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scheduling_ddpm.py
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scheduling_ddpm.py
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# Copyright 2022 UC Berkely Team and 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.
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
import math
import numpy as np
from ..configuration_utils import ConfigMixin
from .scheduling_utils import SchedulerMixin
def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
"""
Create a beta schedule that discretizes the given alpha_t_bar function,
which defines the cumulative product of (1-beta) over time from t = [0,1].
:param num_diffusion_timesteps: the number of betas to produce.
:param alpha_bar: a lambda that takes an argument t from 0 to 1 and
produces the cumulative product of (1-beta) up to that
part of the diffusion process.
:param max_beta: the maximum beta to use; use values lower than 1 to
prevent singularities.
"""
def alpha_bar(time_step):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
betas = []
for i in range(num_diffusion_timesteps):
t1 = i / num_diffusion_timesteps
t2 = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
return np.array(betas, dtype=np.float32)
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_sample=True,
tensor_format="np",
):
super().__init__()
self.register_to_config(
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_sample=clip_sample,
)
if trained_betas is not None:
self.betas = np.asarray(trained_betas)
elif beta_schedule == "linear":
self.betas = np.linspace(beta_start, beta_end, timesteps, dtype=np.float32)
elif beta_schedule == "squaredcos_cap_v2":
# GLIDE cosine schedule
self.betas = betas_for_alpha_bar(timesteps)
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)
def get_variance(self, t, variance_type=None):
alpha_prod_t = self.alphas_cumprod[t]
alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one
# 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 sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variane to pred_sample
variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
if variance_type is None:
variance_type = self.config.variance_type
# hacks - were probs added for training stability
if variance_type == "fixed_small":
variance = self.clip(variance, min_value=1e-20)
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
variance = self.log(self.clip(variance, min_value=1e-20))
elif variance_type == "fixed_large":
variance = self.betas[t]
elif variance_type == "fixed_large_log":
# GLIDE max_log
variance = self.log(self.betas[t])
return variance
def step(self, residual, sample, t, predict_epsilon=True):
# 1. compute alphas, betas
alpha_prod_t = self.alphas_cumprod[t]
alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if predict_epsilon:
pred_original_sample = (sample - beta_prod_t ** (0.5) * residual) / alpha_prod_t ** (0.5)
else:
pred_original_sample = residual
# 3. Clip "predicted x_0"
if self.config.clip_sample:
pred_original_sample = self.clip(pred_original_sample, -1, 1)
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * self.betas[t]) / beta_prod_t
current_sample_coeff = self.alphas[t] ** (0.5) * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample 碌_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
return pred_prev_sample
def forward_step(self, original_sample, noise, t):
sqrt_alpha_prod = self.alphas_cumprod[t] ** 0.5
sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[t]) ** 0.5
noisy_sample = sqrt_alpha_prod * original_sample + sqrt_one_minus_alpha_prod * noise
return noisy_sample
def __len__(self):
return self.config.timesteps