# Editable Image Generation with Consistent Unsupervised Disentanglement Based on GAN
## 1 Introduction  
In recent years, many frameworks for representing high-dimensional data in an unsupervised manner have proliferated. Among many deep generative methods, generative adversarial networks (GANs) are among the most prominent methods for synthesizing real images. Generative Adversarial Network (GAN) has many meaningful works in high-resolution graph generation [1; 2; 3; 4; 5]. The emergence of GANs has made a huge leap in modeling high-dimensional distributed data, which has attracted great research interest. It consists of two networks: a generator and a discriminator, where the generator tries to map the latent data to the real data distribution. However, these data are often highly diverse. Therefore, the training of datasets often brings various model training collapse, both in terms of reduced model generation quality and generation diversity. To solve this problem, there are three main options: (1) modifying the model architecture to change different architectures for different situations (e.g., AdaGAN [6], D2 GAN [7]); (2) achieving better data mapping for the required generative goals by modifying the loss function (e.g., WGAN [8], LSGAN [9], EBAGN [10]); or (3) modifying the hidden space (DeLiGAN [11]). We focus on the model architecture, and we modify the original learning paradigm. This approach does not require large modifications to the original model, which greatly broadens the ease of use and applicability, and allows faster convergence to an equilibrium point. Such a modified approach makes the model more entangled in the latent space. To cope with this problem, [PERSON] [12] disassembled the first fully connected layer of the GAN to disentanglement, and it is this layer of disentanglement that makes the overall disentanglement capability very limited. The Hessian GAN [13] minimizes the non-diagonalterms of the Hessian matrix to better learn disentanglement, and this method treats each output independently but lacks holistic constraint, so it lacks competitiveness in spatial representation. To solve this problem, Jacobian (OroJaR) [14] computes the Jacobian matrix of outputs to represent the changes caused by potential inputs, constraining the Jacobian vectors of each dimension to be orthogonal, so it can make it possible to compensate for this problem. Still, this method lacks the hessian penalty for effective access to the second-order of the original model information and weakens the output diversity. We demonstrate the intrinsic connection between these two methods and propose a new method that allows the model to have a clear separation boundary and effective spatial separation. The mode collapse of the generative adversarial network has existed since the model was produced. Some scholars' works [15; 16; 17; 18] make the model better to avoid this problem. For using these methods [15; 16; 17; 18], the original model needs to be significantly changed, so we propose our method to mitigate mode collapse. The method we propose to mitigate mode collapse is dedicated to further reducing the probability of mode collapse based on the original model, which we mainly show through loss line graphs and evaluation metrics. Moreover, our model can mitigate mode collapse without increasing the training burden. Alleviating model collapse is only the first step in practical training. To generate higher-quality and controllable images, disentanglement is necessary. Some scholars' works [19; 20; 21; 22; 23] have done many works, but they do not perform well in disentanglement with multiple objects and complex scenes, so we propose our disentanglement method. Our method outperforms previous disentanglement performances for multiple objects and complex scenes. In summary, we propose a new training framework that improves model stability and disentanglement with brief modifications to the model. Our approach allows training the model without getting into a local Nash equilibrium problem and doing the model as a disentanglement process.