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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 typing import Tuple | |
| import torch | |
| class EDMScaling: | |
| def __init__(self, sigma_data: float = 0.5): | |
| self.sigma_data = sigma_data | |
| def __call__(self, sigma: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: | |
| c_skip = self.sigma_data**2 / (sigma**2 + self.sigma_data**2) | |
| c_out = sigma * self.sigma_data / (sigma**2 + self.sigma_data**2) ** 0.5 | |
| c_in = 1 / (sigma**2 + self.sigma_data**2) ** 0.5 | |
| c_noise = 0.25 * sigma.log() | |
| return c_skip, c_out, c_in, c_noise | |