High Quality Image Generation using Improved Generative Adversarial Networks and Optimized Stable Diffusion Models
Abstract
Abstract Generative adversarial networks (GANs) demonstrated significant promise in generating synthetic images. GANs involves dual deep neural networks; the generator for generating realistic and high-quality new images, whereas the discriminator for classifying these images. GAN generator sometimes produces limited image variations and requires hyperparameters tuning. Diffusion models also involves dual processes; the forward process for adding pure noise and the reverse process for reconstructing the original images. Individually the diffusion models are stable during training as compared to GANs, with less risk of mode collapse. However, training GANs with stable diffusion leads to two issues namely model collapse and non-convergence. In this paper, these two issues are addressed by combining GAN and stable diffusion to rework the network architecture to obtain a more powerful model. GANs and stable diffusion-based model architecture is designed and implemented for high-quality image generation and analysis. The GANs and stable diffusion are developed for the interpretation of the images using architecture such as stable-diffusion-2-1, stable-diffusion-2-base, GAN and unsupervised text to image translation models. Along with existing architecture, the performance analysis are also carried out by incorporating additional hybrid architecture. The CLIP scores and Real or Fake scores metrics are used to evaluate stable diffusion and GAN performance respectively. The proposed…
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