Find Similar Designs is an AI retrieval feature that helps teams discover visually related assets in large files or libraries. It supports consistency by linking discovery to your Design System and optional Style Reference inputs.

Text-to-Image Generation creates visuals directly from prompt instructions. Most modern systems rely on a Diffusion Model and can be steered using a Reference Image.

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.

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.

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.

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.

Segment Anything Model (SAM) produces masks from points, boxes, or text-like prompts for rapid object selection. It underpins modern Image Segmentation workflows and improves control in Reference Image editing.

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.

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.