Flux 2 Klein 9b V1:

Flux 2 Klein's awesome VAE means it picks up fine details incredibly well. While it still needs more training, I have some other stuff to train in the meantime so I thought it was worth it to push this out now as it can do some things incredibly well. Expect some body horror, especially if you use it with the distilled version of the model. I found that perhaps using more steps than 4 was helpful with the distilled version, but I also didn't try it much. Using this with the base model has far less anatomy issues. I expect them both to improve further with more training.

SNOFS was trained on natural language, not tags. It will work best if you use full sentences to describe what you want.

Training details (skip to the version 1.3 details below if you just want to know what this model can at least somewhat do right now):

I trained this as a factor 4 lokr using AI Toolkit this time. I used AI-Toolkit because when I started the training the other options had issues with their lycoris output and ComfyUI. While it's a great piece of software for new users, training parameters aren't really explained anywhere in detail as they're simplified. I found myself going through the code way too much to figure out things like the timestep parameters.

I think my starting learning rate was way too high at 1e-4 with an effective batch size of 4-6 or so. I quickly decreased it but it was perhaps still too high starting at 5e-5. I'm running a different training run at 1e-5 right now and it's still learning quite quickly. I might try to further train this at a very low LR and see what happens instead of starting fresh. Note: this is probably largely because of my large lokr size. I wanted to ensure I had "room" for all of the concepts but it can make things spicy.

I think the main issue people are coming into with training both this and Z-Image are what timesteps you train on. This was mostly trained on a high shift value of 3-5 as in inference Flux 2 Klein stays above the 800 timestep mark for most of the generation and maybe does 1 step out of 50 at below 200. I found I needed to test as I went and see where the generations went wrong and try to adjust on the fly.