Plot of SDXL VAE latents shows diffusion models can still hide unstable values
kalomaze · x · 2026-07-23
A latent-distribution plot says SD3-style diffusion can still have unstable values
- The post argues that even when an autoencoder is trained for diffusion, the latent values are not necessarily well-behaved or finite.
- It points to SD3 as an example where heuristic weighting seems to compensate for bad conditioning in vanilla flow matching.
- The attached plot compares SDXL VAE latent spread per channel across 1,536 images, showing real vs. augmented distributions and clipping bands.
- The broader point is that proxy metrics can hide numerical instability, much like a model could be submitted without layer norm if the benchmarks look fine.
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