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@@ -29,15 +29,15 @@ encoder or decoder, enabling efficient inference at any resolution.
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  ## Key Features
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- - **Fast**: ~3 ms/img encode, ~6 ms/img decode (1 step) on H200 — significantly
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  faster than Flux.2 VAE
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  - **High fidelity**: 38.6 dB mean PSNR (2k images), exceeding Flux.2 VAE (37.0 dB)
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  - **Semantically structured latents**: DINOv2-aligned, producing latents with
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  clear semantic segmentation visible in PCA projections
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  - **Comparable downstream convergence**: empirically matches the downstream
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- diffusion training convergence speed of Flux.2 and PS-VAE v2
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  - **Pure convolutional**: no attention in encoder/decoder, O(n) in spatial resolution
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- - **VP diffusion decoder**: single-step DDIM for PSNR-optimal, multi-step
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  with PDG for perceptual sharpening
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  ## Architecture
 
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  ## Key Features
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+ - **Fast**: ~3 ms/img encode, ~6 ms/img decode (1 step) on Blackwell RTX Pro 6000 — significantly
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  faster than Flux.2 VAE
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  - **High fidelity**: 38.6 dB mean PSNR (2k images), exceeding Flux.2 VAE (37.0 dB)
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  - **Semantically structured latents**: DINOv2-aligned, producing latents with
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  clear semantic segmentation visible in PCA projections
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  - **Comparable downstream convergence**: empirically matches the downstream
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+ diffusion training convergence speed of Flux.2 and PS-VAE
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  - **Pure convolutional**: no attention in encoder/decoder, O(n) in spatial resolution
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+ - **VP diffusion decoder**: single-step DDIM for PSNR-optimal, optional multi-step
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  with PDG for perceptual sharpening
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  ## Architecture