  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.
