I’ve been messing around with image generation models recently and thought this could be wrapped in a library or people could just use the techniques suggested in the blog post I wrote:

We can have commercial grade modern image generation without too much difficulty by running the underlying Python libraries. This is certainly lower hanging fruit than trying to port everything over to Nx and we can use Nx as the bridge to share memory via Pythonx.

I think after having a day to stew on it I’m souring on the idea of creating the library because it’s unclear anyone actually wants this. The people it will benefit can just as easily use a commercial API. Also, these models will go off the boil soon enough and then we’re stuck maintaining an obsolete library.

So for the time being I’m more than happy for people to read the above, glean the lessons I’ve gleaned, embark on some experiments if the ideas in the blog catch your fancy and copy the techniques I’ve outlined.

The source code for the demo: GitHub - GenericJam/imagine_demo · GitHub

The source code for the skeleton library: GitHub - GenericJam/imagine · GitHub

These are generated using the above source code.

“a monkey eating a bag of peanuts”

flux_hybrid_42_17665573679461024×1024 67 KB

“a disco ball and a human skull had a baby and this is the result”

flux_hybrid_42_17665583509161024×1024 64.6 KB