Create realistic noise and compression artifacts on images. This is meant to synthesize paired dataset for SISR training.

DM600_LUDVAE

Rybu

Meant as an experiment to test latest techniques implemented on traiNNer, including: AdaTarget, KernelGAN, UNet discriminator, nESRGAN+ arch, noise patches, camera noise, isotropic/anisotropic/sinc blur, frequency separation, contextual loss, mixup, clipL1 pixel loss, AdamP optimizer, etc. The config file is provided on the download link above. I encourage everybody to mirror the model, distribute and modify it in anywway you want.

Valar

Anime super-resolution, with real degradation.

animerd v1

This is the first community model using the OmniSR network. I trained it mostly to test this network.\nNote: the model output decreases brightness. This is a well known issue we had with OmniSR.

4x_ardo

Purpose: Pretrain to quick start new models.

craft pretrain

Model trained only on downscale degradation (bicubic, bilinear, nearest, lanczos and mitchell). Can be used to start new Real-CUGAN models. ONNX included on the dir.

cugan_pretrain

Model trained only on downscale degradation (bicubic, bilinear, nearest, lanczos and mitchell). Can be used to start new DCTLSA models.

dctlsa_pretrained

Meant to quick start new DITN models trained on `neosr`. Trained only on downscale degradation (bicubic, bilinear, nearest, lanczos and mitchell).
Note: [Flash Attention](https://github.com/muslll/neosr/blob/master/neosr/archs/ditn_arch.py#L108) breaks inference compatibility with official code.

ditn_w16_pretrain

## RealPLKSR GAN pretrain

**Scale:** 4x  
**Architecture:** RealPLKSR  
**Download:** [GDrive](https://drive.google.com/file/d/1iQmsnMhWXsHLYuYEwwRhdj9fMg71h-IU/view) | Training files: [GDrive](https://drive.google.com/drive/folders/1OWLGBce6oe8W6kARINFCWG7o0O5LeC_h?usp=sharing)  

**Author:** musl  
**License:** [CC0](https://creativecommons.org/public-domain/cc0/)  
**Purpose:** Pretrain  
**Subject:** Multipurpose  
**Date:** 15 May 2024  

**Size:** default Real-PLKSR  
**I/O Channels:** 3(RGB)->3(RGB)  

**Dataset:** Nomos-v2  
**Dataset Size:** 6000  
**OTF (on the fly augmentations):** No  
**Pretrained Model:** 4x_realplksr_mssim_pretrain  
**Iterations:** ~450k  
**Batch Size:** 2-6  
**GT Size:** 128-416  

**Description:** Pretrained GAN models for RealPLKSR network. Trained on downsampling-only (nearest, bilinear, bicubic, lanczos and mitchell).

4x_realplksr_gan_pretrain

## RealPLKSR mssim pretrains

**Scale:** 2x and 4x  
**Architecture:** RealPLKSR  
**Links:**  

**`4x_realplksr_mssim_pretrain.pth`** | [GDrive](https://drive.google.com/file/d/12ek1vitEporWc5qqaYo6AMy0-RYlRqu8/view?usp=drive_link)  
**`2x_realplksr_mssim_pretrain.pth`** | [GDrive](https://drive.google.com/file/d/1GAdf5VOqYa5ntswT9sYsKKZ2Z7OQp7gO/view?usp=drive_link)  

**Author:** musl  
**License:** [CC0](https://creativecommons.org/public-domain/cc0/)  
**Purpose:** Pretrain  
**Subject:** Multipurpose  
**Date:** 08 May 2024  

**Size:** default Real-PLKSR  
**I/O Channels:** 3(RGB)->3(RGB)  

**Dataset:** Nomos-v2  
**Dataset Size:** 6000  
**OTF (on the fly augmentations):** No  
**Pretrained Model:** scratch  
**Iterations:** ~260k  
**Batch Size:** 2-6  
**GT Size:** 128-256  

**Description:** Pretrained models for RealPLKSR network. Trained on downsampling-only (nearest, bilinear, bicubic and lanczos).

4x_realplksr_mssim_pretrain

Please load it using `strict_load_g: false`. Model trained only on downscale degradation (bicubic, bilinear, nearest, lanczos and mitchell). Can be used to start new SPAN models.

musl's Models