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Running on Zero
| # 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 __future__ import annotations | |
| from dataclasses import dataclass | |
| from typing import Optional | |
| import torch | |
| class LabelImageCondition: | |
| label: torch.Tensor | |
| def get_classifier_free_guidance_condition(self) -> LabelImageCondition: | |
| return LabelImageCondition(torch.zeros_like(self.label)) | |
| class DenoisePrediction: | |
| x0: torch.Tensor # clean data prediction | |
| eps: Optional[torch.Tensor] = None # noise prediction | |
| logvar: Optional[torch.Tensor] = None # log variance of noise prediction, can be used a confidence / uncertainty | |