app.py

# mostly borrowed from TheStinger/Ilaria_Upscaler spaces.

import gradio as gr
import cv2
import numpy
import os
import random
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.utils.download_util import load_file_from_url


from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact


def model_params(model_name):
    if model_name == 'RealESRGAN_x4plus':  # x4 RRDBNet model
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
        netscale = 4
        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
    elif model_name == 'RealESRNet_x4plus':  # x4 RRDBNet model
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
        netscale = 4
        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
    elif model_name == 'RealESRGAN_x4plus_anime_6B':  # x4 RRDBNet model with 6 blocks
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
        netscale = 4
        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
    elif model_name == 'RealESRGAN_x2plus':  # x2 RRDBNet model
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
        netscale = 2
        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
    elif model_name == 'realesr-general-x4v3':  # x4 VGG-style model (S size)
        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
        netscale = 4
        file_url = [
            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
        ]

    return model, netscale, file_url


def upscale(image, model_name, tile, denoise, face_enhance, scale):
    if not image: return

    model, netscale, file_url = model_params(model_name)

    model_path = os.path.join('weights', model_name + '.pth')
    if not os.path.isfile(model_path):
        ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
        for url in file_url:
            # model_path will be updated
            model_path = load_file_from_url(
                url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)

    dni_weight = None
    if model_name == 'realesr-general-x4v3' and denoise_strength != 1:
        wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
        model_path = [model_path, wdn_model_path]
        dni_weight = [denoise_strength, 1 - denoise_strength]

    upsampler = RealESRGANer(
        scale=netscale,
        model_path=model_path,
        dni_weight=dni_weight,
        model=model,
        tile=tile,
        tile_pad=10,
        pre_pad=10,
        half=False,
        gpu_id=None
    )

    if face_enhance:
        from gfpgan import GFPGANer
        face_enhancer = GFPGANer(
            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
            upscale=outscale,
            arch='clean',
            channel_multiplier=2,
            bg_upsampler=upsampler)

    ################

    cv_img = numpy.array(image)
    img = cv2.cvtColor(cv_img, cv2.COLOR_RGBA2BGRA)

    try:
        if face_enhance:
            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
        else:
            output, _ = upsampler.enhance(img, outscale=scale)
    except RuntimeError as error:
        print('Error', error)
        print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')

    return output


app = gr.Interface(
    title='Real-ESRGAN Upscaler',
    description='Yet another Real-ESRGAN upscaler that uses gradio `Interface`, because why not? It’s not like there are any other options for simplicity and backward compatibility. Oh wait, there are. Never mind.',
    fn=upscale,
    inputs=[
        gr.Image(label='Source Image', type='pil', image_mode='RGBA'),
        gr.Dropdown(
            label='Model',
            choices=["RealESRGAN_x4plus", "RealESRNet_x4plus", "RealESRGAN_x4plus_anime_6B","RealESRGAN_x2plus", "realesr-general-x4v3"],
            show_label=True,
            value='RealESRGAN_x4plus_anime_6B'
        ),
        gr.Slider(
            label='Tile',
            minimum=0,
            maximum=1024,
            step=32,
            value=64
        ),
        gr.Slider(
            label='Denoise Strength',
            minimum=0,
            maximum=1,
            step=0.1,
            value=0.5
        ),
        gr.Checkbox(
            label='Face Enhancement (GFPGAN)',
            value=False,
            show_label=True
        ),
        gr.Slider(
            label='Upscale Size',
            minimum=1,
            maximum=4,
            step=1,
            value=2,
            show_label=True
        )
    ],
    outputs=[
        gr.Image(label='Upscaled Image', image_mode='RGBA')
    ]
)

app.launch(show_api=True)