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license: mit
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Semantic VAE

Purpose: Extremely fast convergence on illustraton data for a downstream DiT model

Trained from DINOv2 in 12 hours as follows:

Frozen DINOv2 -> LayerNorm -> 32 channels

Frozen DINOv2 with patch embed unfrozen -> LayerNorm ->  32 channels

64 channel bottleneck with Sigreg loss (1e-3 weight) from LeJEPA

VA-VAE style convolution decoder

Loss is all layers of DINOv2 and DINOv3 equally weighted MSE on input vs reconstruction.

There are no pixel losses/LPIPS/GAN used, this is DINO space loss only.