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title: Text To Image DDGAN | |
emoji: 🐢 | |
colorFrom: red | |
colorTo: purple | |
sdk: gradio | |
sdk_version: 3.8.2 | |
app_file: app.py | |
pinned: false | |
Text-to-Image Denoising Diffusion GANs is a text-to-image model | |
based on [Denoising Diffusion GANs](https://arxiv.org/abs/2112.07804>). | |
The code is based on their official [code](https://nvlabs.github.io/denoising-diffusion-gan/), | |
which is updated to support text conditioning. Many thanks to the authors of DDGAN for releasing | |
the code. | |
The provided models are trained on [Diffusion DB](https://arxiv.org/abs/2210.14896), which is a dataset that was synthetically | |
generated with Stable Diffusion, many thanks to the authors for releasing the dataset. | |
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. | |