ml-starflow/ACKNOWLEDGMENTS at main · apple/ml-starflow · GitHub

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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