> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/generative-ai/mix-n-match.md). # Mix N Match Single-modality models get more useful when they are chained, so that one model's output becomes another's input. This page shows one system that combines detection, segmentation, and generation across image, text, and speech, and then one that applies generative models to formal theorem proving. The same notes are in [DETECTION](/deep-learning/deep-neural-machine-vision.md#detection), [Segmentation](/deep-learning/deep-neural-machine-vision.md#segmentation), and [Speech](/generative-ai/speech.md). [Grounded Segment Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything) is IDEA-Research's Grounded SAM: it marries Grounding DINO with Segment Anything, Stable Diffusion, and Recognize Anything to automatically detect, segment, and generate anything. The repository extends that chain to more inputs: > Grounding DINO with Segment Anything & Stable Diffusion & BLIP & Whisper - Automatically Detect , Segment and Generate Anything with Image, Text, and Speech Inputs The same mix-and-match idea reaches beyond media. [LeadDojo theorem proving](https://leandojo.org/) is AI-driven formal theorem proving in the Lean ecosystem. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://www.mlcompendium.com/generative-ai/mix-n-match.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.