QC-StyleGAN - Quality Controllable Image Generation and Manipulation - Supplementary Material - Dat Viet Thanh Nguyen 1, Phong Tran 1,2, T an M. Dinh 1 Anh T uan Tran

Table 1: Hyperparameters used in each model training.Parameter FFHQ AFHQ-Cat LSUN-Church Resolution 256 256 512 512 256 256 Number of GPUs 8 8 8 Training length 5M 5M 5M Minibatch size 64 64 64 Minibatch stddev 8 8 8 Feature maps Resnet D Training time 1.5 days 3.5 days 1.5 days Finally, we consider the entire DegradBlock: DB (f, k q) = π (ϕ ( r Thus, the lemma in Equation 10 holds. Karras et al., from which we inherit most of the training details, including weight demodulation, path We also report other details for each training in Table 1. For the image restoration task, we train pSp and run PTI on a single Nvidia V100. First, our reconstructed images can easily be converted to their sharp version. Moreover, since QC-StyleGAN models both sharp and degraded images, the inversion results often stay in-distribution, allowing good editing results.