  Here is a fully trained model of EDSR (Enhanced Deep Residual Networks for Single Image Super-Resolution) model. This model surpassed the performance of the current available SOTA models.  Spaces link - https://huggingface.co/spaces/keras-io/EDSR  Paper Link - https://arxiv.org/pdf/1707.02921  Keras Example link - https://keras.io/examples/vision/edsr/  It was trained for 500 epochs with 200 steps each.  TODO:  Hack to make this work for any image size. Currently the model takes input of image size 150 x 150. We pad the input image with transparant pixels so that it is a square image, which is a multiple of 150 x 150 Then we chop the image into multiple 150 x 150 sub images Upscale it and stich it together.  The output image might look a bit off, because each sub-image dosent have data about other sub-images. This approach assumes that the subimage has enough data about its surroundings 