SANA-SR uses 32x deep compression autoencoding and linear-attention DiT to deliver competitive real-world image super-resolution at 0.019s inference after pruning.
Scalable diffusion models with transformers
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Efficient One-Step Diffusion Restoration Model with Compact Token Compression and Linear Attention
SANA-SR uses 32x deep compression autoencoding and linear-attention DiT to deliver competitive real-world image super-resolution at 0.019s inference after pruning.