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Niko's Project Corner

Language Python and tag Autoencoder at other sites


Introduction to Stable Diffusion's parameters

(10th November 2022)

Sta­ble Dif­fu­sion is an im­age gen­er­ation net­work, which was re­leased to the pub­lic in 2022. It is based on a dif­fu­sion pro­cess, in which the model gets a noisy im­age as an in­put and it tries to gen­er­ate a noise-free im­age as an out­put. This pro­cess can be guided by de­scrib­ing the tar­get im­age in plain En­glish (aka txt2img), and op­tion­ally even giv­ing it a tar­get im­age (aka. img2img). This ar­ti­cle doesn't de­scribe how the model works and how to run it your­self, in­stead this is more of a tu­to­rial on how var­ious pa­ram­eters af­fect the re­sult­ing im­age. Non-tech­ni­cal peo­ple can use these im­age gen­er­at­ing AIs via web­pages such as Ar­tis­tic.wtf (my and my friend's pro­ject), Craiyon.com, Mid­jour­ney.com and others.

Languages: Python PyTorch
Tags: Computer Vision Autoencoder Stable Diffusion

Image and video clustering with an autoencoder

(15th January 2022)

This ar­ti­cle de­scribes a neu­ral net­work which au­to­mat­ically pro­jects a large col­lec­tion of video frames (or im­ages) into 2D co­or­di­nates, based on their con­tent and sim­ilar­ity. It can be used to find con­tent such as ex­plo­sions from Arnold's movies, or car sce­nes from Bonds. It was orig­inally de­vel­oped to or­ga­nize over 6 hours of Go­Pro footage from Åre bike trip from the sum­mer of 2020, and cre­ate a high-res poster which shows the beau­ti­ful and vary­ing land­scape (Fig­ure 9).

Languages: Python Keras
Tags: Computer Vision Autoencoder

Chess video search engine

(13th June 2021)

Youtube has a quite good search func­tion­al­ity based on video ti­tles, de­scrip­tions and maybe even sub­ti­tles but it doesn't go into ac­tual video con­tents and provide ac­cu­rate times­tamps for users' searches. An youtu­ber "Agad­ma­tor" has a very pop­ular chan­nel (1.1 mil­lion sub­scribers, 454 mil­lion video views at the time of writ­ing) which show­cases ma­jor chess games from past and re­cent tour­na­ments and on­line games. Here a search en­gine is in­tro­duced which an­alyzes the videos, rec­og­nizes chess pieces and builds a database of all of the po­si­tions on the board ready to be searched. It keeps track of the ex­act times­tamps of the videos in which the queried po­si­tion oc­curs so it is able to provide di­rect links to rel­evant videos.

Languages: Python Keras Clojure
Tags: Computer Vision Data Structures Autoencoder