File size: 16,078 Bytes
a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b a0bcaae b2af35b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 |
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from socket import has_dualstack_ipv6
import sys
import copy
import traceback
import math
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import torch
import torch.fft
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.cm
import dnnlib
from torch_utils.ops import upfirdn2d
import legacy # pylint: disable=import-error
# ----------------------------------------------------------------------------
class CapturedException(Exception):
def __init__(self, msg=None):
if msg is None:
_type, value, _traceback = sys.exc_info()
assert value is not None
if isinstance(value, CapturedException):
msg = str(value)
else:
msg = traceback.format_exc()
assert isinstance(msg, str)
super().__init__(msg)
# ----------------------------------------------------------------------------
class CaptureSuccess(Exception):
def __init__(self, out):
super().__init__()
self.out = out
# ----------------------------------------------------------------------------
def add_watermark_np(input_image_array, watermark_text="AI Generated"):
image = Image.fromarray(np.uint8(input_image_array)).convert("RGBA")
# Initialize text image
txt = Image.new('RGBA', image.size, (255, 255, 255, 0))
font = ImageFont.truetype('arial.ttf', round(25/512*image.size[0]))
d = ImageDraw.Draw(txt)
text_width, text_height = font.getsize(watermark_text)
text_position = (image.size[0] - text_width -
10, image.size[1] - text_height - 10)
# white color with the alpha channel set to semi-transparent
text_color = (255, 255, 255, 128)
# Draw the text onto the text canvas
d.text(text_position, watermark_text, font=font, fill=text_color)
# Combine the image with the watermark
watermarked = Image.alpha_composite(image, txt)
watermarked_array = np.array(watermarked)
return watermarked_array
# ----------------------------------------------------------------------------
class Renderer:
def __init__(self, disable_timing=False):
self._device = torch.device('cuda')
self._pkl_data = dict() # {pkl: dict | CapturedException, ...}
self._networks = dict() # {cache_key: torch.nn.Module, ...}
self._pinned_bufs = dict() # {(shape, dtype): torch.Tensor, ...}
self._cmaps = dict() # {name: torch.Tensor, ...}
self._is_timing = False
if not disable_timing:
self._start_event = torch.cuda.Event(enable_timing=True)
self._end_event = torch.cuda.Event(enable_timing=True)
self._disable_timing = disable_timing
self._net_layers = dict() # {cache_key: [dnnlib.EasyDict, ...], ...}
def render(self, **args):
if self._disable_timing:
self._is_timing = False
else:
self._start_event.record(torch.cuda.current_stream(self._device))
self._is_timing = True
res = dnnlib.EasyDict()
try:
init_net = False
if not hasattr(self, 'G'):
init_net = True
if hasattr(self, 'pkl'):
if self.pkl != args['pkl']:
init_net = True
if hasattr(self, 'w0_seed'):
if self.w0_seed != args['w0_seed']:
init_net = True
if hasattr(self, 'w_plus'):
if self.w_plus != args['w_plus']:
init_net = True
if args['reset_w']:
init_net = True
res.init_net = init_net
if init_net:
self.init_network(res, **args)
self._render_drag_impl(res, **args)
except:
res.error = CapturedException()
if not self._disable_timing:
self._end_event.record(torch.cuda.current_stream(self._device))
if 'image' in res:
res.image = self.to_cpu(res.image).detach().numpy()
res.image = add_watermark_np(res.image, 'AI Generated')
if 'stats' in res:
res.stats = self.to_cpu(res.stats).detach().numpy()
if 'error' in res:
res.error = str(res.error)
# if 'stop' in res and res.stop:
if self._is_timing and not self._disable_timing:
self._end_event.synchronize()
res.render_time = self._start_event.elapsed_time(
self._end_event) * 1e-3
self._is_timing = False
return res
def get_network(self, pkl, key, **tweak_kwargs):
data = self._pkl_data.get(pkl, None)
if data is None:
print(f'Loading "{pkl}"... ', end='', flush=True)
try:
with dnnlib.util.open_url(pkl, verbose=False) as f:
data = legacy.load_network_pkl(f)
print('Done.')
except:
data = CapturedException()
print('Failed!')
self._pkl_data[pkl] = data
self._ignore_timing()
if isinstance(data, CapturedException):
raise data
orig_net = data[key]
cache_key = (orig_net, self._device, tuple(
sorted(tweak_kwargs.items())))
net = self._networks.get(cache_key, None)
if net is None:
try:
if 'stylegan2' in pkl:
from training.networks_stylegan2 import Generator
elif 'stylegan3' in pkl:
from training.networks_stylegan3 import Generator
elif 'stylegan_human' in pkl:
from stylegan_human.training_scripts.sg2.training.networks import Generator
else:
raise NameError('Cannot infer model type from pkl name!')
print(data[key].init_args)
print(data[key].init_kwargs)
if 'stylegan_human' in pkl:
net = Generator(
*data[key].init_args, **data[key].init_kwargs, square=False, padding=True)
else:
net = Generator(*data[key].init_args,
**data[key].init_kwargs)
net.load_state_dict(data[key].state_dict())
net.to(self._device)
except:
net = CapturedException()
self._networks[cache_key] = net
self._ignore_timing()
if isinstance(net, CapturedException):
raise net
return net
def _get_pinned_buf(self, ref):
key = (tuple(ref.shape), ref.dtype)
buf = self._pinned_bufs.get(key, None)
if buf is None:
buf = torch.empty(ref.shape, dtype=ref.dtype).pin_memory()
self._pinned_bufs[key] = buf
return buf
def to_device(self, buf):
return self._get_pinned_buf(buf).copy_(buf).to(self._device)
def to_cpu(self, buf):
return self._get_pinned_buf(buf).copy_(buf).clone()
def _ignore_timing(self):
self._is_timing = False
def _apply_cmap(self, x, name='viridis'):
cmap = self._cmaps.get(name, None)
if cmap is None:
cmap = matplotlib.cm.get_cmap(name)
cmap = cmap(np.linspace(0, 1, num=1024), bytes=True)[:, :3]
cmap = self.to_device(torch.from_numpy(cmap))
self._cmaps[name] = cmap
hi = cmap.shape[0] - 1
x = (x * hi + 0.5).clamp(0, hi).to(torch.int64)
x = torch.nn.functional.embedding(x, cmap)
return x
def init_network(self, res,
pkl=None,
w0_seed=0,
w_load=None,
w_plus=True,
noise_mode='const',
trunc_psi=0.7,
trunc_cutoff=None,
input_transform=None,
lr=0.001,
**kwargs
):
# Dig up network details.
self.pkl = pkl
G = self.get_network(pkl, 'G_ema')
self.G = G
res.img_resolution = G.img_resolution
res.num_ws = G.num_ws
res.has_noise = any('noise_const' in name for name,
_buf in G.synthesis.named_buffers())
res.has_input_transform = (
hasattr(G.synthesis, 'input') and hasattr(G.synthesis.input, 'transform'))
# Set input transform.
if res.has_input_transform:
m = np.eye(3)
try:
if input_transform is not None:
m = np.linalg.inv(np.asarray(input_transform))
except np.linalg.LinAlgError:
res.error = CapturedException()
G.synthesis.input.transform.copy_(torch.from_numpy(m))
# Generate random latents.
self.w0_seed = w0_seed
self.w_load = w_load
if self.w_load is None:
# Generate random latents.
z = torch.from_numpy(np.random.RandomState(
w0_seed).randn(1, 512)).to(self._device).float()
# Run mapping network.
label = torch.zeros([1, G.c_dim], device=self._device)
w = G.mapping(z, label, truncation_psi=trunc_psi,
truncation_cutoff=trunc_cutoff)
else:
w = self.w_load.clone().to(self._device)
self.w0 = w.detach().clone()
self.w_plus = w_plus
if w_plus:
self.w = w.detach()
else:
self.w = w[:, 0, :].detach()
self.w.requires_grad = True
self.w_optim = torch.optim.Adam([self.w], lr=lr)
self.feat_refs = None
self.points0_pt = None
def update_lr(self, lr):
del self.w_optim
self.w_optim = torch.optim.Adam([self.w], lr=lr)
print(f'Rebuild optimizer with lr: {lr}')
print(' Remain feat_refs and points0_pt')
def _render_drag_impl(self, res,
points=[],
targets=[],
mask=None,
lambda_mask=10,
reg=0,
feature_idx=5,
r1=3,
r2=12,
random_seed=0,
noise_mode='const',
trunc_psi=0.7,
force_fp32=False,
layer_name=None,
sel_channels=3,
base_channel=0,
img_scale_db=0,
img_normalize=False,
untransform=False,
is_drag=False,
reset=False,
to_pil=False,
**kwargs
):
G = self.G
ws = self.w
if ws.dim() == 2:
ws = ws.unsqueeze(1).repeat(1, 6, 1)
ws = torch.cat([ws[:, :6, :], self.w0[:, 6:, :]], dim=1)
if hasattr(self, 'points'):
if len(points) != len(self.points):
reset = True
if reset:
self.feat_refs = None
self.points0_pt = None
self.points = points
# Run synthesis network.
label = torch.zeros([1, G.c_dim], device=self._device)
img, feat = G(ws, label, truncation_psi=trunc_psi,
noise_mode=noise_mode, input_is_w=True, return_feature=True)
h, w = G.img_resolution, G.img_resolution
if is_drag:
X = torch.linspace(0, h, h)
Y = torch.linspace(0, w, w)
xx, yy = torch.meshgrid(X, Y)
feat_resize = F.interpolate(
feat[feature_idx], [h, w], mode='bilinear')
if self.feat_refs is None:
self.feat0_resize = F.interpolate(
feat[feature_idx].detach(), [h, w], mode='bilinear')
self.feat_refs = []
for point in points:
py, px = round(point[0]), round(point[1])
self.feat_refs.append(self.feat0_resize[:, :, py, px])
self.points0_pt = torch.Tensor(points).unsqueeze(
0).to(self._device) # 1, N, 2
# Point tracking with feature matching
with torch.no_grad():
for j, point in enumerate(points):
r = round(r2 / 512 * h)
up = max(point[0] - r, 0)
down = min(point[0] + r + 1, h)
left = max(point[1] - r, 0)
right = min(point[1] + r + 1, w)
feat_patch = feat_resize[:, :, up:down, left:right]
L2 = torch.linalg.norm(
feat_patch - self.feat_refs[j].reshape(1, -1, 1, 1), dim=1)
_, idx = torch.min(L2.view(1, -1), -1)
width = right - left
point = [idx.item() // width + up, idx.item() %
width + left]
points[j] = point
res.points = [[point[0], point[1]] for point in points]
# Motion supervision
loss_motion = 0
res.stop = True
for j, point in enumerate(points):
direction = torch.Tensor(
[targets[j][1] - point[1], targets[j][0] - point[0]])
if torch.linalg.norm(direction) > max(2 / 512 * h, 2):
res.stop = False
if torch.linalg.norm(direction) > 1:
distance = (
(xx.to(self._device) - point[0])**2 + (yy.to(self._device) - point[1])**2)**0.5
relis, reljs = torch.where(distance < round(r1 / 512 * h))
direction = direction / \
(torch.linalg.norm(direction) + 1e-7)
gridh = (relis-direction[1]) / (h-1) * 2 - 1
gridw = (reljs-direction[0]) / (w-1) * 2 - 1
grid = torch.stack(
[gridw, gridh], dim=-1).unsqueeze(0).unsqueeze(0)
target = F.grid_sample(
feat_resize.float(), grid, align_corners=True).squeeze(2)
loss_motion += F.l1_loss(
feat_resize[:, :, relis, reljs], target.detach())
loss = loss_motion
if mask is not None:
if mask.min() == 0 and mask.max() == 1:
mask_usq = mask.to(self._device).unsqueeze(0).unsqueeze(0)
loss_fix = F.l1_loss(
feat_resize * mask_usq, self.feat0_resize * mask_usq)
loss += lambda_mask * loss_fix
loss += reg * F.l1_loss(ws, self.w0) # latent code regularization
if not res.stop:
self.w_optim.zero_grad()
loss.backward()
self.w_optim.step()
# Scale and convert to uint8.
img = img[0]
if img_normalize:
img = img / img.norm(float('inf'),
dim=[1, 2], keepdim=True).clip(1e-8, 1e8)
img = img * (10 ** (img_scale_db / 20))
img = (img * 127.5 + 128).clamp(0,
255).to(torch.uint8).permute(1, 2, 0)
if to_pil:
from PIL import Image
img = img.cpu().numpy()
img = Image.fromarray(img)
res.image = img
# ----------------------------------------------------------------------------
|