QuantSR+ introduces RBD, QSA, and SFD techniques to achieve state-of-the-art accuracy-efficiency trade-offs in 2-4 bit quantized image super-resolution networks, with reported PSNR gains like 0.29 dB on Urban100 for SwinIR-S.
Exploiting diffusion prior for real-world image super-resolution.International Journal of Computer Vision, 132(12):5929–5949
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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.
citing papers explorer
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QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks
QuantSR+ introduces RBD, QSA, and SFD techniques to achieve state-of-the-art accuracy-efficiency trade-offs in 2-4 bit quantized image super-resolution networks, with reported PSNR gains like 0.29 dB on Urban100 for SwinIR-S.
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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.