Skip to content

Instantly share code, notes, and snippets.

@adamelliotfields

adamelliotfields/upscale.py

Created August 11, 2024 17:51
Show Gist options
  • Save adamelliotfields/0a696db8f28f72a66d5cd90da8eefd99 to your computer and use it in GitHub Desktop.
Save adamelliotfields/0a696db8f28f72a66d5cd90da8eefd99 to your computer and use it in GitHub Desktop.
Download ZIP
Real-ESRGAN Upscaler CLI
Raw
upscale.py
# Real-ESRGAN CLI with ai-forever/Real-ESRGAN weights and inference code.
# Usage: python upscale.py [--scale {2,4,8}] [--out STR] [IMAGES ...]
import argparse
import os
import einops
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
LICENSE = """BSD 3-Clause License
Copyright (c) 2021, Sberbank AI
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."""
# https://huggingface.co/ai-forever/Real-ESRGAN
HF_MODELS = {
2: dict(
repo_id="ai-forever/Real-ESRGAN",
filename="RealESRGAN_x2.pth",
),
4: dict(
repo_id="ai-forever/Real-ESRGAN",
filename="RealESRGAN_x4.pth",
),
8: dict(
repo_id="ai-forever/Real-ESRGAN",
filename="RealESRGAN_x8.pth",
),
}
def pad_reflect(image, pad_size):
imsize = image.shape
height, width = imsize[:2]
new_img = np.zeros([height + pad_size * 2, width + pad_size * 2, imsize[2]]).astype(np.uint8)
new_img[pad_size:-pad_size, pad_size:-pad_size, :] = image
new_img[0:pad_size, pad_size:-pad_size, :] = np.flip(image[0:pad_size, :, :], axis=0) # top
new_img[-pad_size:, pad_size:-pad_size, :] = np.flip(image[-pad_size:, :, :], axis=0) # bottom
new_img[:, 0:pad_size, :] = np.flip(new_img[:, pad_size : pad_size * 2, :], axis=1) # left
new_img[:, -pad_size:, :] = np.flip(new_img[:, -pad_size * 2 : -pad_size, :], axis=1) # right
return new_img
def unpad_image(image, pad_size):
return image[pad_size:-pad_size, pad_size:-pad_size, :]
def pad_patch(image_patch, padding_size, channel_last=True):
if channel_last:
return np.pad(
image_patch,
((padding_size, padding_size), (padding_size, padding_size), (0, 0)),
"edge",
)
else:
return np.pad(
image_patch,
((0, 0), (padding_size, padding_size), (padding_size, padding_size)),
"edge",
)
def unpad_patches(image_patches, padding_size):
return image_patches[:, padding_size:-padding_size, padding_size:-padding_size, :]
def split_image_into_overlapping_patches(image_array, patch_size, padding_size=2):
xmax, ymax, _ = image_array.shape
x_remainder = xmax % patch_size
y_remainder = ymax % patch_size
# modulo here is to avoid extending of patch_size instead of 0
x_extend = (patch_size - x_remainder) % patch_size
y_extend = (patch_size - y_remainder) % patch_size
# make sure the image is divisible into regular patches
extended_image = np.pad(image_array, ((0, x_extend), (0, y_extend), (0, 0)), "edge")
# add padding around the image to simplify computations
padded_image = pad_patch(extended_image, padding_size, channel_last=True)
patches = []
xmax, ymax, _ = padded_image.shape
x_lefts = range(padding_size, xmax - padding_size, patch_size)
y_tops = range(padding_size, ymax - padding_size, patch_size)
for x in x_lefts:
for y in y_tops:
x_left = x - padding_size
y_top = y - padding_size
x_right = x + patch_size + padding_size
y_bottom = y + patch_size + padding_size
patch = padded_image[x_left:x_right, y_top:y_bottom, :]
patches.append(patch)
return np.array(patches), padded_image.shape
def stitch_together(patches, padded_image_shape, target_shape, padding_size=4):
xmax, ymax, _ = padded_image_shape
patches = unpad_patches(patches, padding_size)
patch_size = patches.shape[1]
n_patches_per_row = ymax // patch_size
complete_image = np.zeros((xmax, ymax, 3))
row = -1
col = 0
for i in range(len(patches)):
if i % n_patches_per_row == 0:
row += 1
col = 0
complete_image[
row * patch_size : (row + 1) * patch_size, col * patch_size : (col + 1) * patch_size, :
] = patches[i]
col += 1
return complete_image[0 : target_shape[0], 0 : target_shape[1], :]
@torch.no_grad()
def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
if not isinstance(module_list, list):
module_list = [module_list]
for module in module_list:
for m in module.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, **kwargs)
m.weight.data *= scale
if m.bias is not None:
m.bias.data.fill_(bias_fill)
elif isinstance(m, nn.Linear):
init.kaiming_normal_(m.weight, **kwargs)
m.weight.data *= scale
if m.bias is not None:
m.bias.data.fill_(bias_fill)
elif isinstance(m, _BatchNorm):
init.constant_(m.weight, 1)
if m.bias is not None:
m.bias.data.fill_(bias_fill)
def make_layer(basic_block, num_basic_block, **kwarg):
layers = []
for _ in range(num_basic_block):
layers.append(basic_block(**kwarg))
return nn.Sequential(*layers)
def pixel_unshuffle(x, scale):
_, _, h, w = x.shape
assert h % scale == 0 and w % scale == 0, "Height and width must be divisible by scale"
return einops.rearrange(
x,
"b c (h s1) (w s2) -> b (c s1 s2) h w",
s1=scale,
s2=scale,
)
class ResidualDenseBlock(nn.Module):
def __init__(self, num_feat=64, num_grow_ch=32):
super(ResidualDenseBlock, self).__init__()
self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
def forward(self, x):
x1 = self.lrelu(self.conv1(x))
x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
return x5 * 0.2 + x # scale the residual by a factor of 0.2
class RRDB(nn.Module):
def __init__(self, num_feat, num_grow_ch=32):
super(RRDB, self).__init__()
self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
def forward(self, x):
out = self.rdb1(x)
out = self.rdb2(out)
out = self.rdb3(out)
return out * 0.2 + x # scale the residual by a factor of 0.2
class RRDBNet(nn.Module):
def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32):
super(RRDBNet, self).__init__()
self.scale = scale
if scale == 2:
num_in_ch = num_in_ch * 4
elif scale == 1:
num_in_ch = num_in_ch * 16
self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
if scale == 8:
self.conv_up3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, x):
if self.scale == 2:
feat = pixel_unshuffle(x, scale=2)
elif self.scale == 1:
feat = pixel_unshuffle(x, scale=4)
else:
feat = x
feat = self.conv_first(feat)
body_feat = self.conv_body(self.body(feat))
feat = feat + body_feat
# upsample
feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode="nearest")))
feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode="nearest")))
if self.scale == 8:
feat = self.lrelu(self.conv_up3(F.interpolate(feat, scale_factor=2, mode="nearest")))
out = self.conv_last(self.lrelu(self.conv_hr(feat)))
return out
class RealESRGAN:
def __init__(self, device, scale=4):
self.device = device
self.scale = scale
self.model = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=23,
num_grow_ch=32,
scale=scale,
)
def load_weights(self):
assert self.scale in [2, 4, 8], "You can download models only with scales: 2, 4, 8"
config = HF_MODELS[self.scale]
cache_path = hf_hub_download(config["repo_id"], filename=config["filename"])
loadnet = torch.load(cache_path)
if "params" in loadnet:
self.model.load_state_dict(loadnet["params"], strict=True)
elif "params_ema" in loadnet:
self.model.load_state_dict(loadnet["params_ema"], strict=True)
else:
self.model.load_state_dict(loadnet, strict=True)
self.model.eval()
self.model.to(self.device)
@torch.cuda.amp.autocast()
def predict(self, lr_image, batch_size=4, patches_size=192, padding=24, pad_size=15):
scale = self.scale
device = self.device
lr_image = np.array(lr_image)
lr_image = pad_reflect(lr_image, pad_size)
patches, p_shape = split_image_into_overlapping_patches(
lr_image,
patch_size=patches_size,
padding_size=padding,
)
img = einops.rearrange(torch.FloatTensor(patches / 255), "b h w c -> b c h w")
img = img.to(device).detach()
with torch.no_grad():
res = self.model(img[0:batch_size])
for i in range(batch_size, img.shape[0], batch_size):
res = torch.cat((res, self.model(img[i : i + batch_size])), 0)
sr_image = einops.rearrange(
res.clamp(0, 1),
"b c h w -> b h w c",
).cpu()
np_sr_image = sr_image.numpy()
padded_size_scaled = tuple(np.multiply(p_shape[0:2], scale)) + (3,)
scaled_image_shape = tuple(np.multiply(lr_image.shape[0:2], scale)) + (3,)
np_sr_image = stitch_together(
np_sr_image,
padded_image_shape=padded_size_scaled,
target_shape=scaled_image_shape,
padding_size=padding * scale,
)
sr_img = (np_sr_image * 255).astype(np.uint8)
sr_img = unpad_image(sr_img, pad_size * scale)
sr_img = Image.fromarray(sr_img)
return sr_img
def process_image(model, input, output):
image = Image.open(input).convert("RGB")
sr_image = model.predict(image)
sr_image.save(output)
def main():
# fmt: off
parser = argparse.ArgumentParser(description="Image upscaling with ai-forever/Real-ESRGAN.")
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument("images", nargs="*", default=[], metavar="IMAGES", help="input image file(s)")
parser.add_argument("-s", "--scale", type=int, choices=[2, 4, 8], default=4, metavar="INT", help="upscaling factor (default: 4)")
parser.add_argument("-o", "--out", default="out", metavar="STR", help="output directory (default: out)")
group.add_argument("--license", action="store_true", required=False, help="show original BSD-3 license")
args = parser.parse_args()
# fmt: on
if args.license:
print(LICENSE)
return
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = RealESRGAN(device, scale=args.scale)
model.load_weights()
if len(args.images):
os.makedirs(args.out, exist_ok=True)
for image_path in args.images:
if not os.path.exists(image_path):
print(f"{image_path} does not exist...")
continue
base_name, _ = os.path.splitext(os.path.basename(image_path))
out_path = os.path.join(args.out, f"{base_name}_{args.scale}x.png")
process_image(model, image_path, out_path)
print(f"{image_path} -> {out_path}")
if __name__ == "__main__":
main()
@adamelliotfields
Copy link
Author

Inference code is from ai-forever/Real-ESRGAN. Lots of unused code removed, Hugging Face cache used for weights, and Einops added for tensor reshaping operations.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment