InvokeAI/ldm/gfpgan/gfpgan_tools.py

import torch
import warnings
import os
import sys
import numpy as np

from PIL import Image
from scripts.dream import create_argv_parser

arg_parser = create_argv_parser()
opt        = arg_parser.parse_args()

def run_gfpgan(image, strength, seed, upsampler_scale=4):
    print(f'>> GFPGAN - Restoring Faces for image seed:{seed}')
    gfpgan = None
    with warnings.catch_warnings():
        warnings.filterwarnings('ignore', category=DeprecationWarning)
        warnings.filterwarnings('ignore', category=UserWarning)
        
        model_path          = os.path.join(opt.gfpgan_dir, opt.gfpgan_model_path)
        gfpgan_model_exists = os.path.isfile(model_path)

        try:
            if not gfpgan_model_exists:
                raise Exception('GFPGAN model not found at path ' + model_path)

            sys.path.append(os.path.abspath(opt.gfpgan_dir))
            from gfpgan import GFPGANer

            bg_upsampler = _load_gfpgan_bg_upsampler(
                opt.gfpgan_bg_upsampler, upsampler_scale, opt.gfpgan_bg_tile
            )

            gfpgan = GFPGANer(
                model_path=model_path,
                upscale=upsampler_scale,
                arch='clean',
                channel_multiplier=2,
                bg_upsampler=bg_upsampler,
            )
        except Exception:
            import traceback

            print('>> Error loading GFPGAN:', file=sys.stderr)
            print(traceback.format_exc(), file=sys.stderr)

    if gfpgan is None:
        print(
            f'>> WARNING: GFPGAN not initialized.'
        )
        print(
            f'>> Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth to {model_path}, \nor change GFPGAN directory with --gfpgan_dir.'
        )
        return image

    image = image.convert('RGB')

    cropped_faces, restored_faces, restored_img = gfpgan.enhance(
        np.array(image, dtype=np.uint8),
        has_aligned=False,
        only_center_face=False,
        paste_back=True,
    )
    res = Image.fromarray(restored_img)

    if strength < 1.0:
        # Resize the image to the new image if the sizes have changed
        if restored_img.size != image.size:
            image = image.resize(res.size)
        res = Image.blend(image, res, strength)

    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    gfpgan = None

    return res


def _load_gfpgan_bg_upsampler(bg_upsampler, upsampler_scale, bg_tile=400):
    if bg_upsampler == 'realesrgan':
        if not torch.cuda.is_available(): # CPU or MPS on M1
            use_half_precision = False
        else:
            use_half_precision = True

        model_path = {
            2: 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
            4: 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth',
        }

        if upsampler_scale not in model_path:
            return None

        from basicsr.archs.rrdbnet_arch import RRDBNet
        from realesrgan import RealESRGANer

        if upsampler_scale == 4:
            model = RRDBNet(
                num_in_ch=3,
                num_out_ch=3,
                num_feat=64,
                num_block=23,
                num_grow_ch=32,
                scale=4,
            )
        if upsampler_scale == 2:
            model = RRDBNet(
                num_in_ch=3,
                num_out_ch=3,
                num_feat=64,
                num_block=23,
                num_grow_ch=32,
                scale=2,
            )

        bg_upsampler = RealESRGANer(
            scale=upsampler_scale,
            model_path=model_path[upsampler_scale],
            model=model,
            tile=bg_tile,
            tile_pad=10,
            pre_pad=0,
            half=use_half_precision,
        )
    else:
        bg_upsampler = None

    return bg_upsampler


def real_esrgan_upscale(image, strength, upsampler_scale, seed):
    print(
        f'>> Real-ESRGAN Upscaling seed:{seed} : scale:{upsampler_scale}x'
    )

    with warnings.catch_warnings():
        warnings.filterwarnings('ignore', category=DeprecationWarning)
        warnings.filterwarnings('ignore', category=UserWarning)

        try:
            upsampler = _load_gfpgan_bg_upsampler(
                opt.gfpgan_bg_upsampler, upsampler_scale, opt.gfpgan_bg_tile
            )
        except Exception:
            import traceback

            print('>> Error loading Real-ESRGAN:', file=sys.stderr)
            print(traceback.format_exc(), file=sys.stderr)

    output, img_mode = upsampler.enhance(
        np.array(image, dtype=np.uint8),
        outscale=upsampler_scale,
        alpha_upsampler=opt.gfpgan_bg_upsampler,
    )

    res = Image.fromarray(output)

    if strength < 1.0:
        # Resize the image to the new image if the sizes have changed
        if output.size != image.size:
            image = image.resize(res.size)
        res = Image.blend(image, res, strength)

    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    upsampler = None

    return res