/
method.py
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/
method.py
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# Copyright 2022 Luping Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import copy
import torch as th
def choose_method(name):
if name == 'DDIM':
return gen_order_1
elif name == 'S-PNDM':
return gen_order_2
elif name == 'F-PNDM':
return gen_order_4
elif name == 'FON':
return gen_fon
elif name == 'PF':
return gen_pflow
else:
return None
def gen_pflow(img, t, t_next, model, betas, total_step):
n = img.shape[0]
beta_0, beta_1 = betas[0], betas[-1]
t_start = th.ones(n, device=img.device) * t
beta_t = (beta_0 + t_start * (beta_1 - beta_0)) * total_step
log_mean_coeff = (-0.25 * t_start ** 2 * (beta_1 - beta_0) - 0.5 * t_start * beta_0) * total_step
std = th.sqrt(1. - th.exp(2. * log_mean_coeff))
# drift, diffusion -> f(x,t), g(t)
drift, diffusion = -0.5 * beta_t.view(-1, 1, 1, 1) * img, th.sqrt(beta_t)
score = - model(img, t_start * (total_step - 1)) / std.view(-1, 1, 1, 1) # score -> noise
drift = drift - diffusion.view(-1, 1, 1, 1) ** 2 * score * 0.5 # drift -> dx/dt
return drift
def gen_fon(img, t, t_next, model, alphas_cump, ets):
t_list = [t, (t + t_next) / 2.0, t_next]
if len(ets) > 2:
noise = model(img, t)
img_next = transfer(img, t, t-1, noise, alphas_cump)
delta = img_next - img
ets.append(delta)
else:
noise = model(img, t_list[0])
img_ = transfer(img, t, t - 1, noise, alphas_cump)
delta_1 = img_ - img
ets.append(delta_1)
img_2 = img + delta_1 * (t - t_next).view(-1, 1, 1, 1) / 2.0
noise = model(img_2, t_list[1])
img_ = transfer(img, t, t - 1, noise, alphas_cump)
delta_2 = img_ - img
img_3 = img + delta_2 * (t - t_next).view(-1, 1, 1, 1) / 2.0
noise = model(img_3, t_list[1])
img_ = transfer(img, t, t - 1, noise, alphas_cump)
delta_3 = img_ - img
img_4 = img + delta_3 * (t - t_next).view(-1, 1, 1, 1)
noise = model(img_4, t_list[2])
img_ = transfer(img, t, t - 1, noise, alphas_cump)
delta_4 = img_ - img
delta = (1 / 6.0) * (delta_1 + 2*delta_2 + 2*delta_3 + delta_4)
img_next = img + delta * (t - t_next).view(-1, 1, 1, 1)
return img_next
def gen_order_4(img, t, t_next, model, alphas_cump, ets):
t_list = [t, (t+t_next)/2, t_next]
if len(ets) > 2:
noise_ = model(img, t)
ets.append(noise_)
noise = (1 / 24) * (55 * ets[-1] - 59 * ets[-2] + 37 * ets[-3] - 9 * ets[-4])
else:
noise = runge_kutta(img, t_list, model, alphas_cump, ets)
img_next = transfer(img, t, t_next, noise, alphas_cump)
return img_next
def runge_kutta(x, t_list, model, alphas_cump, ets):
e_1 = model(x, t_list[0])
ets.append(e_1)
x_2 = transfer(x, t_list[0], t_list[1], e_1, alphas_cump)
e_2 = model(x_2, t_list[1])
x_3 = transfer(x, t_list[0], t_list[1], e_2, alphas_cump)
e_3 = model(x_3, t_list[1])
x_4 = transfer(x, t_list[0], t_list[2], e_3, alphas_cump)
e_4 = model(x_4, t_list[2])
et = (1 / 6) * (e_1 + 2 * e_2 + 2 * e_3 + e_4)
return et
def gen_order_2(img, t, t_next, model, alphas_cump, ets):
if len(ets) > 0:
noise_ = model(img, t)
ets.append(noise_)
noise = 0.5 * (3 * ets[-1] - ets[-2])
else:
noise = improved_eular(img, t, t_next, model, alphas_cump, ets)
img_next = transfer(img, t, t_next, noise, alphas_cump)
return img_next
def improved_eular(x, t, t_next, model, alphas_cump, ets):
e_1 = model(x, t)
ets.append(e_1)
x_2 = transfer(x, t, t_next, e_1, alphas_cump)
e_2 = model(x_2, t_next)
et = (e_1 + e_2) / 2
# x_next = transfer(x, t, t_next, et, alphas_cump)
return et
def gen_order_1(img, t, t_next, model, alphas_cump, ets):
noise = model(img, t)
ets.append(noise)
img_next = transfer(img, t, t_next, noise, alphas_cump)
return img_next
def transfer(x, t, t_next, et, alphas_cump):
at = alphas_cump[t.long() + 1].view(-1, 1, 1, 1)
at_next = alphas_cump[t_next.long() + 1].view(-1, 1, 1, 1)
x_delta = (at_next - at) * ((1 / (at.sqrt() * (at.sqrt() + at_next.sqrt()))) * x - \
1 / (at.sqrt() * (((1 - at_next) * at).sqrt() + ((1 - at) * at_next).sqrt())) * et)
x_next = x + x_delta
return x_next
def transfer_dev(x, t, t_next, et, alphas_cump):
at = alphas_cump[t.long()+1].view(-1, 1, 1, 1)
at_next = alphas_cump[t_next.long()+1].view(-1, 1, 1, 1)
x_start = th.sqrt(1.0 / at) * x - th.sqrt(1.0 / at - 1) * et
x_start = x_start.clamp(-1.0, 1.0)
x_next = x_start * th.sqrt(at_next) + th.sqrt(1 - at_next) * et
return x_next