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# This file includes code derived from the SiT project (https://github.com/willisma/SiT),
# which is licensed under the MIT License.
#
# MIT License
#
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import torch as th
import numpy as np
from functools import partial
def expand_t_like_x(t, x):
"""Function to reshape time t to broadcastable dimension of x
Args:
t: [batch_dim,], time vector
x: [batch_dim,...], data point
"""
dims = [1] * (len(x.size()) - 1)
t = t.view(t.size(0), *dims)
return t
#################### Coupling Plans ####################
class ICPlan:
"""Linear Coupling Plan"""
def __init__(self, sigma=0.0):
self.sigma = sigma
def compute_alpha_t(self, t):
"""Compute the data coefficient along the path"""
return t, 1
def compute_sigma_t(self, t):
"""Compute the noise coefficient along the path"""
return 1 - t, -1
def compute_d_alpha_alpha_ratio_t(self, t):
"""Compute the ratio between d_alpha and alpha"""
return 1 / t
def compute_drift(self, x, t):
"""We always output sde according to score parametrization; """
t = expand_t_like_x(t, x)
alpha_ratio = self.compute_d_alpha_alpha_ratio_t(t)
sigma_t, d_sigma_t = self.compute_sigma_t(t)
drift = alpha_ratio * x
diffusion = alpha_ratio * (sigma_t ** 2) - sigma_t * d_sigma_t
return -drift, diffusion
def compute_diffusion(self, x, t, form="constant", norm=1.0):
"""Compute the diffusion term of the SDE
Args:
x: [batch_dim, ...], data point
t: [batch_dim,], time vector
form: str, form of the diffusion term
norm: float, norm of the diffusion term
"""
t = expand_t_like_x(t, x)
choices = {
"constant": norm,
"SBDM": norm * self.compute_drift(x, t)[1],
"sigma": norm * self.compute_sigma_t(t)[0],
"linear": norm * (1 - t),
"decreasing": 0.25 * (norm * th.cos(np.pi * t) + 1) ** 2,
"inccreasing-decreasing": norm * th.sin(np.pi * t) ** 2,
}
try:
diffusion = choices[form]
except KeyError:
raise NotImplementedError(f"Diffusion form {form} not implemented")
return diffusion
def get_score_from_velocity(self, velocity, x, t):
"""Wrapper function: transfrom velocity prediction model to score
Args:
velocity: [batch_dim, ...] shaped tensor; velocity model output
x: [batch_dim, ...] shaped tensor; x_t data point
t: [batch_dim,] time tensor
"""
t = expand_t_like_x(t, x)
alpha_t, d_alpha_t = self.compute_alpha_t(t)
sigma_t, d_sigma_t = self.compute_sigma_t(t)
mean = x
reverse_alpha_ratio = alpha_t / d_alpha_t
var = sigma_t**2 - reverse_alpha_ratio * d_sigma_t * sigma_t
score = (reverse_alpha_ratio * velocity - mean) / var
return score
def get_noise_from_velocity(self, velocity, x, t):
"""Wrapper function: transfrom velocity prediction model to denoiser
Args:
velocity: [batch_dim, ...] shaped tensor; velocity model output
x: [batch_dim, ...] shaped tensor; x_t data point
t: [batch_dim,] time tensor
"""
t = expand_t_like_x(t, x)
alpha_t, d_alpha_t = self.compute_alpha_t(t)
sigma_t, d_sigma_t = self.compute_sigma_t(t)
mean = x
reverse_alpha_ratio = alpha_t / d_alpha_t
var = reverse_alpha_ratio * d_sigma_t - sigma_t
noise = (reverse_alpha_ratio * velocity - mean) / var
return noise
def get_velocity_from_score(self, score, x, t):
"""Wrapper function: transfrom score prediction model to velocity
Args:
score: [batch_dim, ...] shaped tensor; score model output
x: [batch_dim, ...] shaped tensor; x_t data point
t: [batch_dim,] time tensor
"""
t = expand_t_like_x(t, x)
drift, var = self.compute_drift(x, t)
velocity = var * score - drift
return velocity
def compute_mu_t(self, t, x0, x1):
"""Compute the mean of time-dependent density p_t"""
t = expand_t_like_x(t, x1)
alpha_t, _ = self.compute_alpha_t(t)
sigma_t, _ = self.compute_sigma_t(t)
# t*x1 + (1-t)*x0 ; t=0 x0; t=1 x1
return alpha_t * x1 + sigma_t * x0
def compute_xt(self, t, x0, x1):
"""Sample xt from time-dependent density p_t; rng is required"""
xt = self.compute_mu_t(t, x0, x1)
return xt
def compute_ut(self, t, x0, x1, xt):
"""Compute the vector field corresponding to p_t"""
t = expand_t_like_x(t, x1)
_, d_alpha_t = self.compute_alpha_t(t)
_, d_sigma_t = self.compute_sigma_t(t)
return d_alpha_t * x1 + d_sigma_t * x0
def plan(self, t, x0, x1):
xt = self.compute_xt(t, x0, x1)
ut = self.compute_ut(t, x0, x1, xt)
return t, xt, ut
class VPCPlan(ICPlan):
"""class for VP path flow matching"""
def __init__(self, sigma_min=0.1, sigma_max=20.0):
self.sigma_min = sigma_min
self.sigma_max = sigma_max
self.log_mean_coeff = lambda t: -0.25 * ((1 - t) ** 2) * \
(self.sigma_max - self.sigma_min) - 0.5 * (1 - t) * self.sigma_min
self.d_log_mean_coeff = lambda t: 0.5 * (1 - t) * \
(self.sigma_max - self.sigma_min) + 0.5 * self.sigma_min
def compute_alpha_t(self, t):
"""Compute coefficient of x1"""
alpha_t = self.log_mean_coeff(t)
alpha_t = th.exp(alpha_t)
d_alpha_t = alpha_t * self.d_log_mean_coeff(t)
return alpha_t, d_alpha_t
def compute_sigma_t(self, t):
"""Compute coefficient of x0"""
p_sigma_t = 2 * self.log_mean_coeff(t)
sigma_t = th.sqrt(1 - th.exp(p_sigma_t))
d_sigma_t = th.exp(p_sigma_t) * (2 * self.d_log_mean_coeff(t)) / (-2 * sigma_t)
return sigma_t, d_sigma_t
def compute_d_alpha_alpha_ratio_t(self, t):
"""Special purposed function for computing numerical stabled d_alpha_t / alpha_t"""
return self.d_log_mean_coeff(t)
def compute_drift(self, x, t):
"""Compute the drift term of the SDE"""
t = expand_t_like_x(t, x)
beta_t = self.sigma_min + (1 - t) * (self.sigma_max - self.sigma_min)
return -0.5 * beta_t * x, beta_t / 2
class GVPCPlan(ICPlan):
def __init__(self, sigma=0.0):
super().__init__(sigma)
def compute_alpha_t(self, t):
"""Compute coefficient of x1"""
alpha_t = th.sin(t * np.pi / 2)
d_alpha_t = np.pi / 2 * th.cos(t * np.pi / 2)
return alpha_t, d_alpha_t
def compute_sigma_t(self, t):
"""Compute coefficient of x0"""
sigma_t = th.cos(t * np.pi / 2)
d_sigma_t = -np.pi / 2 * th.sin(t * np.pi / 2)
return sigma_t, d_sigma_t
def compute_d_alpha_alpha_ratio_t(self, t):
"""Special purposed function for computing numerical stabled d_alpha_t / alpha_t"""
return np.pi / (2 * th.tan(t * np.pi / 2))
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