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# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from statistics import NormalDist
import numpy as np
import torch
class EDMSDE:
def __init__(
self,
p_mean: float = -1.2,
p_std: float = 1.2,
sigma_max: float = 80.0,
sigma_min: float = 0.002,
):
self.gaussian_dist = NormalDist(mu=p_mean, sigma=p_std)
self.sigma_max = sigma_max
self.sigma_min = sigma_min
def sample_t(self, batch_size: int) -> torch.Tensor:
cdf_vals = np.random.uniform(size=(batch_size))
samples_interval_gaussian = [self.gaussian_dist.inv_cdf(cdf_val) for cdf_val in cdf_vals]
log_sigma = torch.tensor(samples_interval_gaussian, device="cuda")
return torch.exp(log_sigma)
def marginal_prob(self, x0: torch.Tensor, sigma: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
"""This is trivial in the base class, but may be used by derived classes in a more interesting way"""
return x0, sigma
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