SlowGuess/NeurIPS_2023_OCR huggingface dataset:
We download the checkpoint of the pre-trained StyleGAN-2 models with Adaptive Data Augmentation (ADA)[29] from the official website. We pre-train a StyleGAN-2 model [30] following the same configuration of the original paper. All models converge with adversarial training under different settings (conditional or unconditional with different data augmentation strategies). We initialize the generator with the weights of pre-trained GAN generators. We initialize the DM  $s_{\phi}$  for implicit distribution with the same weights as the pre-trained DMs. We then use the Diff-Instruct with pre-trained DMs to improve the generator. For both the implicit DM  $s_{\phi}$  and the generator  $g_{\theta}$ , we use the Adam optimizer to update the parameters. For the generator, we use the same exponential moving average technique as the official implementation of StyleGAN-ADA with Pytorch. We put detailed hyper-parameters for each experiment in Table 9.
<sup>7</sup>https://github.com/NVlabs/edm
$^{8}$ https://github.com/NVlabs/stylegan2-ada-pytorch