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"The bigger the model, the better", Scaling Law was once again caught fire by OpenAI, but the latest research by researchers at Google falsifies this view.
In a study published Monday, researchers at Google Research and Johns Hopkins University have gained a new understanding of the efficiency of artificial intelligence (AI) models in image generation tasks. These findings challenge the common belief that "bigger is better" and could have a significant impact on the development of more efficient AI systems.
1. The contest between model size and performance
The study, led by researchers Kangfu Mei and Zhengzhong Tu, focused on the scaling characteristics of latent diffusion models (LDMs) and their sampling efficiency. LDM is an artificial intelligence model that is used to generate high-quality images based on textual descriptions.
To investigate the relationship between model size and performance, the researchers trained a set of 12 text-to-image LDMs with a number of parameters ranging from 39 million to a staggering 5 billion. The models were then evaluated on a variety of tasks, including text-to-image generation, super-resolution, and topic-driven composition.
"Although improved network architecture and inference algorithms have been shown to be effective in improving the sampling efficiency of diffusion models, the role of model size, a key determinant of sampling efficiency, has not been thoroughly examined. “
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Surprisingly, studies have shown that smaller models can outperform larger models when run at a given inference budget (the same sampling cost). In other words, when computational resources are limited, a more compact model may be able to produce higher-quality images than a larger, resource-intensive model. This provides a promising direction for accelerating LDMs at model scale.
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The paper further shows that the sampling efficiency is consistent across multiple dimensions. An important finding was that the sampling efficiency of the smaller model was consistent across various diffusion samplers (random and deterministic), even in the distillation model (a compressed version of the original model). This suggests that the advantages of smaller models are not limited to specific sampling techniques or model compression methods.
The researchers believe that this analysis of scaled sampling efficiency will play a key role in guiding the future development of LDMs, especially in balancing model size with performance and efficiency in a wide range of practical applications.
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However, the study also noted that larger models still excel at generating fine-grained details when computational constraints are relaxed. This suggests that while a smaller model may be more effective, there are still situations where a larger model is still needed.
2. Write at the end
The implications of this research are far-reaching, as it opens up new possibilities for the development of more efficient AI systems for image generation. By understanding the scaling nature of LDM and the trade-offs between model size and performance, researchers and developers can create AI models that strike a balance between efficiency and quality.
These findings are in line with the latest trend in the AI community that small language models such as LLaMa and Falcon outperform large language models in a variety of tasks. The push to build open-source, smaller, and more efficient models is to democratize the field of AI, enabling developers to build their own AI systems that can run on a single device without requiring large amounts of computing resources.
I have to say that in the field of GenAI, there is a bit of a feeling of "big business regardless of detail, big gifts do not give up".
Reference link: https://arxiv.org/pdf/2404.01367.pdf
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Source: 51CTO Technology Stack