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# Copyright 2022 Google Brain 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/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin
class ScoreSdeVpScheduler(SchedulerMixin, ConfigMixin):
"""
The variance preserving stochastic differential equation (SDE) scheduler.
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
[`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
[`~SchedulerMixin.from_pretrained`] functions.
For more information, see the original paper: https://arxiv.org/abs/2011.13456
UNDER CONSTRUCTION
"""
order = 1
@register_to_config
def __init__(self, num_train_timesteps=2000, beta_min=0.1, beta_max=20, sampling_eps=1e-3):
self.sigmas = None
self.discrete_sigmas = None
self.timesteps = None
def set_timesteps(self, num_inference_steps, device: Union[str, torch.device] = None):
self.timesteps = torch.linspace(1, self.config.sampling_eps, num_inference_steps, device=device)
def step_pred(self, score, x, t, generator=None):
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
)
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
log_mean_coeff = (
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
std = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
std = std.flatten()
while len(std.shape) < len(score.shape):
std = std.unsqueeze(-1)
score = -score / std
# compute
dt = -1.0 / len(self.timesteps)
beta_t = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
beta_t = beta_t.flatten()
while len(beta_t.shape) < len(x.shape):
beta_t = beta_t.unsqueeze(-1)
drift = -0.5 * beta_t * x
diffusion = torch.sqrt(beta_t)
drift = drift - diffusion**2 * score
x_mean = x + drift * dt
# add noise
noise = torch.randn(x.shape, layout=x.layout, generator=generator).to(x.device)
x = x_mean + diffusion * math.sqrt(-dt) * noise
return x, x_mean
def __len__(self):
return self.config.num_train_timesteps
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