Spaces:
Running
on
Zero
Running
on
Zero
File size: 12,707 Bytes
2a50f45 |
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 |
import torch
import torch.fft as fft
import math
def get_longpath(BOX_SIZE_H=0.3, BOX_SIZE_W=0.3, input_mode=4):
if input_mode == 1:
# mode 1
inputs = [[0, 0, 0 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W],
[7, 1-BOX_SIZE_H, 1, (1-BOX_SIZE_W) / 15 * 7, (1-BOX_SIZE_W) / 15 * 7 + BOX_SIZE_W],
[8, 1-BOX_SIZE_H, 1, (1-BOX_SIZE_W) / 15 * 8, (1-BOX_SIZE_W) / 15 * 8 + BOX_SIZE_W],
[15, 0, 0 + BOX_SIZE_H, 1-BOX_SIZE_W, 1],
[16, 0.1, 0.1 + BOX_SIZE_H, 0.9-BOX_SIZE_W, 0.9],
[25, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W],
[31, 0.9-BOX_SIZE_H, 0.9, 0.1, 0.1 + BOX_SIZE_W],
[32, 1-BOX_SIZE_H, 1, 0, 0 + BOX_SIZE_W],
[39, 0, 0 + BOX_SIZE_H, (1-BOX_SIZE_W) / 15 * 7, (1-BOX_SIZE_W) / 15 * 7 + BOX_SIZE_W],
[40, 0, 0 + BOX_SIZE_H, (1-BOX_SIZE_W) / 15 * 8, (1-BOX_SIZE_W) / 15 * 8 + BOX_SIZE_W],
[47, 1-BOX_SIZE_H, 1, 1-BOX_SIZE_W, 1],
[48, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9],
[57, 0.9-BOX_SIZE_H, 0.9, 0.1, 0.1 + BOX_SIZE_W],
[63, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W]]
elif input_mode == 2:
# mode 2
inputs = [[0, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W],
[6, 0.9-BOX_SIZE_H, 0.9, 0.1, 0.1 + BOX_SIZE_W],
[15, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9],
[16, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9],
[22, 0.1, 0.1 + BOX_SIZE_H, 0.9-BOX_SIZE_W, 0.9],
[31, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W],
[32, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W],
[41, 0.1, 0.1 + BOX_SIZE_H, 0.9-BOX_SIZE_W, 0.9],
[47, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9],
[48, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9],
[57, 0.9-BOX_SIZE_H, 0.9, 0.1, 0.1 + BOX_SIZE_W],
[63, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W]]
elif input_mode == 3:
# mode 3 ||||
inputs = [[0, 0, 0 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W],
[9, 1-BOX_SIZE_H, 1, (1-BOX_SIZE_W) / 7 * 1, (1-BOX_SIZE_W) / 7 * 1 + BOX_SIZE_W],
[18, 0, 0 + BOX_SIZE_H, (1-BOX_SIZE_W) / 7 * 2, (1-BOX_SIZE_W) / 7 * 2 + BOX_SIZE_W],
[27, 1-BOX_SIZE_H, 1, (1-BOX_SIZE_W) / 7 * 3, (1-BOX_SIZE_W) / 7 * 3 + BOX_SIZE_W],
[36, 0, 0 + BOX_SIZE_H, (1-BOX_SIZE_W) / 7 * 4, (1-BOX_SIZE_W) / 7 * 4 + BOX_SIZE_W],
[45, 1-BOX_SIZE_H, 1, (1-BOX_SIZE_W) / 7 * 5, (1-BOX_SIZE_W) / 7 * 5 + BOX_SIZE_W],
[54, 0, 0 + BOX_SIZE_H, (1-BOX_SIZE_W) / 7 * 6, (1-BOX_SIZE_W) / 7 * 6 + BOX_SIZE_W],
[63, 1-BOX_SIZE_H, 1, 1-BOX_SIZE_W, 1]]
elif input_mode == 4:
# mode 4 ----
inputs = [[0, 0, 0 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W],
[9, (1-BOX_SIZE_H) / 7 * 1, (1-BOX_SIZE_H) / 7 * 1 + BOX_SIZE_H, 1-BOX_SIZE_W, 1],
[18, (1-BOX_SIZE_H) / 7 * 2, (1-BOX_SIZE_H) / 7 * 2 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W],
[27, (1-BOX_SIZE_H) / 7 * 3, (1-BOX_SIZE_H) / 7 * 3 + BOX_SIZE_H, 1-BOX_SIZE_W, 1],
[36, (1-BOX_SIZE_H) / 7 * 4, (1-BOX_SIZE_H) / 7 * 4 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W],
[45, (1-BOX_SIZE_H) / 7 * 5, (1-BOX_SIZE_H) / 7 * 5 + BOX_SIZE_H, 1-BOX_SIZE_W, 1],
[54, (1-BOX_SIZE_H) / 7 * 6, (1-BOX_SIZE_H) / 7 * 6 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W],
[63, 1-BOX_SIZE_H, 1, 1-BOX_SIZE_W, 1]]
else:
print('error')
exit()
outputs = plan_path(inputs)
# print(outputs)
return outputs
def get_path(BOX_SIZE_H=0.3, BOX_SIZE_W=0.3, input_mode=0):
if input_mode == 0:
# \ d
inputs = [[0, 0, 0 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W], [15, 1-BOX_SIZE_H, 1, 1-BOX_SIZE_W, 1]]
elif input_mode == 1:
# / re d
inputs = [[0, 0, 0 + BOX_SIZE_H, 1-BOX_SIZE_W, 1], [15, 1-BOX_SIZE_H, 1, 0, 0 + BOX_SIZE_W]]
elif input_mode == 2:
# L
inputs = [[0, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W], [6, 0.9-BOX_SIZE_H, 0.9, 0.1, 0.1 + BOX_SIZE_W], [15, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9]]
elif input_mode == 3:
# re L
inputs = [[0, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9], [6, 0.1, 0.1 + BOX_SIZE_H, 0.9-BOX_SIZE_W, 0.9], [15, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W]]
elif input_mode == 4:
# V
inputs = [[0, 0, 0 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W], [7, 1-BOX_SIZE_H, 1, (1-BOX_SIZE_W) / 15 * 7, (1-BOX_SIZE_W) / 15 * 7 + BOX_SIZE_W], [8, 1-BOX_SIZE_H, 1, (1-BOX_SIZE_W) / 15 * 8, (1-BOX_SIZE_W) / 15 * 8 + BOX_SIZE_W], [15, 0, 0 + BOX_SIZE_H, 1-BOX_SIZE_W, 1]]
elif input_mode == 5:
# re V
inputs = [[0, 1-BOX_SIZE_H, 1, 1-BOX_SIZE_W, 1], [7, 0, 0 + BOX_SIZE_H, (1-BOX_SIZE_W) / 15 * 8, (1-BOX_SIZE_W) / 15 * 8 + BOX_SIZE_W], [8, 0, 0 + BOX_SIZE_H, (1-BOX_SIZE_W) / 15 * 7, (1-BOX_SIZE_W) / 15 * 7 + BOX_SIZE_W], [15, 1-BOX_SIZE_H, 1, 0, 0 + BOX_SIZE_W]]
elif input_mode == 6:
# -- goback
inputs = [[0, 0.35, 0.35 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W], [7, 0.35, 0.35 + BOX_SIZE_H, 0.9-BOX_SIZE_W, 0.9], [8, 0.35, 0.35 + BOX_SIZE_H, 0.9-BOX_SIZE_W, 0.9], [15, 0.35, 0.35 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W]]
elif input_mode == 7:
# tri
inputs = [[0, 0.1, 0.1 + BOX_SIZE_H, 0.35, 0.35 + BOX_SIZE_W], [5, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9], [10, 0.9-BOX_SIZE_H, 0.9, 0.1, 0.1 + BOX_SIZE_W], [15, 0.1, 0.1 + BOX_SIZE_H, 0.35, 0.35 + BOX_SIZE_W]]
outputs = plan_path(inputs)
return outputs
# input: List([frame, h_start, h_end, w_start, w_end], ...)
# return: List([h_start, h_end, w_start, w_end], ...)
def plan_path(input, video_length = 16):
len_input = len(input)
path = [input[0][1:]]
for i in range(1, len_input):
start = input[i-1]
end = input[i]
start_frame = start[0]
end_frame = end[0]
h_start_change = (end[1] - start[1]) / (end_frame - start_frame)
h_end_change = (end[2] - start[2]) / (end_frame - start_frame)
w_start_change = (end[3] - start[3]) / (end_frame - start_frame)
w_end_change = (end[4] - start[4]) / (end_frame - start_frame)
for j in range(start_frame+1, end_frame + 1):
increase_frame = j - start_frame
path += [[increase_frame * h_start_change + start[1], increase_frame * h_end_change + start[2], increase_frame * w_start_change + start[3], increase_frame * w_end_change + start[4]]]
if input[0][0] > 0:
h_change = path[1][0] - path[0][0]
w_change = path[1][2] - path[0][2]
for i in range(input[0][0]):
path = [path[0][0] - h_change, path[0][1] - h_change, path[0][2] - w_change, path[0][3] - w_change] + path
if input[-1][0] < video_length - 1:
h_change = path[-1][0] - path[-2][0]
w_change = path[-1][2] - path[-2][2]
for i in range(video_length - 1 - input[-1][0]):
path = path + [path[-1][0] + h_change, path[-1][1] + h_change, path[-1][2] + w_change, path[-1][3] + w_change]
return path
def gaussian_2d(x=0, y=0, mx=0, my=0, sx=1, sy=1):
""" 2d Gaussian weight function
"""
gaussian_map = (
1
/ (2 * math.pi * sx * sy)
* torch.exp(-((x - mx) ** 2 / (2 * sx**2) + (y - my) ** 2 / (2 * sy**2)))
)
gaussian_map.div_(gaussian_map.max())
return gaussian_map
def gaussian_weight(height=32, width=32, KERNEL_DIVISION=3.0):
x = torch.linspace(0, height, height)
y = torch.linspace(0, width, width)
x, y = torch.meshgrid(x, y, indexing="ij")
noise_patch = (
gaussian_2d(
x,
y,
mx=int(height / 2),
my=int(width / 2),
sx=float(height / KERNEL_DIVISION),
sy=float(width / KERNEL_DIVISION),
)
).half()
return noise_patch
def freq_mix_3d(x, noise, LPF):
"""
Noise reinitialization.
Args:
x: diffused latent
noise: randomly sampled noise
LPF: low pass filter
"""
# FFT
x_freq = fft.fftn(x, dim=(-3, -2, -1))
x_freq = fft.fftshift(x_freq, dim=(-3, -2, -1))
noise_freq = fft.fftn(noise, dim=(-3, -2, -1))
noise_freq = fft.fftshift(noise_freq, dim=(-3, -2, -1))
# frequency mix
HPF = 1 - LPF
x_freq_low = x_freq * LPF
noise_freq_high = noise_freq * HPF
x_freq_mixed = x_freq_low + noise_freq_high # mix in freq domain
# IFFT
x_freq_mixed = fft.ifftshift(x_freq_mixed, dim=(-3, -2, -1))
x_mixed = fft.ifftn(x_freq_mixed, dim=(-3, -2, -1)).real
return x_mixed
def get_freq_filter(shape, device, filter_type, n, d_s, d_t):
"""
Form the frequency filter for noise reinitialization.
Args:
shape: shape of latent (B, C, T, H, W)
filter_type: type of the freq filter
n: (only for butterworth) order of the filter, larger n ~ ideal, smaller n ~ gaussian
d_s: normalized stop frequency for spatial dimensions (0.0-1.0)
d_t: normalized stop frequency for temporal dimension (0.0-1.0)
"""
if filter_type == "gaussian":
return gaussian_low_pass_filter(shape=shape, d_s=d_s, d_t=d_t).to(device)
elif filter_type == "ideal":
return ideal_low_pass_filter(shape=shape, d_s=d_s, d_t=d_t).to(device)
elif filter_type == "box":
return box_low_pass_filter(shape=shape, d_s=d_s, d_t=d_t).to(device)
elif filter_type == "butterworth":
return butterworth_low_pass_filter(shape=shape, n=n, d_s=d_s, d_t=d_t).to(device)
else:
raise NotImplementedError
def gaussian_low_pass_filter(shape, d_s=0.25, d_t=0.25):
"""
Compute the gaussian low pass filter mask.
Args:
shape: shape of the filter (volume)
d_s: normalized stop frequency for spatial dimensions (0.0-1.0)
d_t: normalized stop frequency for temporal dimension (0.0-1.0)
"""
T, H, W = shape[-3], shape[-2], shape[-1]
mask = torch.zeros(shape)
if d_s==0 or d_t==0:
return mask
for t in range(T):
for h in range(H):
for w in range(W):
d_square = (((d_s/d_t)*(2*t/T-1))**2 + (2*h/H-1)**2 + (2*w/W-1)**2)
mask[..., t,h,w] = math.exp(-1/(2*d_s**2) * d_square)
return mask
def butterworth_low_pass_filter(shape, n=4, d_s=0.25, d_t=0.25):
"""
Compute the butterworth low pass filter mask.
Args:
shape: shape of the filter (volume)
n: order of the filter, larger n ~ ideal, smaller n ~ gaussian
d_s: normalized stop frequency for spatial dimensions (0.0-1.0)
d_t: normalized stop frequency for temporal dimension (0.0-1.0)
"""
T, H, W = shape[-3], shape[-2], shape[-1]
mask = torch.zeros(shape)
if d_s==0 or d_t==0:
return mask
for t in range(T):
for h in range(H):
for w in range(W):
d_square = (((d_s/d_t)*(2*t/T-1))**2 + (2*h/H-1)**2 + (2*w/W-1)**2)
mask[..., t,h,w] = 1 / (1 + (d_square / d_s**2)**n)
return mask
def ideal_low_pass_filter(shape, d_s=0.25, d_t=0.25):
"""
Compute the ideal low pass filter mask.
Args:
shape: shape of the filter (volume)
d_s: normalized stop frequency for spatial dimensions (0.0-1.0)
d_t: normalized stop frequency for temporal dimension (0.0-1.0)
"""
T, H, W = shape[-3], shape[-2], shape[-1]
mask = torch.zeros(shape)
if d_s==0 or d_t==0:
return mask
for t in range(T):
for h in range(H):
for w in range(W):
d_square = (((d_s/d_t)*(2*t/T-1))**2 + (2*h/H-1)**2 + (2*w/W-1)**2)
mask[..., t,h,w] = 1 if d_square <= d_s*2 else 0
return mask
def box_low_pass_filter(shape, d_s=0.25, d_t=0.25):
"""
Compute the ideal low pass filter mask (approximated version).
Args:
shape: shape of the filter (volume)
d_s: normalized stop frequency for spatial dimensions (0.0-1.0)
d_t: normalized stop frequency for temporal dimension (0.0-1.0)
"""
T, H, W = shape[-3], shape[-2], shape[-1]
mask = torch.zeros(shape)
if d_s==0 or d_t==0:
return mask
threshold_s = round(int(H // 2) * d_s)
threshold_t = round(T // 2 * d_t)
cframe, crow, ccol = T // 2, H // 2, W //2
mask[..., cframe - threshold_t:cframe + threshold_t, crow - threshold_s:crow + threshold_s, ccol - threshold_s:ccol + threshold_s] = 1.0
return mask
|