| --- |
| 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/2xLexicaRRDBNet) |
|
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| # 2xLexicaRRDBNet_Sharp |
| |
| Name: 2xLexicaRRDBNet_Sharp |
| Author: Philip Hofmann |
| Release Date: 01.06.2023 |
| License: CC BY 4.0 |
| Network: RRDBNet |
| Scale: 2 |
| Purpose: Upscaling AI generated images - a bit sharper then above model |
| Iterations: 220'000 |
| batch_size: 4 |
| HR_size: 128 |
| Epoch: 18 (require iter number per epoch: 10964) |
| Dataset: lexica-aperture-v3-small |
| Number of train images: 43856 |
| OTF Training: No |
| Pretrained_Model_G: None |
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| Description: Its like the above model, but trained for some more with l1_gt_usm and percep_gt_usm set to true, resulting in sharper outputs. I provide both so they can be chosen based on preferrence of the user. |
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