Merge pull request #1371 from d8ahazard/master

        if not os.path.exists(filename) or filename is None:

            print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr)

            return None

        model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=2)  # define network

        model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4)  # define network

        model.load_state_dict(torch.load(filename), strict=True)

        model.eval()

        for k, v in model.named_parameters():

        self.scalers = [scaler_data]

    def load_model(self, path: str):

        # Remove incorrect project.yaml file if too big

        yaml_path = os.path.join(self.model_path, "project.yaml")

        old_model_path = os.path.join(self.model_path, "model.pth")

        new_model_path = os.path.join(self.model_path, "model.ckpt")

        if os.path.exists(yaml_path):

            statinfo = os.stat(yaml_path)

            if statinfo.st_size >= 10485760:

                print("Removing invalid LDSR YAML file.")

                os.remove(yaml_path)

        if os.path.exists(old_model_path):

            print("Renaming model from model.pth to model.ckpt")

            os.rename(old_model_path, new_model_path)

        model = load_file_from_url(url=self.model_url, model_dir=self.model_path,

                                   file_name="model.pth", progress=True)

                                   file_name="model.ckpt", progress=True)

        yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,

                                  file_name="project.yaml", progress=True)

            print("NO LDSR!")

            return img

        ddim_steps = shared.opts.ldsr_steps

        pre_scale = shared.opts.ldsr_pre_down

        return ldsr.super_resolution(img, ddim_steps, self.scale)

        im_og = image

        width_og, height_og = im_og.size

        # If we can adjust the max upscale size, then the 4 below should be our variable

        print("Foo")

        down_sample_rate = target_scale / 4

        print(f"Downsample rate is {down_sample_rate}")

        wd = width_og * down_sample_rate

        hd = height_og * down_sample_rate

        width_downsampled_pre = int(wd)

                f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')

            im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)

        else:

            print(f"Down sample rate is 1 from {target_scale} / 4")

            print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")

        logs = self.run(model["model"], im_og, diffusion_steps, eta)

        sample = logs["sample"]

import glob

import os

import shutil

import importlib

        for place in places:

            if os.path.exists(place):

                for file in os.listdir(place):

                for file in glob.iglob(place + '**/**', recursive=True):

                    full_path = os.path.join(place, file)

                    if os.path.isdir(full_path):

                        continue