How To Upscale


Link to Github Release

Name: 4xRealWebPhoto_v3_atd
License: CC BY 4.0
Author: Philip Hofmann
Network: ATD
Scale: 4
Release Date: 22.03.2024
Purpose: 4x upscaler for photos downloaded from the web
Iterations: 250'000
epoch: 10
batch_size: 6, 3
HR_size: 128, 192
Dataset: 4xRealWebPhoto_v3
Number of train images: 101'904
OTF Training: No
Pretrained_Model_G: 003_ATD_SRx4_finetune

4x real web photo upscaler, meant for upscaling photos downloaded from the web. Trained on my v3 of my 4xRealWebPhoto dataset, it should be able to handle noise, jpg and webp (re)compression, (re)scaling, and just a little bit of lens blur, while also be able to handle good quality input. Trained on the very recently released (~2 weeks ago) Adaptive-Token-Dictionary network.

My 4xRealWebPhoto dataset tried to simulate the use-case of a photo being uploaded to the web and being processed by the service provides (like on a social media platform) so compression/downscaling, then maybe being downloaded and re-uploaded by another used where it, again, were processed by the service provider. I included different variants in the dataset. The pdf with info to the v2 dataset can be found here, while i simply included whats different in the v3 png:


Training details: AdamW optimizer with U-Net SN discriminator and BFloat16. Degraded with otf jpg compression down to 40, re-compression down to 40, together with resizes and the blur kernels.
Losses: PixelLoss using CHC (Clipped Huber with Cosine Similarity Loss), PerceptualLoss using Huber, GANLoss, LDL using Huber, Focal Frequency, Gradient Variance with Huber, YCbCr Color Loss (bt601) and Luma Loss (CIE XYZ) on neosr with norm: true.

11 Examples: Slowpics

Color Mode
Private use
Commercial use
Credit required
State Changes
No Liability & Warranty
Dataset size101904
Training iterations250000
Training epochs10
Training batch size3
Training HR size192
Training OTFNo

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