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  Text-to-Image Denoising Diffusion GANs is a text-to-image model
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- based on Denoising Diffusion GANs <https://arxiv.org/abs/2112.07804>.
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- The code is based on their official code <<https://nvlabs.github.io/denoising-diffusion-gan/>,
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  which is updated to support text conditioning. Many thanks to the authors of DDGAN for releasing
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  the code.
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- The provided models are trained on DiffusionDB <https://arxiv.org/abs/2210.14896>, which is a dataset that was synthetically
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  generated with Stable Diffusion, many thanks to the authors for releasing the dataset.
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- Models were trained on JURECA-DC supercomputer at Jülich Supercomputing Centre (JSC), many thanks for the compute provided to train the models.
 
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  Text-to-Image Denoising Diffusion GANs is a text-to-image model
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+ based on [Denoising Diffusion GANs](https://arxiv.org/abs/2112.07804>).
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+ The code is based on their official [code](https://nvlabs.github.io/denoising-diffusion-gan/),
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  which is updated to support text conditioning. Many thanks to the authors of DDGAN for releasing
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  the code.
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+ The provided models are trained on [Diffusion DB](https://arxiv.org/abs/2210.14896), which is a dataset that was synthetically
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  generated with Stable Diffusion, many thanks to the authors for releasing the dataset.
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+ Models were trained on [JURECA-DC](https://www.fz-juelich.de/en/news/archive/press-release/2021/2021-06-23-jureca-dc) supercomputer at Jülich Supercomputing Centre (JSC), many thanks for the compute provided to train the models.