4xLSDIRCompactR

Iterations: 130000
batch_size: Variable(1-5)
HR_size: 256
Dataset: LSDIR
Dataset_size: 84991
OTF Training No
Pretrained_Model_G: 4xLSDIRCompact.pth

Description: Extending my last 4xLSDIRCompact model to Real-ESRGAN, meaning trained on synthetic data instead to handle more kinds of degradations, it should be able to handle compression, noise, and slight blur.

Here is a comparison to show that 4xLSDIRCompact cannot handle compression artifacts, and that these two models will produce better output for that specific scenario. These models are not ‘better’ than the previous one, they are just meant to handle a different use case: https://imgsli.com/MTYyODY3

Example1 Example2 Example3 Example4 Example5

--- license: cc-by-4.0 pipeline_tag: image-to-image tags: - pytorch - super-resolution --- [Link to Github Release](https://github.com/Phhofm/models/releases/tag/4xHFA2k_ludvae_realplksr_dysample) # 4xLSDIRCompactR Name: 4xLSDIRCompactR Author: Philip Hofmann Release Date: 17.03.2023 License: CC BY 4.0 Network: SRVGGNetCompact Scale: 4 Purpose: 4x photo uspcaler that handles jpg compression, noise and slight Iterations: 130000 batch_size: Variable(1-5) HR_size: 256 Dataset: LSDIR Dataset_size: 84991 OTF Training No Pretrained_Model_G: 4xLSDIRCompact.pth Description: Extending my last 4xLSDIRCompact model to Real-ESRGAN, meaning trained on synthetic data instead to handle more kinds of degradations, it should be able to handle compression, noise, and slight blur. --- Here is a comparison to show that 4xLSDIRCompact cannot handle compression artifacts, and that these two models will produce better output for that specific scenario. These models are not ‘better’ than the previous one, they are just meant to handle a different use case: https://imgsli.com/MTYyODY3