Summary of Scalable, Tokenization-free Diffusion Model Architectures with Efficient Initial Convolution and Fixed-size Reusable Structures For On-device Image Generation, by Sanchar Palit et al.
Scalable, Tokenization-Free Diffusion Model Architectures with Efficient Initial Convolution and Fixed-Size Reusable Structures for On-Device Image Generation
by Sanchar Palit, Sathya Veera Reddy Dendi, Mallikarjuna Talluri, Raj Narayana Gadde
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an architecture that combines Vision Transformers and U-Net to implement Diffusion Models on-device, addressing challenges posed by these architectures. By using fixed-size, reusable transformer blocks, the proposed model achieves low complexity, token-free design, absence of positional embeddings, uniformity, and scalability, making it suitable for deployment on mobile and resource-constrained devices. The architecture is shown to exhibit competitive and consistent performance across both unconditional and conditional image generation tasks, achieving a state-of-the-art FID score of 1.6 on unconditional image generation with the CelebA dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computers better at creating realistic images without needing powerful machines. It combines two popular ideas in computer vision to create a new way of doing this. The new approach is designed to work well even when computers don’t have much power or memory, which could be useful for things like smartphones and self-driving cars. The results show that this new method can create good images quickly and efficiently. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Token » Transformer
