SPADE is a semantic image synthesis framework and generative adversarial network designed to transform semantic label maps into photorealistic images. It uses a spatially-adaptive normalization model to modulate activations based on semantic maps, ensuring that spatial layouts and details are preserved throughout the synthesis process.
The project enables the generation of diverse image variations from a single semantic layout by integrating variational autoencoders and latent vector style control. These mechanisms allow for the adjustment of visual appearances and textures while keeping the underlying structural layout constant.
The system includes workflows for training synthesis models on custom image and label pairs and supports reverse mapping training to convert photos back into semantic maps. Training is supported by multi-GPU distributed acceleration to handle high-resolution image generation.
