Model Hub

SwinIR Classical x4 - Swin Transformer image-restoration model for 4x super-resolution, reconstructing sharp high-resolution detail from low-resolution images. Works at any input size. Apache 2.0.

Swin2SR Classical ×2 — Swin Transformer V2 image super-resolution model that upscales images 2× while restoring fine detail; the classical-SR variant for clean low-resolution inputs. Apache 2.0.

Real-ESRGAN ×4 — xinntao practical image and photo super-resolution model (GAN-based) that upscales 4× while removing real-world noise, blur and compression artifacts. BSD-3 license.

HAT-L x4 - XPixel Group Hybrid Attention Transformer (Large) super-resolution model that activates more input pixels through combined channel and window attention for state-of-the-art 4x image upscaling. Works at any input size. Apache 2.0.

Swin2SR Classical ×4 — Swin Transformer V2 image super-resolution model that upscales images 4× while restoring fine detail; the classical-SR variant for clean low-resolution inputs. Apache 2.0.

Swin2SR RealWorld x4 - Swin Transformer V2 super-resolution trained on BSRGAN-style degradations, for real-world photographs carrying noise, blur and compression artifacts rather than clean sources. PSNR-oriented variant, so it favours fidelity over invented detail. Upscales 4x. Apache 2.0.

SwinIR Classical x2 - Swin Transformer image-restoration model for 2x super-resolution, reconstructing sharp high-resolution detail from low-resolution images. Works at any input size. Apache 2.0.

HAT-S x4 - XPixel Group Hybrid Attention Transformer (Small) super-resolution model that activates more input pixels through combined channel and window attention for 4x image upscaling. Works at any input size. Apache 2.0.