GenRex
- Transform your gens into embeddings
- Search your gens by text, image, or embedding
- Filter and sort by similarity, aesthetics
Backlog
- Save and download searched or favorite gens
- Create your style key (personal style embedding)
- Generate images by clicking on gen in gallery
- Fix gens automatically by inpainting (eyes)
- Upscale gens using interpolated scalers
- Save your search gens’ captions, scores to csv
Based on clip-retrieval
Easily compute clip embeddings and build a clip retrieval system with them. 100M text+image embeddings can be processed in 20h using a 3080.
- clip client allows remote querying of backend via python. clip-client notebook
- clip inference allows you to quickly (1500 sample/s on a 3080) compute image and text embeddings
- clip index builds efficient indices out of the embeddings
- clip filter allows you to filter out the data using the clip index
- clip back hosts the indices with a simple flask service
- clip front is a simple ui querying the back. Check it out at clip-retrieval ui
- clip end2end runs img2dataset, inference, index then back and front to make all of this easier to begin with
End to end this make it possible to build a simple semantic search system. Interested to learn about semantic search in general ? You can read my medium post on the topic.
Also see laion5B and semantic search at billions scale to read more on how to make this scale to billion of samples.
