“…Later, GeneGAN solved this problem by training latent feature blocks with paired images possessing adverse attributes, but the disadvantage of only one attribute being able to be exchanged is inconvenient for users expecting to achieve multiattributes transfer. DnaGAN [2,[32][33][34][35][36], ELEGANT [3] adopted iterative training strategy to realize the multiattribute disentangled representation but it demonstrated undesirable transferred and reconstructed efects with huge transformation of nonediting facial information and style deviation of target attribute as shown in Figure 3(a). Subsequently, the traditional image translation [4,19,20,25,26,[37][38][39] methods were created, but they often lead to some unnecessary outcomes, such as age, background changes, and so on.…”