A Multi-reference Field is a CMS relationship field that stores references to multiple records from another collection.

A Multi Collection Reference Field creates a one-to-many relationship by allowing a CMS item to reference multiple records from another collection.

Retrieval-Augmented Generation (RAG) enriches model outputs by fetching external knowledge at runtime and conditioning generation on it.

Style Reference lets you guide the aesthetic of generated assets by pointing the model to example visuals. It is frequently combined with Reference Image inputs in Text-to-Image Generation workflows.

A Reference Image is a conditioning input that guides composition, structure, or aesthetics during generation. It is central to Style Reference workflows and Multi-image Conditioning.

Generative Expand increases image boundaries and predicts plausible continuation beyond original edges. It is commonly used alongside Generative Fill in broader Text-to-Image Generation workflows.

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).

Classifier-Free Guidance (CFG) is a sampling technique that adjusts prompt adherence versus diversity in generated outputs. It is a common control in Diffusion Model pipelines and complements Prompt Enhancement.

ControlNet augments diffusion generation with explicit structural conditions such as edges, depth, or pose. It improves control in Diffusion Model systems and often uses cues from Image Segmentation.

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.

Subject-Driven Generation aims to keep a specific person, product, or character consistent across new generated scenes. It is often implemented with DreamBooth and guided by a Reference Image.