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Running
on
T4
| import os, sys | |
| import cv2 | |
| import time | |
| import gradio as gr | |
| import torch | |
| import numpy as np | |
| from torchvision.utils import save_image | |
| # Import files from the local folder | |
| root_path = os.path.abspath('.') | |
| sys.path.append(root_path) | |
| from test_code.inference import super_resolve_img | |
| from test_code.test_utils import load_grl, load_rrdb | |
| def auto_download_if_needed(weight_path): | |
| if os.path.exists(weight_path): | |
| return | |
| if not os.path.exists("pretrained"): | |
| os.makedirs("pretrained") | |
| if weight_path == "pretrained/4x_APISR_GRL_GAN_generator.pth": | |
| os.system("wget https://github.com/Kiteretsu77/APISR/releases/download/v0.1.0/4x_APISR_GRL_GAN_generator.pth") | |
| os.system("mv 4x_APISR_GRL_GAN_generator.pth pretrained") | |
| if weight_path == "pretrained/2x_APISR_RRDB_GAN_generator.pth": | |
| os.system("wget https://github.com/Kiteretsu77/APISR/releases/download/v0.1.0/2x_APISR_RRDB_GAN_generator.pth") | |
| os.system("mv 2x_APISR_RRDB_GAN_generator.pth pretrained") | |
| def inference(img_path, model_name): | |
| try: | |
| weight_dtype = torch.float32 | |
| # Load the model | |
| if model_name == "4xGRL": | |
| weight_path = "pretrained/4x_APISR_GRL_GAN_generator.pth" | |
| auto_download_if_needed(weight_path) | |
| generator = load_grl(weight_path, scale=4) # Directly use default way now | |
| elif model_name == "2xRRDB": | |
| weight_path = "pretrained/2x_APISR_RRDB_GAN_generator.pth" | |
| auto_download_if_needed(weight_path) | |
| generator = load_rrdb(weight_path, scale=2) # Directly use default way now | |
| else: | |
| raise gr.Error("We don't support such Model") | |
| generator = generator.to(dtype=weight_dtype) | |
| # In default, we will automatically use crop to match 4x size | |
| super_resolved_img = super_resolve_img(generator, img_path, output_path=None, weight_dtype=weight_dtype, crop_for_4x=True) | |
| store_name = str(time.time()) + ".png" | |
| save_image(super_resolved_img, store_name) | |
| outputs = cv2.imread(store_name) | |
| outputs = cv2.cvtColor(outputs, cv2.COLOR_RGB2BGR) | |
| return outputs | |
| except Exception as error: | |
| raise gr.Error(f"global exception: {error}") | |
| if __name__ == '__main__': | |
| MARKDOWN = \ | |
| """ | |
| ## <p style='text-align: center'> APISR: Anime Production Inspired Real-World Anime Super-Resolution (CVPR 2024) </p> | |
| [GitHub](https://github.com/Kiteretsu77/APISR) | [Paper](https://arxiv.org/abs/2403.01598) | |
| APISR aims at restoring and enhancing low-quality low-resolution anime images and video sources with various degradations from real-world scenarios. | |
| If APISR is helpful, please help star the GitHub Repo. Thanks! | |
| """ | |
| block = gr.Blocks().queue(max_size=10) | |
| with block: | |
| with gr.Row(): | |
| gr.Markdown(MARKDOWN) | |
| with gr.Row(elem_classes=["container"]): | |
| with gr.Column(scale=2): | |
| input_image = gr.Image(type="filepath", label="Input") | |
| model_name = gr.Dropdown( | |
| [ | |
| "2xRRDB", | |
| "4xGRL" | |
| ], | |
| type="value", | |
| value="4xGRL", | |
| label="model", | |
| ) | |
| run_btn = gr.Button(value="Submit") | |
| with gr.Column(scale=3): | |
| output_image = gr.Image(type="numpy", label="Output image") | |
| with gr.Row(elem_classes=["container"]): | |
| gr.Examples( | |
| [ | |
| ["__assets__/lr_inputs/image-00277.png"], | |
| ["__assets__/lr_inputs/image-00542.png"], | |
| ["__assets__/lr_inputs/41.png"], | |
| ["__assets__/lr_inputs/f91.jpg"], | |
| ["__assets__/lr_inputs/image-00440.png"], | |
| ["__assets__/lr_inputs/image-00164.jpg"], | |
| ["__assets__/lr_inputs/img_eva.jpeg"], | |
| ], | |
| [input_image], | |
| ) | |
| run_btn.click(inference, inputs=[input_image, model_name], outputs=[output_image]) | |
| block.launch() |