The paper presents a method for synthesizing photographic images from pixel-wise semantic layouts using a cascaded refinement network (CRN). The CRN is a feedforward convolutional network trained end-to-end with a regression loss, which does not rely on adversarial training. The network is designed to produce high-resolution images, with the ability to scale seamlessly to 2-megapixel resolution. The approach is evaluated on the Cityscapes and NYU datasets, demonstrating that the synthesized images are more realistic than those produced by alternative methods, including GAN-based approaches. The paper also introduces a diversity loss to generate a diverse set of images for each input layout. The results show that the CRN outperforms baselines in terms of realism and efficiency, suggesting its potential for advancing the field of image synthesis.The paper presents a method for synthesizing photographic images from pixel-wise semantic layouts using a cascaded refinement network (CRN). The CRN is a feedforward convolutional network trained end-to-end with a regression loss, which does not rely on adversarial training. The network is designed to produce high-resolution images, with the ability to scale seamlessly to 2-megapixel resolution. The approach is evaluated on the Cityscapes and NYU datasets, demonstrating that the synthesized images are more realistic than those produced by alternative methods, including GAN-based approaches. The paper also introduces a diversity loss to generate a diverse set of images for each input layout. The results show that the CRN outperforms baselines in terms of realism and efficiency, suggesting its potential for advancing the field of image synthesis.