Upload lora-scripts/sd-scripts/tools/latent_upscaler.py with huggingface_hub
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lora-scripts/sd-scripts/tools/latent_upscaler.py
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1 |
+
# 外部から簡単にupscalerを呼ぶためのスクリプト
|
2 |
+
# 単体で動くようにモデル定義も含めている
|
3 |
+
|
4 |
+
import argparse
|
5 |
+
import glob
|
6 |
+
import os
|
7 |
+
import cv2
|
8 |
+
from diffusers import AutoencoderKL
|
9 |
+
|
10 |
+
from typing import Dict, List
|
11 |
+
import numpy as np
|
12 |
+
|
13 |
+
import torch
|
14 |
+
from library.device_utils import init_ipex, get_preferred_device
|
15 |
+
init_ipex()
|
16 |
+
|
17 |
+
from torch import nn
|
18 |
+
from tqdm import tqdm
|
19 |
+
from PIL import Image
|
20 |
+
from library.utils import setup_logging
|
21 |
+
setup_logging()
|
22 |
+
import logging
|
23 |
+
logger = logging.getLogger(__name__)
|
24 |
+
|
25 |
+
class ResidualBlock(nn.Module):
|
26 |
+
def __init__(self, in_channels, out_channels=None, kernel_size=3, stride=1, padding=1):
|
27 |
+
super(ResidualBlock, self).__init__()
|
28 |
+
|
29 |
+
if out_channels is None:
|
30 |
+
out_channels = in_channels
|
31 |
+
|
32 |
+
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False)
|
33 |
+
self.bn1 = nn.BatchNorm2d(out_channels)
|
34 |
+
self.relu1 = nn.ReLU(inplace=True)
|
35 |
+
|
36 |
+
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size, stride, padding, bias=False)
|
37 |
+
self.bn2 = nn.BatchNorm2d(out_channels)
|
38 |
+
|
39 |
+
self.relu2 = nn.ReLU(inplace=True) # このReLUはresidualに足す前にかけるほうがいいかも
|
40 |
+
|
41 |
+
# initialize weights
|
42 |
+
self._initialize_weights()
|
43 |
+
|
44 |
+
def _initialize_weights(self):
|
45 |
+
for m in self.modules():
|
46 |
+
if isinstance(m, nn.Conv2d):
|
47 |
+
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
|
48 |
+
if m.bias is not None:
|
49 |
+
nn.init.constant_(m.bias, 0)
|
50 |
+
elif isinstance(m, nn.BatchNorm2d):
|
51 |
+
nn.init.constant_(m.weight, 1)
|
52 |
+
nn.init.constant_(m.bias, 0)
|
53 |
+
elif isinstance(m, nn.Linear):
|
54 |
+
nn.init.normal_(m.weight, 0, 0.01)
|
55 |
+
nn.init.constant_(m.bias, 0)
|
56 |
+
|
57 |
+
def forward(self, x):
|
58 |
+
residual = x
|
59 |
+
|
60 |
+
out = self.conv1(x)
|
61 |
+
out = self.bn1(out)
|
62 |
+
out = self.relu1(out)
|
63 |
+
|
64 |
+
out = self.conv2(out)
|
65 |
+
out = self.bn2(out)
|
66 |
+
|
67 |
+
out += residual
|
68 |
+
|
69 |
+
out = self.relu2(out)
|
70 |
+
|
71 |
+
return out
|
72 |
+
|
73 |
+
|
74 |
+
class Upscaler(nn.Module):
|
75 |
+
def __init__(self):
|
76 |
+
super(Upscaler, self).__init__()
|
77 |
+
|
78 |
+
# define layers
|
79 |
+
# latent has 4 channels
|
80 |
+
|
81 |
+
self.conv1 = nn.Conv2d(4, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
82 |
+
self.bn1 = nn.BatchNorm2d(128)
|
83 |
+
self.relu1 = nn.ReLU(inplace=True)
|
84 |
+
|
85 |
+
# resblocks
|
86 |
+
# 数の暴力で20個:次元数を増やすよりもブロックを増やしたほうがreceptive fieldが広がるはずだぞ
|
87 |
+
self.resblock1 = ResidualBlock(128)
|
88 |
+
self.resblock2 = ResidualBlock(128)
|
89 |
+
self.resblock3 = ResidualBlock(128)
|
90 |
+
self.resblock4 = ResidualBlock(128)
|
91 |
+
self.resblock5 = ResidualBlock(128)
|
92 |
+
self.resblock6 = ResidualBlock(128)
|
93 |
+
self.resblock7 = ResidualBlock(128)
|
94 |
+
self.resblock8 = ResidualBlock(128)
|
95 |
+
self.resblock9 = ResidualBlock(128)
|
96 |
+
self.resblock10 = ResidualBlock(128)
|
97 |
+
self.resblock11 = ResidualBlock(128)
|
98 |
+
self.resblock12 = ResidualBlock(128)
|
99 |
+
self.resblock13 = ResidualBlock(128)
|
100 |
+
self.resblock14 = ResidualBlock(128)
|
101 |
+
self.resblock15 = ResidualBlock(128)
|
102 |
+
self.resblock16 = ResidualBlock(128)
|
103 |
+
self.resblock17 = ResidualBlock(128)
|
104 |
+
self.resblock18 = ResidualBlock(128)
|
105 |
+
self.resblock19 = ResidualBlock(128)
|
106 |
+
self.resblock20 = ResidualBlock(128)
|
107 |
+
|
108 |
+
# last convs
|
109 |
+
self.conv2 = nn.Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
110 |
+
self.bn2 = nn.BatchNorm2d(64)
|
111 |
+
self.relu2 = nn.ReLU(inplace=True)
|
112 |
+
|
113 |
+
self.conv3 = nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
114 |
+
self.bn3 = nn.BatchNorm2d(64)
|
115 |
+
self.relu3 = nn.ReLU(inplace=True)
|
116 |
+
|
117 |
+
# final conv: output 4 channels
|
118 |
+
self.conv_final = nn.Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0))
|
119 |
+
|
120 |
+
# initialize weights
|
121 |
+
self._initialize_weights()
|
122 |
+
|
123 |
+
def _initialize_weights(self):
|
124 |
+
for m in self.modules():
|
125 |
+
if isinstance(m, nn.Conv2d):
|
126 |
+
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
|
127 |
+
if m.bias is not None:
|
128 |
+
nn.init.constant_(m.bias, 0)
|
129 |
+
elif isinstance(m, nn.BatchNorm2d):
|
130 |
+
nn.init.constant_(m.weight, 1)
|
131 |
+
nn.init.constant_(m.bias, 0)
|
132 |
+
elif isinstance(m, nn.Linear):
|
133 |
+
nn.init.normal_(m.weight, 0, 0.01)
|
134 |
+
nn.init.constant_(m.bias, 0)
|
135 |
+
|
136 |
+
# initialize final conv weights to 0: 流行りのzero conv
|
137 |
+
nn.init.constant_(self.conv_final.weight, 0)
|
138 |
+
|
139 |
+
def forward(self, x):
|
140 |
+
inp = x
|
141 |
+
|
142 |
+
x = self.conv1(x)
|
143 |
+
x = self.bn1(x)
|
144 |
+
x = self.relu1(x)
|
145 |
+
|
146 |
+
# いくつかのresblockを通した��に、residualを足すことで精度向上と学習速度向上が見込めるはず
|
147 |
+
residual = x
|
148 |
+
x = self.resblock1(x)
|
149 |
+
x = self.resblock2(x)
|
150 |
+
x = self.resblock3(x)
|
151 |
+
x = self.resblock4(x)
|
152 |
+
x = x + residual
|
153 |
+
residual = x
|
154 |
+
x = self.resblock5(x)
|
155 |
+
x = self.resblock6(x)
|
156 |
+
x = self.resblock7(x)
|
157 |
+
x = self.resblock8(x)
|
158 |
+
x = x + residual
|
159 |
+
residual = x
|
160 |
+
x = self.resblock9(x)
|
161 |
+
x = self.resblock10(x)
|
162 |
+
x = self.resblock11(x)
|
163 |
+
x = self.resblock12(x)
|
164 |
+
x = x + residual
|
165 |
+
residual = x
|
166 |
+
x = self.resblock13(x)
|
167 |
+
x = self.resblock14(x)
|
168 |
+
x = self.resblock15(x)
|
169 |
+
x = self.resblock16(x)
|
170 |
+
x = x + residual
|
171 |
+
residual = x
|
172 |
+
x = self.resblock17(x)
|
173 |
+
x = self.resblock18(x)
|
174 |
+
x = self.resblock19(x)
|
175 |
+
x = self.resblock20(x)
|
176 |
+
x = x + residual
|
177 |
+
|
178 |
+
x = self.conv2(x)
|
179 |
+
x = self.bn2(x)
|
180 |
+
x = self.relu2(x)
|
181 |
+
x = self.conv3(x)
|
182 |
+
x = self.bn3(x)
|
183 |
+
|
184 |
+
# ここにreluを入れないほうがいい気がする
|
185 |
+
|
186 |
+
x = self.conv_final(x)
|
187 |
+
|
188 |
+
# network estimates the difference between the input and the output
|
189 |
+
x = x + inp
|
190 |
+
|
191 |
+
return x
|
192 |
+
|
193 |
+
def support_latents(self) -> bool:
|
194 |
+
return False
|
195 |
+
|
196 |
+
def upscale(
|
197 |
+
self,
|
198 |
+
vae: AutoencoderKL,
|
199 |
+
lowreso_images: List[Image.Image],
|
200 |
+
lowreso_latents: torch.Tensor,
|
201 |
+
dtype: torch.dtype,
|
202 |
+
width: int,
|
203 |
+
height: int,
|
204 |
+
batch_size: int = 1,
|
205 |
+
vae_batch_size: int = 1,
|
206 |
+
):
|
207 |
+
# assertion
|
208 |
+
assert lowreso_images is not None, "Upscaler requires lowreso image"
|
209 |
+
|
210 |
+
# make upsampled image with lanczos4
|
211 |
+
upsampled_images = []
|
212 |
+
for lowreso_image in lowreso_images:
|
213 |
+
upsampled_image = np.array(lowreso_image.resize((width, height), Image.LANCZOS))
|
214 |
+
upsampled_images.append(upsampled_image)
|
215 |
+
|
216 |
+
# convert to tensor: this tensor is too large to be converted to cuda
|
217 |
+
upsampled_images = [torch.from_numpy(upsampled_image).permute(2, 0, 1).float() for upsampled_image in upsampled_images]
|
218 |
+
upsampled_images = torch.stack(upsampled_images, dim=0)
|
219 |
+
upsampled_images = upsampled_images.to(dtype)
|
220 |
+
|
221 |
+
# normalize to [-1, 1]
|
222 |
+
upsampled_images = upsampled_images / 127.5 - 1.0
|
223 |
+
|
224 |
+
# convert upsample images to latents with batch size
|
225 |
+
# logger.info("Encoding upsampled (LANCZOS4) images...")
|
226 |
+
upsampled_latents = []
|
227 |
+
for i in tqdm(range(0, upsampled_images.shape[0], vae_batch_size)):
|
228 |
+
batch = upsampled_images[i : i + vae_batch_size].to(vae.device)
|
229 |
+
with torch.no_grad():
|
230 |
+
batch = vae.encode(batch).latent_dist.sample()
|
231 |
+
upsampled_latents.append(batch)
|
232 |
+
|
233 |
+
upsampled_latents = torch.cat(upsampled_latents, dim=0)
|
234 |
+
|
235 |
+
# upscale (refine) latents with this model with batch size
|
236 |
+
logger.info("Upscaling latents...")
|
237 |
+
upscaled_latents = []
|
238 |
+
for i in range(0, upsampled_latents.shape[0], batch_size):
|
239 |
+
with torch.no_grad():
|
240 |
+
upscaled_latents.append(self.forward(upsampled_latents[i : i + batch_size]))
|
241 |
+
upscaled_latents = torch.cat(upscaled_latents, dim=0)
|
242 |
+
|
243 |
+
return upscaled_latents * 0.18215
|
244 |
+
|
245 |
+
|
246 |
+
# external interface: returns a model
|
247 |
+
def create_upscaler(**kwargs):
|
248 |
+
weights = kwargs["weights"]
|
249 |
+
model = Upscaler()
|
250 |
+
|
251 |
+
logger.info(f"Loading weights from {weights}...")
|
252 |
+
if os.path.splitext(weights)[1] == ".safetensors":
|
253 |
+
from safetensors.torch import load_file
|
254 |
+
|
255 |
+
sd = load_file(weights)
|
256 |
+
else:
|
257 |
+
sd = torch.load(weights, map_location=torch.device("cpu"))
|
258 |
+
model.load_state_dict(sd)
|
259 |
+
return model
|
260 |
+
|
261 |
+
|
262 |
+
# another interface: upscale images with a model for given images from command line
|
263 |
+
def upscale_images(args: argparse.Namespace):
|
264 |
+
DEVICE = get_preferred_device()
|
265 |
+
us_dtype = torch.float16 # TODO: support fp32/bf16
|
266 |
+
os.makedirs(args.output_dir, exist_ok=True)
|
267 |
+
|
268 |
+
# load VAE with Diffusers
|
269 |
+
assert args.vae_path is not None, "VAE path is required"
|
270 |
+
logger.info(f"Loading VAE from {args.vae_path}...")
|
271 |
+
vae = AutoencoderKL.from_pretrained(args.vae_path, subfolder="vae")
|
272 |
+
vae.to(DEVICE, dtype=us_dtype)
|
273 |
+
|
274 |
+
# prepare model
|
275 |
+
logger.info("Preparing model...")
|
276 |
+
upscaler: Upscaler = create_upscaler(weights=args.weights)
|
277 |
+
# logger.info("Loading weights from", args.weights)
|
278 |
+
# upscaler.load_state_dict(torch.load(args.weights))
|
279 |
+
upscaler.eval()
|
280 |
+
upscaler.to(DEVICE, dtype=us_dtype)
|
281 |
+
|
282 |
+
# load images
|
283 |
+
image_paths = glob.glob(args.image_pattern)
|
284 |
+
images = []
|
285 |
+
for image_path in image_paths:
|
286 |
+
image = Image.open(image_path)
|
287 |
+
image = image.convert("RGB")
|
288 |
+
|
289 |
+
# make divisible by 8
|
290 |
+
width = image.width
|
291 |
+
height = image.height
|
292 |
+
if width % 8 != 0:
|
293 |
+
width = width - (width % 8)
|
294 |
+
if height % 8 != 0:
|
295 |
+
height = height - (height % 8)
|
296 |
+
if width != image.width or height != image.height:
|
297 |
+
image = image.crop((0, 0, width, height))
|
298 |
+
|
299 |
+
images.append(image)
|
300 |
+
|
301 |
+
# debug output
|
302 |
+
if args.debug:
|
303 |
+
for image, image_path in zip(images, image_paths):
|
304 |
+
image_debug = image.resize((image.width * 2, image.height * 2), Image.LANCZOS)
|
305 |
+
|
306 |
+
basename = os.path.basename(image_path)
|
307 |
+
basename_wo_ext, ext = os.path.splitext(basename)
|
308 |
+
dest_file_name = os.path.join(args.output_dir, f"{basename_wo_ext}_lanczos4{ext}")
|
309 |
+
image_debug.save(dest_file_name)
|
310 |
+
|
311 |
+
# upscale
|
312 |
+
logger.info("Upscaling...")
|
313 |
+
upscaled_latents = upscaler.upscale(
|
314 |
+
vae, images, None, us_dtype, width * 2, height * 2, batch_size=args.batch_size, vae_batch_size=args.vae_batch_size
|
315 |
+
)
|
316 |
+
upscaled_latents /= 0.18215
|
317 |
+
|
318 |
+
# decode with batch
|
319 |
+
logger.info("Decoding...")
|
320 |
+
upscaled_images = []
|
321 |
+
for i in tqdm(range(0, upscaled_latents.shape[0], args.vae_batch_size)):
|
322 |
+
with torch.no_grad():
|
323 |
+
batch = vae.decode(upscaled_latents[i : i + args.vae_batch_size]).sample
|
324 |
+
batch = batch.to("cpu")
|
325 |
+
upscaled_images.append(batch)
|
326 |
+
upscaled_images = torch.cat(upscaled_images, dim=0)
|
327 |
+
|
328 |
+
# tensor to numpy
|
329 |
+
upscaled_images = upscaled_images.permute(0, 2, 3, 1).numpy()
|
330 |
+
upscaled_images = (upscaled_images + 1.0) * 127.5
|
331 |
+
upscaled_images = upscaled_images.clip(0, 255).astype(np.uint8)
|
332 |
+
|
333 |
+
upscaled_images = upscaled_images[..., ::-1]
|
334 |
+
|
335 |
+
# save images
|
336 |
+
for i, image in enumerate(upscaled_images):
|
337 |
+
basename = os.path.basename(image_paths[i])
|
338 |
+
basename_wo_ext, ext = os.path.splitext(basename)
|
339 |
+
dest_file_name = os.path.join(args.output_dir, f"{basename_wo_ext}_upscaled{ext}")
|
340 |
+
cv2.imwrite(dest_file_name, image)
|
341 |
+
|
342 |
+
|
343 |
+
if __name__ == "__main__":
|
344 |
+
parser = argparse.ArgumentParser()
|
345 |
+
parser.add_argument("--vae_path", type=str, default=None, help="VAE path")
|
346 |
+
parser.add_argument("--weights", type=str, default=None, help="Weights path")
|
347 |
+
parser.add_argument("--image_pattern", type=str, default=None, help="Image pattern")
|
348 |
+
parser.add_argument("--output_dir", type=str, default=".", help="Output directory")
|
349 |
+
parser.add_argument("--batch_size", type=int, default=4, help="Batch size")
|
350 |
+
parser.add_argument("--vae_batch_size", type=int, default=1, help="VAE batch size")
|
351 |
+
parser.add_argument("--debug", action="store_true", help="Debug mode")
|
352 |
+
|
353 |
+
args = parser.parse_args()
|
354 |
+
upscale_images(args)
|