583 lines
21 KiB
Python
583 lines
21 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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from latent_preview import Latent2RGBPreviewer
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'''
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The following gaussian functions were utilized from the Fooocus UI, many thanks to github.com/Illyasviel !
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'''
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def gaussian_kernel(kernel_size, sigma):
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kernel = np.fromfunction(
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lambda x, y: (1 / (2 * np.pi * sigma ** 2)) *
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np.exp(-((x - (kernel_size - 1) / 2) ** 2 + (y - (kernel_size - 1) / 2) ** 2) / (2 * sigma ** 2)),
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(kernel_size, kernel_size)
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)
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return kernel / np.sum(kernel)
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class GaussianBlur(nn.Module):
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def __init__(self, channels, kernel_size, sigma):
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super(GaussianBlur, self).__init__()
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self.channels = channels
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self.kernel_size = kernel_size
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self.sigma = sigma
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self.padding = kernel_size // 2 # Ensure output size matches input size
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self.register_buffer('kernel', torch.tensor(gaussian_kernel(kernel_size, sigma), dtype=torch.float32))
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self.kernel = self.kernel.view(1, 1, kernel_size, kernel_size)
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self.kernel = self.kernel.expand(self.channels, -1, -1, -1) # Repeat the kernel for each input channel
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def forward(self, x):
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x = F.conv2d(x, self.kernel.to(x), padding=self.padding, groups=self.channels)
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return x
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gaussian_filter_2d = GaussianBlur(4, 7, 0.8)
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'''
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As of August 18th (on Fooocus' GitHub), the gaussian functions were replaced by an anisotropic function for better stability.
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'''
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Tensor = torch.Tensor
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Device = torch.DeviceObjType
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Dtype = torch.Type
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pad = torch.nn.functional.pad
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def _compute_zero_padding(kernel_size: tuple[int, int] | int) -> tuple[int, int]:
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ky, kx = _unpack_2d_ks(kernel_size)
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return (ky - 1) // 2, (kx - 1) // 2
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def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]:
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if isinstance(kernel_size, int):
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ky = kx = kernel_size
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else:
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assert len(kernel_size) == 2, '2D Kernel size should have a length of 2.'
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ky, kx = kernel_size
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ky = int(ky)
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kx = int(kx)
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return ky, kx
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def gaussian(
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window_size: int, sigma: Tensor | float, *, device: Device | None = None, dtype: Dtype | None = None
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) -> Tensor:
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batch_size = sigma.shape[0]
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x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1)
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if window_size % 2 == 0:
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x = x + 0.5
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gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0)))
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return gauss / gauss.sum(-1, keepdim=True)
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def get_gaussian_kernel1d(
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kernel_size: int,
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sigma: float | Tensor,
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force_even: bool = False,
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*,
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device: Device | None = None,
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dtype: Dtype | None = None,
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) -> Tensor:
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return gaussian(kernel_size, sigma, device=device, dtype=dtype)
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def get_gaussian_kernel2d(
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kernel_size: tuple[int, int] | int,
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sigma: tuple[float, float] | Tensor,
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force_even: bool = False,
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*,
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device: Device | None = None,
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dtype: Dtype | None = None,
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) -> Tensor:
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sigma = torch.Tensor([[sigma, sigma]]).to(device=device, dtype=dtype)
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ksize_y, ksize_x = _unpack_2d_ks(kernel_size)
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sigma_y, sigma_x = sigma[:, 0, None], sigma[:, 1, None]
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kernel_y = get_gaussian_kernel1d(ksize_y, sigma_y, force_even, device=device, dtype=dtype)[..., None]
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kernel_x = get_gaussian_kernel1d(ksize_x, sigma_x, force_even, device=device, dtype=dtype)[..., None]
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return kernel_y * kernel_x.view(-1, 1, ksize_x)
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def _bilateral_blur(
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input: Tensor,
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guidance: Tensor | None,
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kernel_size: tuple[int, int] | int,
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sigma_color: float | Tensor,
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sigma_space: tuple[float, float] | Tensor,
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border_type: str = 'reflect',
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color_distance_type: str = 'l1',
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) -> Tensor:
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if isinstance(sigma_color, Tensor):
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sigma_color = sigma_color.to(device=input.device, dtype=input.dtype).view(-1, 1, 1, 1, 1)
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ky, kx = _unpack_2d_ks(kernel_size)
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pad_y, pad_x = _compute_zero_padding(kernel_size)
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padded_input = pad(input, (pad_x, pad_x, pad_y, pad_y), mode=border_type)
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unfolded_input = padded_input.unfold(2, ky, 1).unfold(3, kx, 1).flatten(-2) # (B, C, H, W, Ky x Kx)
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if guidance is None:
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guidance = input
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unfolded_guidance = unfolded_input
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else:
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padded_guidance = pad(guidance, (pad_x, pad_x, pad_y, pad_y), mode=border_type)
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unfolded_guidance = padded_guidance.unfold(2, ky, 1).unfold(3, kx, 1).flatten(-2) # (B, C, H, W, Ky x Kx)
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diff = unfolded_guidance - guidance.unsqueeze(-1)
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if color_distance_type == "l1":
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color_distance_sq = diff.abs().sum(1, keepdim=True).square()
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elif color_distance_type == "l2":
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color_distance_sq = diff.square().sum(1, keepdim=True)
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else:
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raise ValueError("color_distance_type only acceps l1 or l2")
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color_kernel = (-0.5 / sigma_color**2 * color_distance_sq).exp() # (B, 1, H, W, Ky x Kx)
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space_kernel = get_gaussian_kernel2d(kernel_size, sigma_space, device=input.device, dtype=input.dtype)
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space_kernel = space_kernel.view(-1, 1, 1, 1, kx * ky)
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kernel = space_kernel * color_kernel
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out = (unfolded_input * kernel).sum(-1) / kernel.sum(-1)
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return out
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def bilateral_blur(
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input: Tensor,
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kernel_size: tuple[int, int] | int = (13, 13),
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sigma_color: float | Tensor = 3.0,
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sigma_space: tuple[float, float] | Tensor = 3.0,
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border_type: str = 'reflect',
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color_distance_type: str = 'l1',
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) -> Tensor:
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return _bilateral_blur(input, None, kernel_size, sigma_color, sigma_space, border_type, color_distance_type)
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def joint_bilateral_blur(
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input: Tensor,
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guidance: Tensor,
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kernel_size: tuple[int, int] | int,
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sigma_color: float | Tensor,
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sigma_space: tuple[float, float] | Tensor,
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border_type: str = 'reflect',
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color_distance_type: str = 'l1',
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) -> Tensor:
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return _bilateral_blur(input, guidance, kernel_size, sigma_color, sigma_space, border_type, color_distance_type)
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class _BilateralBlur(torch.nn.Module):
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def __init__(
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self,
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kernel_size: tuple[int, int] | int,
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sigma_color: float | Tensor,
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sigma_space: tuple[float, float] | Tensor,
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border_type: str = 'reflect',
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color_distance_type: str = "l1",
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) -> None:
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super().__init__()
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self.kernel_size = kernel_size
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self.sigma_color = sigma_color
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self.sigma_space = sigma_space
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self.border_type = border_type
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self.color_distance_type = color_distance_type
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def __repr__(self) -> str:
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return (
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f"{self.__class__.__name__}"
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f"(kernel_size={self.kernel_size}, "
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f"sigma_color={self.sigma_color}, "
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f"sigma_space={self.sigma_space}, "
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f"border_type={self.border_type}, "
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f"color_distance_type={self.color_distance_type})"
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)
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class BilateralBlur(_BilateralBlur):
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def forward(self, input: Tensor) -> Tensor:
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return bilateral_blur(
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input, self.kernel_size, self.sigma_color, self.sigma_space, self.border_type, self.color_distance_type
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)
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class JointBilateralBlur(_BilateralBlur):
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def forward(self, input: Tensor, guidance: Tensor) -> Tensor:
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return joint_bilateral_blur(
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input,
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guidance,
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self.kernel_size,
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self.sigma_color,
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self.sigma_space,
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self.border_type,
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self.color_distance_type,
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)
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# Below is perlin noise from https://github.com/tasptz/pytorch-perlin-noise/blob/main/perlin_noise/perlin_noise.py
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from torch import Generator, Tensor, lerp
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from torch.nn.functional import unfold
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from typing import Callable, Tuple
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from math import pi
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def get_positions(block_shape: Tuple[int, int]) -> Tensor:
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"""
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Generate position tensor.
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Arguments:
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block_shape -- (height, width) of position tensor
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Returns:
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position vector shaped (1, height, width, 1, 1, 2)
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"""
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bh, bw = block_shape
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positions = torch.stack(
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torch.meshgrid(
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[(torch.arange(b) + 0.5) / b for b in (bw, bh)],
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indexing="xy",
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),
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-1,
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).view(1, bh, bw, 1, 1, 2)
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return positions
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def unfold_grid(vectors: Tensor) -> Tensor:
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"""
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Unfold vector grid to batched vectors.
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Arguments:
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vectors -- grid vectors
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Returns:
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batched grid vectors
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"""
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batch_size, _, gpy, gpx = vectors.shape
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return (
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unfold(vectors, (2, 2))
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.view(batch_size, 2, 4, -1)
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.permute(0, 2, 3, 1)
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.view(batch_size, 4, gpy - 1, gpx - 1, 2)
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)
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def smooth_step(t: Tensor) -> Tensor:
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"""
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Smooth step function [0, 1] -> [0, 1].
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Arguments:
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t -- input values (any shape)
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Returns:
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output values (same shape as input values)
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"""
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return t * t * (3.0 - 2.0 * t)
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def perlin_noise_tensor(
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vectors: Tensor, positions: Tensor, step: Callable = None
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) -> Tensor:
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"""
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Generate perlin noise from batched vectors and positions.
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Arguments:
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vectors -- batched grid vectors shaped (batch_size, 4, grid_height, grid_width, 2)
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positions -- batched grid positions shaped (batch_size or 1, block_height, block_width, grid_height or 1, grid_width or 1, 2)
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Keyword Arguments:
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step -- smooth step function [0, 1] -> [0, 1] (default: `smooth_step`)
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Raises:
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Exception: if position and vector shapes do not match
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Returns:
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(batch_size, block_height * grid_height, block_width * grid_width)
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"""
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if step is None:
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step = smooth_step
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batch_size = vectors.shape[0]
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# grid height, grid width
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gh, gw = vectors.shape[2:4]
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# block height, block width
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bh, bw = positions.shape[1:3]
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for i in range(2):
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if positions.shape[i + 3] not in (1, vectors.shape[i + 2]):
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raise Exception(
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f"Blocks shapes do not match: vectors ({vectors.shape[1]}, {vectors.shape[2]}), positions {gh}, {gw})"
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)
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if positions.shape[0] not in (1, batch_size):
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raise Exception(
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f"Batch sizes do not match: vectors ({vectors.shape[0]}), positions ({positions.shape[0]})"
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)
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vectors = vectors.view(batch_size, 4, 1, gh * gw, 2)
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positions = positions.view(positions.shape[0], bh * bw, -1, 2)
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step_x = step(positions[..., 0])
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step_y = step(positions[..., 1])
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row0 = lerp(
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(vectors[:, 0] * positions).sum(dim=-1),
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(vectors[:, 1] * (positions - positions.new_tensor((1, 0)))).sum(dim=-1),
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step_x,
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)
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row1 = lerp(
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(vectors[:, 2] * (positions - positions.new_tensor((0, 1)))).sum(dim=-1),
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(vectors[:, 3] * (positions - positions.new_tensor((1, 1)))).sum(dim=-1),
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step_x,
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)
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noise = lerp(row0, row1, step_y)
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return (
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noise.view(
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batch_size,
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bh,
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bw,
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gh,
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gw,
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)
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.permute(0, 3, 1, 4, 2)
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.reshape(batch_size, gh * bh, gw * bw)
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)
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def perlin_noise(
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grid_shape: Tuple[int, int],
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out_shape: Tuple[int, int],
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batch_size: int = 1,
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generator: Generator = None,
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*args,
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**kwargs,
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) -> Tensor:
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"""
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Generate perlin noise with given shape. `*args` and `**kwargs` are forwarded to `Tensor` creation.
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Arguments:
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grid_shape -- Shape of grid (height, width).
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out_shape -- Shape of output noise image (height, width).
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Keyword Arguments:
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batch_size -- (default: {1})
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generator -- random generator used for grid vectors (default: {None})
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Raises:
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Exception: if grid and out shapes do not match
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Returns:
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Noise image shaped (batch_size, height, width)
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"""
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# grid height and width
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gh, gw = grid_shape
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# output height and width
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oh, ow = out_shape
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# block height and width
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bh, bw = oh // gh, ow // gw
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if oh != bh * gh:
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raise Exception(f"Output height {oh} must be divisible by grid height {gh}")
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if ow != bw * gw != 0:
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raise Exception(f"Output width {ow} must be divisible by grid width {gw}")
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angle = torch.empty(
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[batch_size] + [s + 1 for s in grid_shape], *args, **kwargs
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).uniform_(to=2.0 * pi, generator=generator)
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# random vectors on grid points
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vectors = unfold_grid(torch.stack((torch.cos(angle), torch.sin(angle)), dim=1))
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# positions inside grid cells [0, 1)
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positions = get_positions((bh, bw)).to(vectors)
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return perlin_noise_tensor(vectors, positions).squeeze(0)
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def generate_1f_noise(tensor, alpha, k):
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"""Generate 1/f noise for a given tensor.
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Args:
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tensor: The tensor to add noise to.
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alpha: The parameter that determines the slope of the spectrum.
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k: A constant.
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Returns:
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A tensor with the same shape as `tensor` containing 1/f noise.
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"""
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fft = torch.fft.fft2(tensor)
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freq = torch.arange(1, len(fft) + 1, dtype=torch.float)
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spectral_density = k / freq**alpha
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noise = torch.randn(tensor.shape) * spectral_density
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return noise
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def green_noise(width, height):
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noise = torch.randn(width, height)
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scale = 1.0 / (width * height)
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fy = torch.fft.fftfreq(width)[:, None] ** 2
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fx = torch.fft.fftfreq(height) ** 2
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f = fy + fx
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power = torch.sqrt(f)
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power[0, 0] = 1
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noise = torch.fft.ifft2(torch.fft.fft2(noise) / torch.sqrt(power))
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noise *= scale / noise.std()
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return torch.real(noise)
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# Tonemapping functions
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def train_difference(a: Tensor, b: Tensor, c: Tensor) -> Tensor:
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diff_AB = a.float() - b.float()
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distance_A0 = torch.abs(b.float() - c.float())
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distance_A1 = torch.abs(b.float() - a.float())
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sum_distances = distance_A0 + distance_A1
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scale = torch.where(
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sum_distances != 0, distance_A1 / sum_distances, torch.tensor(0.0).float()
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)
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sign_scale = torch.sign(b.float() - c.float())
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scale = sign_scale * torch.abs(scale)
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new_diff = scale * torch.abs(diff_AB)
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return new_diff
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class ModelSamplerLatentMegaModifier:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"sharpness_multiplier": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.1}),
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"sharpness_method": (["anisotropic", "gaussian"], ),
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"tonemap_multiplier": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.01}),
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"tonemap_method": (["reinhard", "arctan", "quantile"], ),
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"tonemap_percentile": ("FLOAT", {"default": 100.0, "min": 0.0, "max": 100.0, "step": 0.05}),
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"rescale_cfg_phi": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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"extra_noise_type": (["gaussian", "perlin", "pink", "green"], ),
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"extra_noise_method": (["add", "add_scaled", "speckle"], ),
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"extra_noise_multiplier": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "mega_modify"
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CATEGORY = "clybNodes"
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def mega_modify(self, model, sharpness_multiplier, sharpness_method, tonemap_multiplier, tonemap_method, tonemap_percentile, rescale_cfg_phi, extra_noise_type, extra_noise_method, extra_noise_multiplier):
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match sharpness_method:
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case "anisotropic":
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degrade_func = bilateral_blur
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case "gaussian":
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degrade_func = gaussian_filter_2d
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case _:
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print("For some reason, the sharpness filter could not be found.")
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def modify_latent(args):
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cond = args["cond"]
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uncond = args["uncond"]
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cond_scale = args["cond_scale"]
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timestep = args["timestep"]
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noise_pred = (cond - uncond)
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# Extra noise
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if extra_noise_multiplier > 0:
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match extra_noise_type:
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case "gaussian":
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extra_noise = torch.randn_like(cond)
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case "perlin":
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cond_size_0 = cond.size(dim=2)
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cond_size_1 = cond.size(dim=3)
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extra_noise = perlin_noise(grid_shape=(cond_size_0, cond_size_1), out_shape=(cond_size_0, cond_size_1), batch_size=4).to(cond.device).unsqueeze(0)
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mean = torch.mean(extra_noise)
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std = torch.std(extra_noise)
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|
|
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extra_noise.sub_(mean).div_(std)
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case "pink":
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|
extra_noise = generate_1f_noise(cond, 2, extra_noise_multiplier).to(cond.device)
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mean = torch.mean(extra_noise)
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std = torch.std(extra_noise)
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|
|
|
extra_noise.sub_(mean).div_(std)
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case "green":
|
|
cond_size_0 = cond.size(dim=2)
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|
cond_size_1 = cond.size(dim=3)
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|
extra_noise = green_noise(cond_size_0, cond_size_1).to(cond.device)
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|
mean = torch.mean(extra_noise)
|
|
std = torch.std(extra_noise)
|
|
|
|
extra_noise.sub_(mean).div_(std)
|
|
alpha_noise = 1.0 - (timestep / 999.0)[:, None, None, None].clone() # Get alpha multiplier, lower alpha at high sigmas/high noise
|
|
alpha_noise *= 0.001 * extra_noise_multiplier # User-input and weaken the strength so we don't annihilate the latent.
|
|
match extra_noise_method:
|
|
case "add":
|
|
cond = cond + extra_noise * alpha_noise
|
|
case "add_scaled":
|
|
cond = cond + train_difference(cond, extra_noise, cond) * alpha_noise
|
|
case "speckle":
|
|
cond = cond + cond * extra_noise * alpha_noise
|
|
case _:
|
|
cond = cond + extra_noise * alpha_noise
|
|
|
|
# Sharpness
|
|
alpha = 1.0 - (timestep / 999.0)[:, None, None, None].clone() # Get alpha multiplier, lower alpha at high sigmas/high noise
|
|
alpha *= 0.001 * sharpness_multiplier # User-input and weaken the strength so we don't annihilate the latent.
|
|
degraded_cond = degrade_func(cond) * alpha + cond * (1.0 - alpha) # Mix the modified latent with the existing latent by the alpha
|
|
noise_pred_degraded = (degraded_cond - uncond) # New noise pred
|
|
|
|
# Tonemap noise
|
|
if tonemap_multiplier == 0:
|
|
new_magnitude = 1.0
|
|
else:
|
|
match tonemap_method:
|
|
case "reinhard":
|
|
noise_pred_vector_magnitude = (torch.linalg.vector_norm(noise_pred_degraded, dim=(1)) + 0.0000000001)[:,None]
|
|
noise_pred_degraded /= noise_pred_vector_magnitude
|
|
|
|
mean = torch.mean(noise_pred_vector_magnitude, dim=(1,2,3), keepdim=True)
|
|
std = torch.std(noise_pred_vector_magnitude, dim=(1,2,3), keepdim=True)
|
|
|
|
top = (std * 3 * (100 / tonemap_percentile) + mean) * tonemap_multiplier
|
|
|
|
noise_pred_vector_magnitude *= (1.0 / top)
|
|
new_magnitude = noise_pred_vector_magnitude / (noise_pred_vector_magnitude + 1.0)
|
|
new_magnitude *= top
|
|
|
|
noise_pred_degraded *= new_magnitude
|
|
case "arctan":
|
|
noise_pred_vector_magnitude = (torch.linalg.vector_norm(noise_pred_degraded, dim=(1)) + 0.0000000001)[:,None]
|
|
noise_pred_degraded /= noise_pred_vector_magnitude
|
|
|
|
noise_pred_degraded = (torch.arctan(noise_pred_degraded * tonemap_multiplier) * (1 / tonemap_multiplier)) + (noise_pred_degraded * (100 - tonemap_percentile) / 100)
|
|
|
|
noise_pred_degraded *= noise_pred_vector_magnitude
|
|
case "quantile":
|
|
s: FloatTensor = torch.quantile(
|
|
(uncond + noise_pred_degraded * cond_scale).flatten(start_dim=1).abs(),
|
|
tonemap_percentile / 100,
|
|
dim = -1
|
|
) * tonemap_multiplier
|
|
s.clamp_(min = 1.)
|
|
s = s.reshape(*s.shape, 1, 1, 1)
|
|
noise_pred_degraded = noise_pred_degraded.clamp(-s, s) / s
|
|
case _:
|
|
print("Could not tonemap, for the method was not found.")
|
|
|
|
# Rescale CFG
|
|
if rescale_cfg_phi == 0:
|
|
x_final = uncond + noise_pred_degraded * cond_scale
|
|
else:
|
|
x_cfg = uncond + noise_pred_degraded * cond_scale
|
|
ro_pos = torch.std(degraded_cond, dim=(1,2,3), keepdim=True)
|
|
ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True)
|
|
|
|
x_rescaled = x_cfg * (ro_pos / ro_cfg)
|
|
x_final = rescale_cfg_phi * x_rescaled + (1.0 - rescale_cfg_phi) * x_cfg
|
|
|
|
return x_final # General formula for CFG. uncond + (cond - uncond) * cond_scale
|
|
|
|
m = model.clone()
|
|
m.set_model_sampler_cfg_function(modify_latent)
|
|
return (m, )
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"Latent Diffusion Mega Modifier": ModelSamplerLatentMegaModifier,
|
|
}
|