DiffNAS: Bootstrapping Diffusion Models by Prompting for Better Architectures

• URL: http://arxiv.org/abs/2310.04750v2
• Date: Tue, 10 Oct 2023 03:22:31 GMT
• Title: DiffNAS: Bootstrapping Diffusion Models by Prompting for Better Architectures
• Authors: Wenhao Li, Xiu Su, Shan You, Fei Wang, Chen Qian, Chang Xu
• Abstract summary: We propose a base model search approach, denoted "DiffNAS" We leverage GPT-4 as a supernet to expedite the search, supplemented with a search memory to enhance the results. Rigorous experimentation corroborates that our algorithm can augment the search efficiency by 2 times under GPT-based scenarios.
• Score: 63.12993314908957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have recently exhibited remarkable performance on synthetic data. After a diffusion path is selected, a base model, such as UNet, operates as a denoising autoencoder, primarily predicting noises that need to be eliminated step by step. Consequently, it is crucial to employ a model that aligns with the expected budgets to facilitate superior synthetic performance. In this paper, we meticulously analyze the diffusion model and engineer a base model search approach, denoted "DiffNAS". Specifically, we leverage GPT-4 as a supernet to expedite the search, supplemented with a search memory to enhance the results. Moreover, we employ RFID as a proxy to promptly rank the experimental outcomes produced by GPT-4. We also adopt a rapid-convergence training strategy to boost search efficiency. Rigorous experimentation corroborates that our algorithm can augment the search efficiency by 2 times under GPT-based scenarios, while also attaining a performance of 2.82 with 0.37 improvement in FID on CIFAR10 relative to the benchmark IDDPM algorithm.

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