| Copyright (c) 2024 Gilles Van De Vyver | |
| Based on work by Huawei Technologies Co., Ltd. | |
| Licensed under CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike 4.0 International) (the "License"); | |
| you may not use this file except in compliance with the License. | |
| You may obtain a copy of the License at | |
| https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode | |
| The code is released for academic research use only. For commercial use, please contact Huawei Technologies Co., Ltd. | |
| 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. | |
| Original project: guided-diffusion by Dhariwal and Nichol (OpenAI), available at https://github.com/openai/guided-diffusion, licensed under the MIT License. | |
| Modifications were made in the RePaint project by Lugmayr et al. (Huawei Technologies Co., Ltd.), available at https://github.com/andreas128/RePaint, licensed under CC BY-NC-SA 4.0. | |
| This project, EchoGAINS, by Van De Vyver et al. (Norwegian University of Science and Technology), is a modification of RePaint and is licensed under CC BY-NC-SA 4.0. | |
| If you use this work, please cite the original authors and the current authors. | |
| Dhariwal, Prafulla, and Alexander Nichol. "Diffusion models beat gans on image synthesis." Advances in neural information processing systems 34 (2021): 8780-8794. | |
| Lugmayr, Andreas, et al. "Repaint: Inpainting using denoising diffusion probabilistic models." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022. | |
| Van De Vyver, Gilles, et al. "Generative augmentations for improved cardiac ultrasound segmentation using diffusion models." arXiv preprint arXiv:2502.20100 (2025). | |