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StyleGAN 2 is a generative adversarial network (GAN) that is an improvement over StyleGAN. StyleGAN 2 addresses the shortcomings of StyleGAN, such as artifacts and instability. differences between StyleGAN and StyleGAN 2 : Weight demodulation instead of AdaIN: StyleGAN 2 uses weight demodulation instead of AdaIN to improve the quality of generated images. Weight demodulation is a technique that scales the weights of the generator network based on the style vectors. This allows StyleGAN 2 to generate images with a higher level of detail and realism. Residual connections instead of progressive growing: StyleGAN 2 uses residual connections instead of progressive growing to improve the stability of training. Residual connections are a technique that adds shortcuts between layers of the generator network. This allows StyleGAN 2 to train more smoothly and generate images with less artifacts. Improved training stability overall: StyleGAN 2 has improved training stability overall. This is due to a number of factors, including the use of weight demodulation and residual connections. As a result, StyleGAN 2 is more likely to converge to a good solution and generate high-quality images. Overall, StyleGAN 2 is a significant improvement over StyleGAN. It addresses the shortcomings of StyleGAN and generates higher-quality images |