stable-diffusion-webui/modules/esrgan_model.py

import os

import numpy as np
import torch
from PIL import Image
from basicsr.utils.download_util import load_file_from_url

import modules.esrgan_model_arch as arch
from modules import shared, modelloader, images, devices
from modules.upscaler import Upscaler, UpscalerData
from modules.shared import opts



def mod2normal(state_dict):
    # this code is copied from https://github.com/victorca25/iNNfer
    if 'conv_first.weight' in state_dict:
        crt_net = {}
        items = []
        for k, v in state_dict.items():
            items.append(k)

        crt_net['model.0.weight'] = state_dict['conv_first.weight']
        crt_net['model.0.bias'] = state_dict['conv_first.bias']

        for k in items.copy():
            if 'RDB' in k:
                ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
                if '.weight' in k:
                    ori_k = ori_k.replace('.weight', '.0.weight')
                elif '.bias' in k:
                    ori_k = ori_k.replace('.bias', '.0.bias')
                crt_net[ori_k] = state_dict[k]
                items.remove(k)

        crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight']
        crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias']
        crt_net['model.3.weight'] = state_dict['upconv1.weight']
        crt_net['model.3.bias'] = state_dict['upconv1.bias']
        crt_net['model.6.weight'] = state_dict['upconv2.weight']
        crt_net['model.6.bias'] = state_dict['upconv2.bias']
        crt_net['model.8.weight'] = state_dict['HRconv.weight']
        crt_net['model.8.bias'] = state_dict['HRconv.bias']
        crt_net['model.10.weight'] = state_dict['conv_last.weight']
        crt_net['model.10.bias'] = state_dict['conv_last.bias']
        state_dict = crt_net
    return state_dict


def resrgan2normal(state_dict, nb=23):
    # this code is copied from https://github.com/victorca25/iNNfer
    if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
        crt_net = {}
        items = []
        for k, v in state_dict.items():
            items.append(k)

        crt_net['model.0.weight'] = state_dict['conv_first.weight']
        crt_net['model.0.bias'] = state_dict['conv_first.bias']

        for k in items.copy():
            if "rdb" in k:
                ori_k = k.replace('body.', 'model.1.sub.')
                ori_k = ori_k.replace('.rdb', '.RDB')
                if '.weight' in k:
                    ori_k = ori_k.replace('.weight', '.0.weight')
                elif '.bias' in k:
                    ori_k = ori_k.replace('.bias', '.0.bias')
                crt_net[ori_k] = state_dict[k]
                items.remove(k)

        crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight']
        crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias']
        crt_net['model.3.weight'] = state_dict['conv_up1.weight']
        crt_net['model.3.bias'] = state_dict['conv_up1.bias']
        crt_net['model.6.weight'] = state_dict['conv_up2.weight']
        crt_net['model.6.bias'] = state_dict['conv_up2.bias']
        crt_net['model.8.weight'] = state_dict['conv_hr.weight']
        crt_net['model.8.bias'] = state_dict['conv_hr.bias']
        crt_net['model.10.weight'] = state_dict['conv_last.weight']
        crt_net['model.10.bias'] = state_dict['conv_last.bias']
        state_dict = crt_net
    return state_dict


def infer_params(state_dict):
    # this code is copied from https://github.com/victorca25/iNNfer
    scale2x = 0
    scalemin = 6
    n_uplayer = 0
    plus = False

    for block in list(state_dict):
        parts = block.split(".")
        n_parts = len(parts)
        if n_parts == 5 and parts[2] == "sub":
            nb = int(parts[3])
        elif n_parts == 3:
            part_num = int(parts[1])
            if (part_num > scalemin
                and parts[0] == "model"
                and parts[2] == "weight"):
                scale2x += 1
            if part_num > n_uplayer:
                n_uplayer = part_num
                out_nc = state_dict[block].shape[0]
        if not plus and "conv1x1" in block:
            plus = True

    nf = state_dict["model.0.weight"].shape[0]
    in_nc = state_dict["model.0.weight"].shape[1]
    out_nc = out_nc
    scale = 2 ** scale2x

    return in_nc, out_nc, nf, nb, plus, scale


class UpscalerESRGAN(Upscaler):
    def __init__(self, dirname):
        self.name = "ESRGAN"
        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth"
        self.model_name = "ESRGAN_4x"
        self.scalers = []
        self.user_path = dirname
        super().__init__()
        model_paths = self.find_models(ext_filter=[".pt", ".pth"])
        scalers = []
        if len(model_paths) == 0:
            scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
            scalers.append(scaler_data)
        for file in model_paths:
            if "http" in file:
                name = self.model_name
            else:
                name = modelloader.friendly_name(file)

            scaler_data = UpscalerData(name, file, self, 4)
            self.scalers.append(scaler_data)

    def do_upscale(self, img, selected_model):
        model = self.load_model(selected_model)
        if model is None:
            return img
        model.to(devices.device_esrgan)
        img = esrgan_upscale(model, img)
        return img

    def load_model(self, path: str):
        if "http" in path:
            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
                                          file_name="%s.pth" % self.model_name,
                                          progress=True)
        else:
            filename = path
        if not os.path.exists(filename) or filename is None:
            print("Unable to load %s from %s" % (self.model_path, filename))
            return None

        state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)

        if "params_ema" in state_dict:
            state_dict = state_dict["params_ema"]
        elif "params" in state_dict:
            state_dict = state_dict["params"]
            num_conv = 16 if "realesr-animevideov3" in filename else 32
            model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu')
            model.load_state_dict(state_dict)
            model.eval()
            return model

        if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict:
            nb = 6 if "RealESRGAN_x4plus_anime_6B" in filename else 23
            state_dict = resrgan2normal(state_dict, nb)
        elif "conv_first.weight" in state_dict:
            state_dict = mod2normal(state_dict)
        elif "model.0.weight" not in state_dict:
            raise Exception("The file is not a recognized ESRGAN model.")

        in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict)

        model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus)
        model.load_state_dict(state_dict)
        model.eval()

        return model


def upscale_without_tiling(model, img):
    img = np.array(img)
    img = img[:, :, ::-1]
    img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
    img = torch.from_numpy(img).float()
    img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_esrgan)
    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]
    return Image.fromarray(output, 'RGB')


def esrgan_upscale(model, img):
    if opts.ESRGAN_tile == 0:
        return upscale_without_tiling(model, img)

    grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
    newtiles = []
    scale_factor = 1

    for y, h, row in grid.tiles:
        newrow = []
        for tiledata in row:
            x, w, tile = tiledata

            output = upscale_without_tiling(model, tile)
            scale_factor = output.width // tile.width

            newrow.append([x * scale_factor, w * scale_factor, output])
        newtiles.append([y * scale_factor, h * scale_factor, newrow])

    newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
    output = images.combine_grid(newgrid)
    return output