Source: https://arxiv.org/abs/2312.14385 Introduction As generative AI continues to advance, models are evolving beyond text generation to include image and video synthesis capabilities. However, these multi-modal models come with unique systems-level challenges compared to traditional language models. A new paper from researchers at Meta and Harvard University provides the first in-depth analysis characterizing the system performance and implications of text-to-image (TTI) and text-to-video (TTV) generative AI models. Their analysis compares two main model architectures – Diffusion-based and Transformer-based – across eight representative models on dimensions like latency, computational intensity, and component breakdown. The researchers make several key observations about the distinct