my-sd/extensions-builtin/SwinIR/scripts/swinir_model.py

import logging
import sys

import numpy as np
import torch
from PIL import Image

from modules import modelloader, devices, script_callbacks, shared
from modules.shared import opts
from modules.upscaler import Upscaler, UpscalerData
from modules.upscaler_utils import tiled_upscale_2

SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"

logger = logging.getLogger(__name__)


class UpscalerSwinIR(Upscaler):
    def __init__(self, dirname):
        self._cached_model = None           # keep the model when SWIN_torch_compile is on to prevent re-compile every runs
        self._cached_model_config = None    # to clear '_cached_model' when changing model (v1/v2) or settings
        self.name = "SwinIR"
        self.model_url = SWINIR_MODEL_URL
        self.model_name = "SwinIR 4x"
        self.user_path = dirname
        super().__init__()
        scalers = []
        model_files = self.find_models(ext_filter=[".pt", ".pth"])
        for model in model_files:
            if model.startswith("http"):
                name = self.model_name
            else:
                name = modelloader.friendly_name(model)
            model_data = UpscalerData(name, model, self)
            scalers.append(model_data)
        self.scalers = scalers

    def do_upscale(self, img: Image.Image, model_file: str) -> Image.Image:
        current_config = (model_file, opts.SWIN_tile)

        device = self._get_device()

        if self._cached_model_config == current_config:
            model = self._cached_model
        else:
            try:
                model = self.load_model(model_file)
            except Exception as e:
                print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr)
                return img
            self._cached_model = model
            self._cached_model_config = current_config

        img = upscale(
            img,
            model,
            tile=opts.SWIN_tile,
            tile_overlap=opts.SWIN_tile_overlap,
            device=device,
        )
        devices.torch_gc()
        return img

    def load_model(self, path, scale=4):
        if path.startswith("http"):
            filename = modelloader.load_file_from_url(
                url=path,
                model_dir=self.model_download_path,
                file_name=f"{self.model_name.replace(' ', '_')}.pth",
            )
        else:
            filename = path

        model_descriptor = modelloader.load_spandrel_model(
            filename,
            device=self._get_device(),
            dtype=devices.dtype,
            expected_architecture="SwinIR",
        )
        if getattr(opts, 'SWIN_torch_compile', False):
            try:
                model_descriptor.model.compile()
            except Exception:
                logger.warning("Failed to compile SwinIR model, fallback to JIT", exc_info=True)
        return model_descriptor

    def _get_device(self):
        return devices.get_device_for('swinir')


def upscale(
    img,
    model,
    *,
    tile: int,
    tile_overlap: int,
    window_size=8,
    scale=4,
    device,
):

    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, dtype=devices.dtype)
    with torch.no_grad(), devices.autocast():
        _, _, h_old, w_old = img.size()
        h_pad = (h_old // window_size + 1) * window_size - h_old
        w_pad = (w_old // window_size + 1) * window_size - w_old
        img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
        img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
        output = tiled_upscale_2(
            img,
            model,
            tile_size=tile,
            tile_overlap=tile_overlap,
            scale=scale,
            device=device,
            desc="SwinIR tiles",
        )
        output = output[..., : h_old * scale, : w_old * scale]
        output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
        if output.ndim == 3:
            output = np.transpose(
                output[[2, 1, 0], :, :], (1, 2, 0)
            )  # CHW-RGB to HCW-BGR
        output = (output * 255.0).round().astype(np.uint8)  # float32 to uint8
        return Image.fromarray(output, "RGB")


def on_ui_settings():
    import gradio as gr

    shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
    shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
    shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run"))


script_callbacks.on_ui_settings(on_ui_settings)