Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866915891195674624 |
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| author | Crowson, Katherine Baumann, Stefan Andreas Birch, Alex Abraham, Tanishq Mathew Kaplan, Daniel Z. Shippole, Enrico |
| author_facet | Crowson, Katherine Baumann, Stefan Andreas Birch, Alex Abraham, Tanishq Mathew Kaplan, Daniel Z. Shippole, Enrico |
| contents | We present the Hourglass Diffusion Transformer (HDiT), an image generative model that exhibits linear scaling with pixel count, supporting training at high-resolution (e.g. $1024 \times 1024$) directly in pixel-space. Building on the Transformer architecture, which is known to scale to billions of parameters, it bridges the gap between the efficiency of convolutional U-Nets and the scalability of Transformers. HDiT trains successfully without typical high-resolution training techniques such as multiscale architectures, latent autoencoders or self-conditioning. We demonstrate that HDiT performs competitively with existing models on ImageNet $256^2$, and sets a new state-of-the-art for diffusion models on FFHQ-$1024^2$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11605 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers Crowson, Katherine Baumann, Stefan Andreas Birch, Alex Abraham, Tanishq Mathew Kaplan, Daniel Z. Shippole, Enrico Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning We present the Hourglass Diffusion Transformer (HDiT), an image generative model that exhibits linear scaling with pixel count, supporting training at high-resolution (e.g. $1024 \times 1024$) directly in pixel-space. Building on the Transformer architecture, which is known to scale to billions of parameters, it bridges the gap between the efficiency of convolutional U-Nets and the scalability of Transformers. HDiT trains successfully without typical high-resolution training techniques such as multiscale architectures, latent autoencoders or self-conditioning. We demonstrate that HDiT performs competitively with existing models on ImageNet $256^2$, and sets a new state-of-the-art for diffusion models on FFHQ-$1024^2$. |
| title | Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2401.11605 |