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---
tags:
- text-to-image
- layout-to-image
- stable-diffusion
- controlnet
license: agpl-3.0
language:
- en
---
<h1 style="font-size:1.5em; " align="center"> Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive (ICLR 2024) </h1>
<div align="center">
[**Project Page**](https://yumengli007.github.io/ALDM/) **|** [**ArXiv**](https://arxiv.org/abs/2401.08815) **|** [**Code**](https://github.com/boschresearch/ALDM)
</div>
<div align="center">
This model repo contains checkpoints trained on Cityscapes and ADE20K datasets using methods proposed in <a href="https://yumengli007.github.io/ALDM/">ALDM</a>.
For usage instructions, please refer to our <a href="https://github.com/boschresearch/ALDM">Github</a>.
</div align="center">
## Model information
[ade20k_step9.ckpt](ade20k_step9.ckpt) and [cityscapes_step9.ckpt](cityscapes_step9.ckpt) are pretrained diffusion model weights for inference.
[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)
are segmentation models used for discriminator intialization in training,
which are adopted from pretrained UperNet101 [here](https://github.com/CSAILVision/semantic-segmentation-pytorch).
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