Scalable, Tokenization-Free Diffusion Model Architectures with Efficient Initial Convolution and Fixed-Size Reusable Structures for On-Device Image Generation

• URL: http://arxiv.org/abs/2411.06119v1
• Date: Sat, 09 Nov 2024 08:58:57 GMT
• Title: Scalable, Tokenization-Free Diffusion Model Architectures with Efficient Initial Convolution and Fixed-Size Reusable Structures for On-Device Image Generation
• Authors: Sanchar Palit, Sathya Veera Reddy Dendi, Mallikarjuna Talluri, Raj Narayana Gadde,
• Abstract summary: Vision Transformers and U-Net architectures have been widely adopted in the implementation of Diffusion Models. We propose an architecture that utilizes a fixed-size, reusable transformer block as a core structure. Our architecture is characterized by low complexity, token-free design, absence of positional embeddings, uniformity, and scalability.
• Score: 0.0
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• Abstract: Vision Transformers and U-Net architectures have been widely adopted in the implementation of Diffusion Models. However, each architecture presents specific challenges while realizing them on-device. Vision Transformers require positional embedding to maintain correspondence between the tokens processed by the transformer, although they offer the advantage of using fixed-size, reusable repetitive blocks following tokenization. The U-Net architecture lacks these attributes, as it utilizes variable-sized intermediate blocks for down-convolution and up-convolution in the noise estimation backbone for the diffusion process. To address these issues, we propose an architecture that utilizes a fixed-size, reusable transformer block as a core structure, making it more suitable for hardware implementation. Our architecture is characterized by low complexity, token-free design, absence of positional embeddings, uniformity, and scalability, making it highly suitable for deployment on mobile and resource-constrained devices. The proposed model exhibit competitive and consistent performance across both unconditional and conditional image generation tasks. The model achieved a state-of-the-art FID score of 1.6 on unconditional image generation with the CelebA.

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