2xLexicaRRDBNet Sharp
Name: 2xLexicaRRDBNetSharp 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...
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 31,9 MB
- Versão
- vRevision 0bf13a6
- Atualizado
- 26 de out. de 2026
- Downloads
- 0
- Fonte
- Hugging Face
Descrição da fonte
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
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.
Example1 Example2 Example3 Example4 Example5 Example6 Example7 Example8 Example9 Example10 Example11 Example12 Example13 Example14 Example15 Example16
Fontes
1 fonte
Fontes
1 fonteVerificado 26 de out.
Artefatos de modelo
1 artefato
Artefatos de modelo
1 artefato2xLexicaRRDBNet_Sharp.safetensors
safetensors · 31,9 MB · SHA-256 c3f31fc2acbd…1fd9 · Hugging Face
Download fileTrechos de fonte
3 trechos
Trechos de fonte
3 trechos--- 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) # 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 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.                