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.

DreamBooth is a personalization method that fine-tunes a model to generate consistent renderings of a chosen subject. It is a specialized form of Fine-tuning used in Subject-Driven Generation.

Textual Inversion introduces new concept tokens by learning embeddings that map to visual ideas. It is lightweight compared to full training and connects closely with Embeddings and DreamBooth workflows.