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# Copyright (C) 2023 Deforum LLC
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, version 3 of the License.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
# Contact the authors: https://deforum.github.io/
import torch
from torch.nn.functional import interpolate
import numpy as np
from PIL import ImageOps
import math
from .animation import sample_to_cv2
import cv2
from modules.shared import opts
DEBUG_MODE = opts.data.get("deforum_debug_mode_enabled", False)
deforum_noise_gen = torch.Generator(device='cpu')
# 2D Perlin noise in PyTorch https://gist.github.com/vadimkantorov/ac1b097753f217c5c11bc2ff396e0a57
def rand_perlin_2d(shape, res, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3):
delta = (res[0] / shape[0], res[1] / shape[1])
d = (shape[0] // res[0], shape[1] // res[1])
grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1]), indexing='ij'), dim = -1) % 1
angles = 2*math.pi*torch.rand(res[0]+1, res[1]+1, generator=deforum_noise_gen)
gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim = -1)
tile_grads = lambda slice1, slice2: gradients[slice1[0]:slice1[1], slice2[0]:slice2[1]].repeat_interleave(d[0], 0).repeat_interleave(d[1], 1)
dot = lambda grad, shift: (torch.stack((grid[:shape[0],:shape[1],0] + shift[0], grid[:shape[0],:shape[1], 1] + shift[1] ), dim = -1) * grad[:shape[0], :shape[1]]).sum(dim = -1)
n00 = dot(tile_grads([0, -1], [0, -1]), [0, 0])
n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0])
n01 = dot(tile_grads([0, -1],[1, None]), [0, -1])
n11 = dot(tile_grads([1, None], [1, None]), [-1,-1])
t = fade(grid[:shape[0], :shape[1]])
return math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1])
def rand_perlin_2d_octaves(shape, res, octaves=1, persistence=0.5):
noise = torch.zeros(shape)
frequency = 1
amplitude = 1
for _ in range(int(octaves)):
noise += amplitude * rand_perlin_2d(shape, (frequency*res[0], frequency*res[1]))
frequency *= 2
amplitude *= persistence
return noise
def condition_noise_mask(noise_mask, invert_mask = False):
if invert_mask:
noise_mask = ImageOps.invert(noise_mask)
noise_mask = np.array(noise_mask.convert("L"))
noise_mask = noise_mask.astype(np.float32) / 255.0
noise_mask = np.around(noise_mask, decimals=0)
noise_mask = torch.from_numpy(noise_mask)
#noise_mask = torch.round(noise_mask)
return noise_mask
def add_noise(sample, noise_amt: float, seed: int, noise_type: str, noise_args, noise_mask = None, invert_mask = False):
deforum_noise_gen.manual_seed(seed) # Reproducibility
perlin_w = sample.shape[0]
perlin_h = sample.shape[1]
perlin_w, perlin_h = map(lambda x: x - x % 64, (perlin_w, perlin_h)) # rescale perlin to multiplies of 64
sample2dshape = (perlin_w, perlin_h)
noise = torch.randn((sample.shape[2], perlin_w, perlin_h), generator=deforum_noise_gen) # White noise
if noise_type == 'perlin':
# rand_perlin_2d_octaves is between -1 and 1, so we need to shift it to be between 0 and 1
# print(sample.shape)
noise = noise * ((rand_perlin_2d_octaves(sample2dshape, (int(noise_args[0]), int(noise_args[1])), octaves=noise_args[2], persistence=noise_args[3]) + torch.ones(sample2dshape)) / 2)
noise = interpolate(noise.unsqueeze(1), size=(sample.shape[0], sample.shape[1])).squeeze(1) # rescale perlin back to the target resolution
if noise_mask is not None:
noise_mask = condition_noise_mask(noise_mask, invert_mask)
noise_to_add = sample_to_cv2(noise * noise_mask)
else:
noise_to_add = sample_to_cv2(noise)
sample = cv2.addWeighted(sample, 1-noise_amt, noise_to_add, noise_amt, 0)
return sample
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