| |
|
|
| import torch |
| import torch.fft as fft |
| import math |
|
|
|
|
| def freq_mix_3d(x, noise, LPF): |
| """ |
| Noise reinitialization. |
| |
| Args: |
| x: diffused latent |
| noise: randomly sampled noise |
| LPF: low pass filter |
| """ |
| |
| 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)) |
|
|
| |
| HPF = 1 - LPF |
| x_freq_low = x_freq * LPF |
| noise_freq_high = noise_freq * HPF |
| x_freq_mixed = x_freq_low + noise_freq_high |
|
|
| |
| 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 |
