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<h2><a href="https://github.com/sczhou/CodeFormer/blob/master/LICENSE">CodeFormer</a></h2> | |
<small>Parts of CodeFormer code had to be copied to be compatible with GFPGAN.</small> | |
<pre> | |
S-Lab License 1.0 | |
Copyright 2022 S-Lab | |
Redistribution and use for non-commercial purpose in source and | |
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<h2><a href="https://github.com/victorca25/iNNfer/blob/main/LICENSE">ESRGAN</a></h2> | |
<small>Code for architecture and reading models copied.</small> | |
<pre> | |
MIT License | |
Copyright (c) 2021 victorca25 | |
Permission is hereby granted, free of charge, to any person obtaining a copy | |
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</pre> | |
<h2><a href="https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE">Real-ESRGAN</a></h2> | |
<small>Some code is copied to support ESRGAN models.</small> | |
<pre> | |
BSD 3-Clause License | |
Copyright (c) 2021, Xintao Wang | |
All rights reserved. | |
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<h2><a href="https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE">InvokeAI</a></h2> | |
<small>Some code for compatibility with OSX is taken from lstein's repository.</small> | |
<pre> | |
MIT License | |
Copyright (c) 2022 InvokeAI Team | |
Permission is hereby granted, free of charge, to any person obtaining a copy | |
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</pre> | |
<h2><a href="https://github.com/Hafiidz/latent-diffusion/blob/main/LICENSE">LDSR</a></h2> | |
<small>Code added by contirubtors, most likely copied from this repository.</small> | |
<pre> | |
MIT License | |
Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich | |
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</pre> | |
<h2><a href="https://github.com/pharmapsychotic/clip-interrogator/blob/main/LICENSE">CLIP Interrogator</a></h2> | |
<small>Some small amounts of code borrowed and reworked.</small> | |
<pre> | |
MIT License | |
Copyright (c) 2022 pharmapsychotic | |
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</pre> | |
<h2><a href="https://github.com/JingyunLiang/SwinIR/blob/main/LICENSE">SwinIR</a></h2> | |
<small>Code added by contributors, most likely copied from this repository.</small> | |
<pre> | |
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<h2><a href="https://github.com/AminRezaei0x443/memory-efficient-attention/blob/main/LICENSE">Memory Efficient Attention</a></h2> | |
<small>The sub-quadratic cross attention optimization uses modified code from the Memory Efficient Attention package that Alex Birch optimized for 3D tensors. This license is updated to reflect that.</small> | |
<pre> | |
MIT License | |
Copyright (c) 2023 Alex Birch | |
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<h2><a href="https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/LICENSE">Scaled Dot Product Attention</a></h2> | |
<small>Some small amounts of code borrowed and reworked.</small> | |
<pre> | |
Copyright 2023 The HuggingFace Team. All rights reserved. | |
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Apache License | |
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</pre> | |
<h2><a href="https://github.com/explosion/curated-transformers/blob/main/LICENSE">Curated transformers</a></h2> | |
<small>The MPS workaround for nn.Linear on macOS 13.2.X is based on the MPS workaround for nn.Linear created by danieldk for Curated transformers</small> | |
<pre> | |
The MIT License (MIT) | |
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</pre> | |
<h2><a href="https://github.com/madebyollin/taesd/blob/main/LICENSE">TAESD</a></h2> | |
<small>Tiny AutoEncoder for Stable Diffusion option for live previews</small> | |
<pre> | |
MIT License | |
Copyright (c) 2023 Ollin Boer Bohan | |
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</pre> |