG).

Prompt-to-Prompt Editing changes specific image attributes by adjusting textual instructions while preserving overall scene structure. It is closely related to Prompt Enhancement and iterative Text-to-Image Generation.

InstructPix2Pix applies natural-language editing commands to existing images while retaining layout context. It extends ideas from Prompt-to-Prompt Editing within practical Text-to-Image Generation pipelines.