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--- |
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license: cc-by-4.0 |
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pipeline_tag: image-to-image |
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tags: |
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- pytorch |
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- super-resolution |
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--- |
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[Link to Github Release](https://github.com/Phhofm/models/releases/tag/4xHFA2kLUDVAESwinIR_light%264xHFA2kLUDVAESRFormer_light) |
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# 4xHFA2kLUDVAESwinIR_light |
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Name: 4xHFA2kLUDVAESwinIR_light |
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Author: Philip Hofmann |
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Release Date: 10.06.2023 |
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License: CC BY 4.0 |
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Network: SwinIR |
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Arch Option: SwinIR-light |
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Scale: 4 |
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Purpose: An lightweight anime 4x upscaling model with realistic degradations, based on musl's HFA2k_LUDVAE dataset |
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Iterations: 350,000 |
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batch_size: 3 |
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HR_size: 256 |
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Epoch: 99 (require iter number per epoch: 3424) |
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Dataset: HFA2kLUDVAE |
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Number of train images: 10270 |
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OTF Training: No |
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Pretrained_Model_G: None |
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Description: 4x lightweight anime upscaler with realistic degradations (compression, noise, blur). Visual outputs can be found on https://github.com/Phhofm/models/tree/main/4xHFA2kLUDVAE_results, together with timestamps and metrics to compare inference speed on the val set with other trained models/networks on this dataset. |
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