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#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Power by Zongsheng Yue 2023-08-15 09:39:58
import argparse
import gradio as gr
from pathlib import Path
from omegaconf import OmegaConf
from sampler import ResShiftSampler
from utils import util_image
from basicsr.utils.download_util import load_file_from_url
_STEP = {
'v1': 15,
'v2': 15,
'v3': 4,
'bicsr': 4,
'inpaint_imagenet': 4,
'inpaint_face': 4,
'faceir': 4,
}
_LINK = {
'vqgan': 'https://github.com/zsyOAOA/ResShift/releases/download/v2.0/autoencoder_vq_f4.pth',
'vqgan_face256': 'https://github.com/zsyOAOA/ResShift/releases/download/v2.0/celeba256_vq_f4_dim3_face.pth',
'vqgan_face512': 'https://github.com/zsyOAOA/ResShift/releases/download/v2.0/ffhq512_vq_f8_dim8_face.pth',
'v1': 'https://github.com/zsyOAOA/ResShift/releases/download/v2.0/resshift_realsrx4_s15_v1.pth',
'v2': 'https://github.com/zsyOAOA/ResShift/releases/download/v2.0/resshift_realsrx4_s15_v2.pth',
'v3': 'https://github.com/zsyOAOA/ResShift/releases/download/v2.0/resshift_realsrx4_s4_v3.pth',
'bicsr': 'https://github.com/zsyOAOA/ResShift/releases/download/v2.0/resshift_bicsrx4_s4.pth',
'inpaint_imagenet': 'https://github.com/zsyOAOA/ResShift/releases/download/v2.0/resshift_inpainting_imagenet_s4.pth',
'inpaint_face': 'https://github.com/zsyOAOA/ResShift/releases/download/v2.0/resshift_inpainting_face_s4.pth',
'faceir': 'https://github.com/zsyOAOA/ResShift/releases/download/v2.0/resshift_faceir_s4.pth',
}
def get_configs(task='realsr', version='v3', scale=4):
ckpt_dir = Path('./weights')
if not ckpt_dir.exists():
ckpt_dir.mkdir()
if task == 'realsr':
if version in ['v1', 'v2']:
configs = OmegaConf.load('./configs/realsr_swinunet_realesrgan256.yaml')
elif version == 'v3':
configs = OmegaConf.load('./configs/realsr_swinunet_realesrgan256_journal.yaml')
else:
raise ValueError(f"Unexpected version type: {version}")
assert scale == 4, 'We only support the 4x super-resolution now!'
ckpt_url = _LINK[version]
ckpt_path = ckpt_dir / f'resshift_{task}x{scale}_s{_STEP[version]}_{version}.pth'
vqgan_url = _LINK['vqgan']
vqgan_path = ckpt_dir / f'autoencoder_vq_f4.pth'
elif task == 'bicsr':
configs = OmegaConf.load('./configs/bicx4_swinunet_lpips.yaml')
assert scale == 4, 'We only support the 4x super-resolution now!'
ckpt_url = _LINK[task]
ckpt_path = ckpt_dir / f'resshift_{task}x{scale}_s{_STEP[task]}.pth'
vqgan_url = _LINK['vqgan']
vqgan_path = ckpt_dir / f'autoencoder_vq_f4.pth'
# elif task == 'inpaint_imagenet':
# configs = OmegaConf.load('./configs/inpaint_lama256_imagenet.yaml')
# assert scale == 1, 'Please set scale equals 1 for image inpainting!'
# ckpt_url = _LINK[task]
# ckpt_path = ckpt_dir / f'resshift_{task}_s{_STEP[task]}.pth'
# vqgan_url = _LINK['vqgan']
# vqgan_path = ckpt_dir / f'autoencoder_vq_f4.pth'
# elif task == 'inpaint_face':
# configs = OmegaConf.load('./configs/inpaint_lama256_face.yaml')
# assert scale == 1, 'Please set scale equals 1 for image inpainting!'
# ckpt_url = _LINK[task]
# ckpt_path = ckpt_dir / f'resshift_{task}_s{_STEP[task]}.pth'
# vqgan_url = _LINK['vqgan_face256']
# vqgan_path = ckpt_dir / f'celeba256_vq_f4_dim3_face.pth'
# elif task == 'faceir':
# configs = OmegaConf.load('./configs/faceir_gfpgan512_lpips.yaml')
# assert scale == 1, 'Please set scale equals 1 for face restoration!'
# ckpt_url = _LINK[task]
# ckpt_path = ckpt_dir / f'resshift_{task}_s{_STEP[task]}.pth'
# vqgan_url = _LINK['vqgan_face512']
# vqgan_path = ckpt_dir / f'ffhq512_vq_f8_dim8_face.pth'
else:
raise TypeError(f"Unexpected task type: {task}!")
# prepare the checkpoint
if not ckpt_path.exists():
load_file_from_url(
url=ckpt_url,
model_dir=ckpt_dir,
progress=True,
file_name=ckpt_path.name,
)
if not vqgan_path.exists():
load_file_from_url(
url=vqgan_url,
model_dir=ckpt_dir,
progress=True,
file_name=vqgan_path.name,
)
configs.model.ckpt_path = str(ckpt_path)
configs.diffusion.params.sf = scale
configs.autoencoder.ckpt_path = str(vqgan_path)
return configs
def predict(in_path, task='realsrx4', seed=12345, scale=4, version='v3'):
configs = get_configs(task, version, scale)
resshift_sampler = ResShiftSampler(
configs,
sf=scale,
chop_size=256,
chop_stride=224,
chop_bs=1,
use_amp=True,
seed=seed,
padding_offset=configs.model.params.get('lq_size', 64),
)
out_dir = Path('restored_output')
if not out_dir.exists():
out_dir.mkdir()
resshift_sampler.inference(
in_path,
out_dir,
mask_path=None,
bs=1,
noise_repeat=False
)
out_path = out_dir / f"{Path(in_path).stem}.png"
assert out_path.exists(), 'Super-resolution failed!'
im_sr = util_image.imread(out_path, chn="rgb", dtype="uint8")
return im_sr, str(out_path)
title = "ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting"
description = r"""
<b>Official Gradio demo</b> for <a href='https://github.com/zsyOAOA/ResShift' target='_blank'><b>ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting</b></a>.<br>
🔥 ResShift is an efficient diffusion model designed for image super-resolution or restoration.<br>
"""
article = r"""
If ResShift is helpful for your work, please help to ⭐ the <a href='https://github.com/zsyOAOA/ResShift' target='_blank'>Github Repo</a>. Thanks!
[](https://github.com/zsyOAOA/ResShift)
---
If our work is useful for your research, please consider citing:
```bibtex
@inproceedings{yue2023resshift,
title={ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting},
author={Yue, Zongsheng and Wang, Jianyi and Loy, Chen Change},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
year={2023},
volume = {36},
pages = {13294--13307},
}
```
📋 **License**
This project is licensed under <a rel="license" href="https://github.com/zsyOAOA/ResShift/blob/master/LICENSE">S-Lab License 1.0</a>.
Redistribution and use for non-commercial purposes should follow this license.
📧 **Contact**
If you have any questions, please feel free to contact me via <b>[email protected]</b>.
"""
demo = gr.Interface(
fn=predict,
inputs=[
gr.Image(type="filepath", label="Input: Low Quality Image"),
gr.Dropdown(
choices=["realsr", "bicsr"],
value="realsr",
label="Task",
),
gr.Number(value=12345, precision=0, label="Ranom seed")
],
outputs=[
gr.Image(type="numpy", label="Output: High Quality Image"),
gr.File(label="Download the output")
],
title=title,
description=description,
article=article,
examples=[
['./testdata/RealSet65/0030.jpg', "realsr", 12345],
['./testdata/RealSet65/dog2.png', "realsr", 12345],
['./testdata/RealSet65/bears.jpg', "realsr", 12345],
['./testdata/RealSet65/oldphoto6.png', "realsr", 12345],
['./testdata/Bicubicx4/lq_matlab/ILSVRC2012_val_00000067.png', "bicsr", 12345],
['./testdata/Bicubicx4/lq_matlab/ILSVRC2012_val_00016898.png', "bicsr", 12345],
]
)
demo.queue(concurrency_count=4)
demo.launch(share=True)
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