Download ACKNOWLEDGMENTS from Lingmin-Ran/SADAE: direct link, hf CLI and curl.
- Browser
- Download file 2.25 kB
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https://huggingface.co/Lingmin-Ran/SADAE/resolve/main/ACKNOWLEDGMENTS
- Command line
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hf download hf://Lingmin-Ran/SADAE/ACKNOWLEDGMENTS
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curl -L -o ACKNOWLEDGMENTS https://huggingface.co/Lingmin-Ran/SADAE/resolve/main/ACKNOWLEDGMENTS
2.25 kB
| Acknowledgements | |
| Portions of this STARFLOW Software may utilize the following copyrighted | |
| material, the use of which is hereby acknowledged. | |
| _____________________ | |
| The Alibaba Wan Team Authors (Wan2.2) | |
| Copyright 2024-2025 The Alibaba Wan Team Authors. 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. | |
| Portions of the VAE implementation in misc/wan_vae2.py are based on or adapted from | |
| the Wan2.2 project (https://github.com/Wan-Video/Wan2.2). | |
| ByteDance Ltd. (1D-Tokenizer) | |
| 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. | |
| Portions of the loss functions and discriminator implementations in misc/ae_losses.py | |
| and misc/discriminator.py are based on or adapted from the 1D-Tokenizer project | |
| (https://github.com/bytedance/1d-tokenizer). | |
| CompVis (Taming Transformers) | |
| Portions of the LPIPS implementation in misc/lpips.py are based on or adapted from | |
| the Taming Transformers project. References: | |
| - https://github.com/CompVis/taming-transformers/blob/master/taming/modules/losses/lpips.py | |
| - https://github.com/CompVis/taming-transformers/blob/master/taming/util.py | |
| - https://github.com/CompVis/taming-transformers/blob/master/taming/modules/losses/vqperceptual.py | |