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 from torchvision.transforms.functional import rgb_to_grayscale last_file = None img_mode = "RGBA" def realesrgan(img, model_name, denoise_strength, face_enhance, outscale): """Real-ESRGAN function to restore (and upscale) images. """ if not img: return # Define model parameters 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' ] # Determine model paths 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) # Use dni to control the denoise strength 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] # Restorer Class upsampler = RealESRGANer( scale=netscale, model_path=model_path, dni_weight=dni_weight, model=model, tile=0, tile_pad=10, pre_pad=10, half=False, gpu_id=None ) # Use GFPGAN for face enhancement 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) # Convert the input PIL image to cv2 image, so that it can be processed by realesrgan cv_img = numpy.array(img) img = cv2.cvtColor(cv_img, cv2.COLOR_RGBA2BGRA) # Apply restoration 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=outscale) except RuntimeError as error: print('Error', error) print('If you encounter CUDA out of memory, try to set --tile with a smaller number.') else: # Save restored image and return it to the output Image component if img_mode == 'RGBA': # RGBA images should be saved in png format extension = 'png' else: extension = 'jpg' out_filename = f"output_{rnd_string(8)}.{extension}" cv2.imwrite(out_filename, output) global last_file last_file = out_filename return out_filename def rnd_string(x): """Returns a string of 'x' random characters """ characters = "abcdefghijklmnopqrstuvwxyz_0123456789" result = "".join((random.choice(characters)) for i in range(x)) return result def reset(): """Resets the Image components of the Gradio interface and deletes the last processed image """ global last_file if last_file: print(f"Deleting {last_file} ...") os.remove(last_file) last_file = None return gr.update(value=None), gr.update(value=None) def has_transparency(img): """This function works by first checking to see if a "transparency" property is defined in the image's info -- if so, we return "True". Then, if the image is using indexed colors (such as in GIFs), it gets the index of the transparent color in the palette (img.info.get("transparency", -1)) and checks if it's used anywhere in the canvas (img.getcolors()). If the image is in RGBA mode, then presumably it has transparency in it, but it double-checks by getting the minimum and maximum values of every color channel (img.getextrema()), and checks if the alpha channel's smallest value falls below 255. https://stackoverflow.com/questions/43864101/python-pil-check-if-image-is-transparent """ if img.info.get("transparency", None) is not None: return True if img.mode == "P": transparent = img.info.get("transparency", -1) for _, index in img.getcolors(): if index == transparent: return True elif img.mode == "RGBA": extrema = img.getextrema() if extrema[3][0] < 255: return True return False def image_properties(img): """Returns the dimensions (width and height) and color mode of the input image and also sets the global img_mode variable to be used by the realesrgan function """ global img_mode if img: if has_transparency(img): img_mode = "RGBA" else: img_mode = "RGB" properties = f"Resolution: Width: {img.size[0]}, Height: {img.size[1]} | Color Mode: {img_mode}" return properties def main(): # Gradio Interface with gr.Blocks(title="Real-ESRGAN Gradio Demo", theme="dark") as demo: gr.Markdown( """#
Ilaria Upscaler 💖
Do not use images over 750x750 especially with 4x the resolution upscaling, it will give you an error. Hugginface port of [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN). """ ) with gr.Accordion("Upscaling option"): with gr.Row(): model_name = gr.Dropdown(label="Upscaler model", choices=["RealESRGAN_x4plus", "RealESRNet_x4plus", "RealESRGAN_x4plus_anime_6B", "RealESRGAN_x2plus", "realesr-general-x4v3"], value="RealESRGAN_x4plus_anime_6B", show_label=True) denoise_strength = gr.Slider(label="Denoise Strength", minimum=0, maximum=1, step=0.1, value=0.5) outscale = gr.Slider(label="Resolution upscale", minimum=1, maximum=6, step=1, value=4, show_label=True) face_enhance = gr.Checkbox(label="Face Enhancement (GFPGAN)", ) with gr.Row(): with gr.Group(): input_image = gr.Image(label="Input Image", type="pil", image_mode="RGBA") input_image_properties = gr.Textbox(label="Image Properties", max_lines=1) output_image = gr.Image(label="Output Image", image_mode="RGBA") with gr.Row(): reset_btn = gr.Button("Remove images") restore_btn = gr.Button("Upscale") # Event listeners: input_image.change(fn=image_properties, inputs=input_image, outputs=input_image_properties) restore_btn.click(fn=realesrgan, inputs=[input_image, model_name, denoise_strength, face_enhance, outscale], outputs=output_image) reset_btn.click(fn=reset, inputs=[], outputs=[output_image, input_image]) # reset_btn.click(None, inputs=[], outputs=[input_image], _js="() => (null)\n") # Undocumented method to clear a component's value using Javascript gr.Markdown( """Made with love by Ilaria 💖 | Support me on [Ko-Fi](https://ko-fi.com/ilariaowo) | Join [AI Hub](https://discord.gg/aihub) """ ) demo.launch() if __name__ == "__main__": main()