custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/freeinit.py

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# Code has been modified from https://github.com/TianxingWu/FreeInit

import torch
import torch.fft as fft
import math


class FreeInitFilter:
    GAUSSIAN = "gaussian"
    IDEAL = "ideal"
    BOX = "box"
    BUTTERWORTH = "butterworth"

    LIST = [GAUSSIAN, BUTTERWORTH, IDEAL, BOX]


def freq_mix_3d(x: torch.Tensor, noise: torch.Tensor, LPF: torch.Tensor):
    """
    Noise reinitialization.

    Args:
        x: diffused latent
        noise: randomly sampled noise
        LPF: low pass filter
    """
    noise = noise.to(dtype=x.dtype, device=x.device)
    LPF = LPF.to(dtype=x.dtype, device=x.device)
    # FFT
    x_freq = fft.fftn(x, dim=(-4, -2, -1))
    x_freq = fft.fftshift(x_freq, dim=(-4, -2, -1))
    noise_freq = fft.fftn(noise, dim=(-4, -2, -1))
    noise_freq = fft.fftshift(noise_freq, dim=(-4, -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=(-4, -2, -1))
    x_mixed = fft.ifftn(x_freq_mixed, dim=(-4, -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 (T, C, 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 == FreeInitFilter.GAUSSIAN:
        return gaussian_low_pass_filter(shape=shape, d_s=d_s, d_t=d_t).to(device)
    elif filter_type == FreeInitFilter.IDEAL:
        return ideal_low_pass_filter(shape=shape, d_s=d_s, d_t=d_t).to(device)
    elif filter_type == FreeInitFilter.BOX:
        return box_low_pass_filter(shape=shape, d_s=d_s, d_t=d_t).to(device)
    elif filter_type == FreeInitFilter.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[-4], 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[-4], 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[-4], 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[-4], 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