4xBHI Realplksr Dysample Real
Scale: 4x Network type: RealPLKSR Author: Philip Hofmann License: CC-BY-4.0 Purpose: 4x upscaling images, handling some blur, some noise, and compression Pretrained Model: 4xNomos2realplksrdysample Training...
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 28,3 MB
- Versão
- vRevision 04267a3
- Atualizado
- 20 de out. de 2026
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- Hugging Face
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4xBHI_realplksr_dysample_real
Scale: 4x
Network type: RealPLKSR
Author: Philip Hofmann
License: CC-BY-4.0
Purpose: 4x upscaling images, handling some blur, some noise, and compression
Pretrained Model: 4xNomos2_realplksr_dysample
Training iterations: 380000
Description: 4x realplksr dysample model to upscale images, trained on the BHI dataset. This is with a created LR set where I used wtp_destroyer to add blur (50% chance), my ludvae200 model to add noise (50% chance), and then wtp to add compression, scaling, recompression (50% chance), scaling.
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1 fonteVerificado 20 de out.
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Artefatos de modelo
1 artefato4xBHI_realplksr_dysample_real.safetensors
safetensors · 28,3 MB · SHA-256 e4cbccc2e3c1…75c2 · Hugging Face
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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/4xbhi_realplksr) 4xBHI_realplksr_dysample_real Scale: 4x Network type: RealPLKSR Author: Philip Hofmann License: CC-BY-4.0 Purpose: 4x upscaling images, handling some blur, some noise, and compression Pretrained Model: 4xNomos2_realplksr_dysample Training iterations: 380000 Description: 4x realplksr dysample model to upscale images, trained on the BHI dataset. This is with a created LR set where I used wtp_destroyer to add blur (50% chance), my ludvae200 model to add noise (50% chance), and then wtp to add compression, scaling, recompression (50% chance), scaling.
Source context: 0 downloads · 5 likes · Pipeline image-to-image · Repo Phips/4xBHI_realplksr_dysample_real
Source context: 0 downloads · 4 likes · Pipeline image-to-image · Repo Phips/4xBHI_realplksr_dysample_real
