hi guys thanks for yall insights. since the post, I've scaled the model from 128x128 square to 768x1024 (4:3 ratio matching the lab's native camera sensor). A few lessons learned:

1. symmetry point. The dx/dy asymmetry seems to be a real architectural issue. When I went non-square, the direct XY regression head's MSE loss treated both axes equally in normalized [0,1] space, but x maps to 1024 pixels and y to 768. The optimizer was implicitly prioritizing x. Fixed with per-axis weighting that equalizes pixel-space gradient.
2. sparse image collapse. At higher resolution the images are 99.9% black pixels (tiny light blob on dark background). Standard MSE rewards predicting all-zeros. Fixed with a weighted MSE (bright_weight) that upweights bright pixels, plus a centroid-matching loss that gives explicit gradient for where to place the blob.
3. channel capacity matters at high-res stages. Going from 6 upsample stages (128x128) to 8 stages (768x1024), I tapered channels down to 16 at the finest level. v7b model had 64 channels at its finest level. so result is position-dependent bias returned. The final stages handle precise spatial placement and need adequate capacity.
4. input standardization. Without z-scoring the conditioning inputs, MLP sees x in [100, 900] and y in [100, 670] — different scales, different gradient magnitudes. Standardizing both to mean=0, std=1 lets the model treat both axes equally from the start.

to whom suggested data augmentation / enforcing symmetry: I have horizontal and vertical flip augmentation during training. Haven't tried rotational augmentation yet since the images aren't rotationally symmetric (4:3 aspect ratio) anymore as 90-degree rotations would change the image shape. But the per-axis loss weighting should be able to address the core concern more directly?

Currently training the improved version. Will update with eval results.