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--- |
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tags: |
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- text-to-image |
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- layout-to-image |
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- stable-diffusion |
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- controlnet |
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license: agpl-3.0 |
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language: |
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- en |
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--- |
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<h1 style="font-size:1.5em; " align="center"> Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive (ICLR 2024) </h1> |
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<div align="center"> |
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[**Project Page**](https://yumengli007.github.io/ALDM/) **|** [**ArXiv**](https://arxiv.org/abs/2401.08815) **|** [**Code**](https://github.com/boschresearch/ALDM) |
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</div> |
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<div align="center"> |
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This model repo contains checkpoints trained on Cityscapes and ADE20K datasets using methods proposed in <a href="https://yumengli007.github.io/ALDM/">ALDM</a>. |
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For usage instructions, please refer to our <a href="https://github.com/boschresearch/ALDM">Github</a>. |
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</div align="center"> |
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## Model information |
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[ade20k_step9.ckpt](ade20k_step9.ckpt) and [cityscapes_step9.ckpt](cityscapes_step9.ckpt) are pretrained diffusion model weights for inference. |
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[encoder_epoch_50.pth](encoder_epoch_50.pth), [decoder_epoch_50_20cls.pth](decoder_epoch_50_20cls.pth) and [decoder_epoch_50_151cls.pth](decoder_epoch_50_151cls.pth) |
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are segmentation models used for disciminator intialization in training, |
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which are adopted from pretrained weights from [here](https://github.com/CSAILVision/semantic-segmentation-pytorch). |
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