4xFFHQDAT

Link to Github Release

4xFFHQDAT

Name: 4xFFHQDAT
Author: Philip Hofmann
Release Date: 25.08.2023
License: CC BY 4.0
Network: DAT
Scale: 4
Purpose: 4x upscaling model for faces
Iterations: 122000
epoch: 2
batch_size: 4
HR_size: 128
Dataset: FFHQ - full dataset till 50k, then first 10k img multiscaled (resulted in ~260k imgs, 126GB)
Number of train images: 259990
OTF Training: Yes
Pretrained_Model_G: DAT_x4.pth

Description: 4x photo upscaler for faces with otf jpg compression, blur and resize, trained on FFHQ dataset. This has been trained on and for faces, but i guess can also be used for other photos, might be able to retain skin detail. This is not face restoration, but simply a 4x upscaler trained on faces, therefore input images need to be of good quality if good output quality is desired.

Examples 4xFFHQDAT:
Imgsli1
Imgsli2
Imgsli3
Imgsli4
Imgsli5
Imgsli6
Imgsli7

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Since the above 4xFFHQDAT model is not able to handle the noise present in low quality input images, i made a small variant/finetune of this, the 4xFFHQLDAT model. This model might come in handy if your input image is of bad quality/not suited for above model. I basically made this model in a response to an input image posted in upscaling-results channel as a request to this upscale model (since 4xFFHQDAT would not be able to handle noise), see Imgsli1 example below for result.

Name: 4xFFHQLDAT
Author: Philip Hofmann
Release Date: 25.08.2023
License: CC BY 4.0
Network: DAT
Scale: 4
Purpose: 4x upscaling model for low quality input photos of faces
Iterations: 44000
epoch: 0
batch_size: 4
HR_size: 128
Dataset: FFHQ - full dataset till 50k, then first 10k img multiscaled (resulted in ~260k imgs, 126GB)
Number of train images: 259990
OTF Training: Yes
Pretrained_Model_G: 4xFFHQDAT

Examples 4xFFHQLDAT:
Imgsli1
Imgsli2
Imgsli3