I was just sharing the info, mainly because some people are interested in backing up the site—and at the very least, this serves as a backup of the assets. That’s my main goal.
As for the purpose of the dataset: it’s not just about pixel art. It includes various graphic styles, music, and more. And honestly, there’s not much anyone can do about it—licenses allow it. Still, if someone finds it useful as a backup, then it’s there for them.
For anyone feeling concerned: There's an important distinction between having a dataset available for training and an AI actually being capable of understanding and using that data well enough to replace artists. Right now, AI is still far from reaching that level—especially when it comes to specialized artistic disciplines.
I doubt that a model trained solely on OGA would be very competitive, considering the vast and diverse datasets used to train most modern models. That said, this is purely speculation—I haven’t tested such a model or seen any artwork generated from it. However, OGA assets could potentially be included in larger datasets depending on their license. Based on my research, assets under CC0 and CC-BY licenses are eligible for inclusion, as they permit reuse—CC0 allows unrestricted use, while CC-BY requires attribution, both of which align with common dataset inclusion standards.
Just to clarify: It’s not the website itself — the assets have been packed and organized by license, and those are what you can download.
For those unfamiliar: the site’s main purpose is not to serve as a backup of OGA, but to train AI using its assets. Nevertheless, you can download the dataset if you’d like to keep your own backup.
Update: I’ve already downloaded the CC0, GPL 2.0, and GPL 3.0 assets. Everything seems to be included, and the attribution list appears accurate. However, the files are organized only by art type, making it difficult to search for assets using other filters, such as artist name. To locate specific items, you'd need to check the attribution file and manually create folders based on artist, filter preferences, or create folders and order them alphabetically.
At the bottom of the page, the tracks are organized by genre and mood, with clear visual illustrations. This makes it one of the easiest ways to find a track for a game or any other project you’re working on.
I was just sharing the info, mainly because some people are interested in backing up the site—and at the very least, this serves as a backup of the assets. That’s my main goal.
As for the purpose of the dataset: it’s not just about pixel art. It includes various graphic styles, music, and more. And honestly, there’s not much anyone can do about it—licenses allow it. Still, if someone finds it useful as a backup, then it’s there for them.
For anyone feeling concerned: There's an important distinction between having a dataset available for training and an AI actually being capable of understanding and using that data well enough to replace artists. Right now, AI is still far from reaching that level—especially when it comes to specialized artistic disciplines.
I doubt that a model trained solely on OGA would be very competitive, considering the vast and diverse datasets used to train most modern models. That said, this is purely speculation—I haven’t tested such a model or seen any artwork generated from it. However, OGA assets could potentially be included in larger datasets depending on their license. Based on my research, assets under CC0 and CC-BY licenses are eligible for inclusion, as they permit reuse—CC0 allows unrestricted use, while CC-BY requires attribution, both of which align with common dataset inclusion standards.
Apparently, someone has already done the work, and based on the dates, it was updated about 4 months ago. You can download everything from here:
https://huggingface.co/collections/nyuuzyou/opengameart-680b5aa2e8ab3ed893af03f0
Just to clarify: It’s not the website itself — the assets have been packed and organized by license, and those are what you can download.
For those unfamiliar: the site’s main purpose is not to serve as a backup of OGA, but to train AI using its assets. Nevertheless, you can download the dataset if you’d like to keep your own backup.
Update: I’ve already downloaded the CC0, GPL 2.0, and GPL 3.0 assets. Everything seems to be included, and the attribution list appears accurate. However, the files are organized only by art type, making it difficult to search for assets using other filters, such as artist name. To locate specific items, you'd need to check the attribution file and manually create folders based on artist, filter preferences, or create folders and order them alphabetically.
I love it and I mixed it: https://opengameart.org/content/battle-in-the-winter-battle-theme-a-pixelsphere-vs-jobromedia
for sure! here you are: https://codeberg.org/glitchapp/fish-fillets-remake, assets are distributed separately, you are credited here.
https://codeberg.org/glitchapp/fish-fillets-remake-assets/src/branch/main/creditstxt
https://codeberg.org/glitchapp/fish-fillets-remake-assets/src/branch/main/Credits.md
https://codeberg.org/glitchapp/fish-fillets-remake-assets/src/branch/main/MusicCredits.txt
Your tracks are here: https://codeberg.org/glitchapp/fish-fillets-remake-assets/src/commit/161bd53ea66020e9d2fe8247cc543925fd842276/music/EricMatyas
They are and you are of course credited on those games that have tracks created by you, thanks!
I like very much the atmospheric music, thanks for sharing!
Thanks! Just to clarify, I didn't write the music—credit goes to the talented musicians listed above.
To make this post more useful, you can find the final mix distribution on the Funkwhale server here: https://audio.pepemoss.com/channels/mixes
At the bottom of the page, the tracks are organized by genre and mood, with clear visual illustrations. This makes it one of the easiest ways to find a track for a game or any other project you’re working on.
Updated preview with most Tracks up to 2026
The game looks very cool and the language and development IDE used for it very interesting. Thanks for letting me know.
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