add tiled inference support for ScuNET

import PIL.Image

import numpy as np

import torch

from tqdm import tqdm

from basicsr.utils.download_util import load_file_from_url

import modules.upscaler

from modules import devices, modelloader

from scunet_model_arch import SCUNet as net

from modules.shared import opts

from modules import images

class UpscalerScuNET(modules.upscaler.Upscaler):

            scalers.append(scaler_data2)

        self.scalers = scalers

    def do_upscale(self, img: PIL.Image, selected_file):

    @staticmethod

    @torch.no_grad()

    def tiled_inference(img, model):

        # test the image tile by tile

        h, w = img.shape[2:]

        tile = opts.SCUNET_tile

        tile_overlap = opts.SCUNET_tile_overlap

        if tile == 0:

            return model(img)

        device = devices.get_device_for('scunet')

        assert tile % 8 == 0, "tile size should be a multiple of window_size"

        sf = 1

        stride = tile - tile_overlap

        h_idx_list = list(range(0, h - tile, stride)) + [h - tile]

        w_idx_list = list(range(0, w - tile, stride)) + [w - tile]

        E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)

        W = torch.zeros_like(E, dtype=devices.dtype, device=device)

        with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:

            for h_idx in h_idx_list:

                for w_idx in w_idx_list:

                    in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]

                    out_patch = model(in_patch)

                    out_patch_mask = torch.ones_like(out_patch)

                    E[

                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf

                    ].add_(out_patch)

                    W[

                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf

                    ].add_(out_patch_mask)

                    pbar.update(1)

        output = E.div_(W)

        return output

    def do_upscale(self, img: PIL.Image.Image, selected_file):

        torch.cuda.empty_cache()

        model = self.load_model(selected_file)

        if model is None:

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

            return img

        device = devices.get_device_for('scunet')

        img = np.array(img)

        img = img[:, :, ::-1]

        img = np.moveaxis(img, 2, 0) / 255

        img = torch.from_numpy(img).float()

        img = img.unsqueeze(0).to(device)

        with torch.no_grad():

            output = model(img)

        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()

        output = 255. * np.moveaxis(output, 0, 2)

        output = output.astype(np.uint8)

        output = output[:, :, ::-1]

        tile = opts.SCUNET_tile

        h, w = img.height, img.width

        np_img = np.array(img)

        np_img = np_img[:, :, ::-1]  # RGB to BGR

        np_img = np_img.transpose((2, 0, 1)) / 255  # HWC to CHW

        torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device)  # type: ignore

        if tile > h or tile > w:

            _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)

            _img[:, :, :h, :w] = torch_img # pad image

            torch_img = _img

        torch_output = self.tiled_inference(torch_img, model).squeeze(0)

        torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any

        np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()

        del torch_img, torch_output

        torch.cuda.empty_cache()

        return PIL.Image.fromarray(output, 'RGB')

        output = np_output.transpose((1, 2, 0))  # CHW to HWC

        output = output[:, :, ::-1]  # BGR to RGB

        return PIL.Image.fromarray((output * 255).astype(np.uint8))

    def load_model(self, path: str):

        device = devices.get_device_for('scunet')

        model = model.to(device)

        return model

    "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),

    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),

    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),

    "SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),

    "SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),

}))

options_templates.update(options_section(('face-restoration', "Face restoration"), {