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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/4xFFHQDAT) |
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# 4xFFHQDAT |
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Name: 4xFFHQDAT |
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Author: Philip Hofmann |
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Release Date: 25.08.2023 |
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License: CC BY 4.0 |
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Network: DAT |
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Scale: 4 |
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Purpose: 4x upscaling model for faces |
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Iterations: 122000 |
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epoch: 2 |
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batch_size: 4 |
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HR_size: 128 |
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Dataset: FFHQ - full dataset till 50k, then first 10k img multiscaled (resulted in ~260k imgs, 126GB) |
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Number of train images: 259990 |
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OTF Training: Yes |
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Pretrained_Model_G: DAT_x4.pth |
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Description: 4x photo upscaler for faces with otf jpg compression, blur and resize, trained on FFHQ dataset. This has been trained on and for faces, but i guess can also be used for other photos, might be able to retain skin detail. This is not face restoration, but simply a 4x upscaler trained on faces, therefore input images need to be of good quality if good output quality is desired. |
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Examples 4xFFHQDAT: |
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[Imgsli1](https://imgsli.com/MjAwNjUz) |
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[Imgsli2](https://imgsli.com/MjAwNjU0) |
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[Imgsli3](https://imgsli.com/MjAwNjU2) |
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[Imgsli4](https://imgsli.com/MjAwNjU3) |
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[Imgsli5](https://imgsli.com/MjAwNjU4) |
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[Imgsli6](https://imgsli.com/MjAwNjU5) |
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[Imgsli7](https://imgsli.com/MjAwNzk0) |
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Since the above 4xFFHQDAT model is not able to handle the noise present in low quality input images, i made a small variant/finetune of this, the 4xFFHQLDAT model. This model might come in handy if your input image is of bad quality/not suited for above model. I basically made this model in a response to an input image posted in upscaling-results channel as a request to this upscale model (since 4xFFHQDAT would not be able to handle noise), see Imgsli1 example below for result. |
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Name: 4xFFHQLDAT |
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Author: Philip Hofmann |
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Release Date: 25.08.2023 |
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License: CC BY 4.0 |
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Network: DAT |
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Scale: 4 |
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Purpose: 4x upscaling model for low quality input photos of faces |
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Iterations: 44000 |
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epoch: 0 |
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batch_size: 4 |
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HR_size: 128 |
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Dataset: FFHQ - full dataset till 50k, then first 10k img multiscaled (resulted in ~260k imgs, 126GB) |
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Number of train images: 259990 |
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OTF Training: Yes |
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Pretrained_Model_G: 4xFFHQDAT |
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Examples 4xFFHQLDAT: |
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[Imgsli1](https://imgsli.com/MjAwNjYx) |
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[Imgsli2](https://imgsli.com/MjAwNjYy) |
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[Imgsli3](https://imgsli.com/MjAwNjYz) |
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