/
scheduling_ddpm.py
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scheduling_ddpm.py
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# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# 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 math
import numpy as np
from ..configuration_utils import ConfigMixin
from .scheduling_utils import SchedulerMixin, betas_for_alpha_bar, linear_beta_schedule
class DDPMScheduler(SchedulerMixin, ConfigMixin):
def __init__(
self,
timesteps=1000,
beta_start=0.0001,
beta_end=0.02,
beta_schedule="linear",
trained_betas=None,
timestep_values=None,
variance_type="fixed_small",
clip_predicted_image=True,
tensor_format="np",
):
super().__init__()
self.register(
timesteps=timesteps,
beta_start=beta_start,
beta_end=beta_end,
beta_schedule=beta_schedule,
trained_betas=trained_betas,
timestep_values=timestep_values,
variance_type=variance_type,
clip_predicted_image=clip_predicted_image,
)
self.timesteps = int(timesteps)
self.timestep_values = timestep_values # save the fixed timestep values for BDDM
self.clip_image = clip_predicted_image
self.variance_type = variance_type
if trained_betas is not None:
self.betas = np.asarray(trained_betas)
elif beta_schedule == "linear":
self.betas = linear_beta_schedule(timesteps, beta_start=beta_start, beta_end=beta_end)
elif beta_schedule == "squaredcos_cap_v2":
# GLIDE cosine schedule
self.betas = betas_for_alpha_bar(
timesteps,
lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
)
else:
raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
self.alphas = 1.0 - self.betas
self.alphas_cumprod = np.cumprod(self.alphas, axis=0)
self.one = np.array(1.0)
self.set_format(tensor_format=tensor_format)
# self.register_buffer("betas", betas.to(torch.float32))
# self.register_buffer("alphas", alphas.to(torch.float32))
# self.register_buffer("alphas_cumprod", alphas_cumprod.to(torch.float32))
# alphas_cumprod_prev = torch.nn.functional.pad(alphas_cumprod[:-1], (1, 0), value=1.0)
# TODO(PVP) - check how much of these is actually necessary!
# LDM only uses "fixed_small"; glide seems to use a weird mix of the two, ...
# https://github.com/openai/glide-text2im/blob/69b530740eb6cef69442d6180579ef5ba9ef063e/glide_text2im/gaussian_diffusion.py#L246
# variance = betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)
# if variance_type == "fixed_small":
# log_variance = torch.log(variance.clamp(min=1e-20))
# elif variance_type == "fixed_large":
# log_variance = torch.log(torch.cat([variance[1:2], betas[1:]], dim=0))
#
#
# self.register_buffer("log_variance", log_variance.to(torch.float32))
def get_alpha(self, time_step):
return self.alphas[time_step]
def get_beta(self, time_step):
return self.betas[time_step]
def get_alpha_prod(self, time_step):
if time_step < 0:
return self.one
return self.alphas_cumprod[time_step]
def get_variance(self, t):
alpha_prod_t = self.get_alpha_prod(t)
alpha_prod_t_prev = self.get_alpha_prod(t - 1)
# For t > 0, compute predicted variance 尾t (see formala (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous image
# x_{t-1} ~ N(pred_prev_image, variance) == add variane to pred_image
variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.get_beta(t)
# hacks - were probs added for training stability
if self.variance_type == "fixed_small":
variance = self.clip(variance, min_value=1e-20)
elif self.variance_type == "fixed_large":
variance = self.get_beta(t)
return variance
def step(self, residual, image, t):
# 1. compute alphas, betas
alpha_prod_t = self.get_alpha_prod(t)
alpha_prod_t_prev = self.get_alpha_prod(t - 1)
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
# 2. compute predicted original image from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
pred_original_image = (image - beta_prod_t ** (0.5) * residual) / alpha_prod_t ** (0.5)
# 3. Clip "predicted x_0"
if self.clip_predicted_image:
pred_original_image = self.clip(pred_original_image, -1, 1)
# 4. Compute coefficients for pred_original_image x_0 and current image x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
pred_original_image_coeff = (alpha_prod_t_prev ** (0.5) * self.get_beta(t)) / beta_prod_t
current_image_coeff = self.get_alpha(t) ** (0.5) * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous image 碌_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
pred_prev_image = pred_original_image_coeff * pred_original_image + current_image_coeff * image
return pred_prev_image
def forward_step(self, original_image, noise, t):
sqrt_alpha_prod = self.get_alpha_prod(t) ** 0.5
sqrt_one_minus_alpha_prod = (1 - self.get_alpha_prod(t)) ** 0.5
noisy_image = sqrt_alpha_prod * original_image + sqrt_one_minus_alpha_prod * noise
return noisy_image
def __len__(self):
return self.timesteps