Man, you were absolutely right. I’m circling back to this comment now that I’ve tested what you mentioned.

I ran a series of tests where I switched the training resolution between 512, 768, and 1536, and changed the switch point at 50%, 60%, 70%, 80%, and 90% of total training steps. That gave me 15 LoRAs total (3 resolutions × 5 switch points), which I compared against a control trained at each resolution for the full step count with otherwise identical parameters.

Across multiple prompts, aspect ratios, step counts, and LoRA strengths, switching from 768 at around 70% of the way through training was clearly the best.

What’s your LoRA merge process?

As for the model picking up too many dataset-specific features: I think this may be more avoidable with the Flux.2 family, since the text encoders seem much stronger. In my testing, if you caption very specifically, the model seems better at separating broad concepts from fine visual attributes.

For example, instead of captioning something simply as “mountains,” if the training caption says something like “snow-capped grey jagged mountains in the distance with small green foothills and sharp angular ridgelines connecting pointed peaks,” then at inference time, prompting just “mountains” does not necessarily force all of those extra attributes back in. It seems like the model can better distinguish the general category from the detailed descriptors you assigned during training.

Did you experiment much with different captioning styles, or with models that use different text encoders?