176 lines
7.6 KiB
Python
176 lines
7.6 KiB
Python
# 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
|
|
|
|
# TODO(Patrick, Anton, Suraj) - make scheduler framework indepedent and clean-up a bit
|
|
from typing import Union
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
from ..configuration_utils import ConfigMixin, register_to_config
|
|
from .scheduling_utils import SchedulerMixin
|
|
|
|
|
|
class ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin):
|
|
"""
|
|
The variance exploding stochastic differential equation (SDE) scheduler.
|
|
|
|
:param snr: coefficient weighting the step from the model_output sample (from the network) to the random noise.
|
|
:param sigma_min: initial noise scale for sigma sequence in sampling procedure. The minimum sigma should mirror the
|
|
distribution of the data.
|
|
:param sigma_max: :param sampling_eps: the end value of sampling, where timesteps decrease progessively from 1 to
|
|
epsilon. :param correct_steps: number of correction steps performed on a produced sample. :param tensor_format:
|
|
"np" or "pt" for the expected format of samples passed to the Scheduler.
|
|
"""
|
|
|
|
@register_to_config
|
|
def __init__(
|
|
self,
|
|
num_train_timesteps=2000,
|
|
snr=0.15,
|
|
sigma_min=0.01,
|
|
sigma_max=1348,
|
|
sampling_eps=1e-5,
|
|
correct_steps=1,
|
|
tensor_format="pt",
|
|
):
|
|
# self.sigmas = None
|
|
# self.discrete_sigmas = None
|
|
#
|
|
# # setable values
|
|
# self.num_inference_steps = None
|
|
self.timesteps = None
|
|
|
|
self.set_sigmas(num_train_timesteps, sigma_min, sigma_max, sampling_eps)
|
|
|
|
self.tensor_format = tensor_format
|
|
self.set_format(tensor_format=tensor_format)
|
|
|
|
def set_timesteps(self, num_inference_steps, sampling_eps=None):
|
|
sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps
|
|
tensor_format = getattr(self, "tensor_format", "pt")
|
|
if tensor_format == "np":
|
|
self.timesteps = np.linspace(1, sampling_eps, num_inference_steps)
|
|
elif tensor_format == "pt":
|
|
self.timesteps = torch.linspace(1, sampling_eps, num_inference_steps)
|
|
else:
|
|
raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
|
|
|
|
def set_sigmas(self, num_inference_steps, sigma_min=None, sigma_max=None, sampling_eps=None):
|
|
sigma_min = sigma_min if sigma_min is not None else self.config.sigma_min
|
|
sigma_max = sigma_max if sigma_max is not None else self.config.sigma_max
|
|
sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps
|
|
if self.timesteps is None:
|
|
self.set_timesteps(num_inference_steps, sampling_eps)
|
|
|
|
tensor_format = getattr(self, "tensor_format", "pt")
|
|
if tensor_format == "np":
|
|
self.discrete_sigmas = np.exp(np.linspace(np.log(sigma_min), np.log(sigma_max), num_inference_steps))
|
|
self.sigmas = np.array([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])
|
|
elif tensor_format == "pt":
|
|
self.discrete_sigmas = torch.exp(torch.linspace(np.log(sigma_min), np.log(sigma_max), num_inference_steps))
|
|
self.sigmas = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])
|
|
else:
|
|
raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
|
|
|
|
def get_adjacent_sigma(self, timesteps, t):
|
|
tensor_format = getattr(self, "tensor_format", "pt")
|
|
if tensor_format == "np":
|
|
return np.where(timesteps == 0, np.zeros_like(t), self.discrete_sigmas[timesteps - 1])
|
|
elif tensor_format == "pt":
|
|
return torch.where(
|
|
timesteps == 0, torch.zeros_like(t), self.discrete_sigmas[timesteps - 1].to(timesteps.device)
|
|
)
|
|
|
|
raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
|
|
|
|
def set_seed(self, seed):
|
|
tensor_format = getattr(self, "tensor_format", "pt")
|
|
if tensor_format == "np":
|
|
np.random.seed(seed)
|
|
elif tensor_format == "pt":
|
|
torch.manual_seed(seed)
|
|
else:
|
|
raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
|
|
|
|
def step_pred(
|
|
self,
|
|
model_output: Union[torch.FloatTensor, np.ndarray],
|
|
timestep: int,
|
|
sample: Union[torch.FloatTensor, np.ndarray],
|
|
seed=None,
|
|
):
|
|
"""
|
|
Predict the sample at the previous timestep by reversing the SDE.
|
|
"""
|
|
if seed is not None:
|
|
self.set_seed(seed)
|
|
# TODO(Patrick) non-PyTorch
|
|
|
|
timestep = timestep * torch.ones(
|
|
sample.shape[0], device=sample.device
|
|
) # torch.repeat_interleave(timestep, sample.shape[0])
|
|
timesteps = (timestep * (len(self.timesteps) - 1)).long()
|
|
|
|
sigma = self.discrete_sigmas[timesteps].to(sample.device)
|
|
adjacent_sigma = self.get_adjacent_sigma(timesteps, timestep)
|
|
drift = self.zeros_like(sample)
|
|
diffusion = (sigma**2 - adjacent_sigma**2) ** 0.5
|
|
|
|
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
|
|
# also equation 47 shows the analog from SDE models to ancestral sampling methods
|
|
drift = drift - diffusion[:, None, None, None] ** 2 * model_output
|
|
|
|
# equation 6: sample noise for the diffusion term of
|
|
noise = self.randn_like(sample)
|
|
prev_sample_mean = sample - drift # subtract because `dt` is a small negative timestep
|
|
# TODO is the variable diffusion the correct scaling term for the noise?
|
|
prev_sample = prev_sample_mean + diffusion[:, None, None, None] * noise # add impact of diffusion field g
|
|
|
|
return {"prev_sample": prev_sample, "prev_sample_mean": prev_sample_mean}
|
|
|
|
def step_correct(
|
|
self,
|
|
model_output: Union[torch.FloatTensor, np.ndarray],
|
|
sample: Union[torch.FloatTensor, np.ndarray],
|
|
seed=None,
|
|
):
|
|
"""
|
|
Correct the predicted sample based on the output model_output of the network. This is often run repeatedly
|
|
after making the prediction for the previous timestep.
|
|
"""
|
|
if seed is not None:
|
|
self.set_seed(seed)
|
|
|
|
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
|
|
# sample noise for correction
|
|
noise = self.randn_like(sample)
|
|
|
|
# compute step size from the model_output, the noise, and the snr
|
|
grad_norm = self.norm(model_output)
|
|
noise_norm = self.norm(noise)
|
|
step_size = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
|
|
step_size = step_size * torch.ones(sample.shape[0]).to(sample.device)
|
|
# self.repeat_scalar(step_size, sample.shape[0])
|
|
|
|
# compute corrected sample: model_output term and noise term
|
|
prev_sample_mean = sample + step_size[:, None, None, None] * model_output
|
|
prev_sample = prev_sample_mean + ((step_size * 2) ** 0.5)[:, None, None, None] * noise
|
|
|
|
return {"prev_sample": prev_sample}
|
|
|
|
def __len__(self):
|
|
return self.config.num_train_timesteps
|