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.
Llm-pruner: On the structural pruning of large language models.Advances in Neural Information Processing Systems, 36:21702–21720, 2023
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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.