Abstract: A method for training a GAN to transfer lighting from a reference image to a source image includes: receiving the source image and the reference image; generating a lighting vector from the reference image; applying features of the source image and the lighting vector to a generative network of the GAN to create a generated image; applying features of the reference image and the lighting vector to a discriminative network of the GAN to update weights of the discriminative network; and applying features of the generated image and the lighting vector to the discriminative network to update weights of the generative network.

Abstract: Systems and methods for generating images using hybrid sampling include obtaining a noisy image and generating a first denoised image during a first reverse diffusion phase using a diffusion neural network. The first denoised image is generated based on a first sampler that uses a first sampling density during at least a portion of the first reverse diffusion phase. Subsequently, a second denoised image is generated based on the first denoised image during a second reverse diffusion phase using the diffusion neural network. The second denoised image is generated based on a second sampler that uses a second sampling density different from the first sampling density during at least a portion of the second reverse diffusion phase.

Abstract: A method for training a GAN to transfer lighting from a reference image to a source image includes: receiving the source image and the reference image; generating a lighting vector from the reference image; applying features of the source image and the lighting vector to a generative network of the GAN to create a generated image; applying features of the reference image and the lighting vector to a discriminative network of the GAN to update weights of the discriminative network; and applying features of the generated image and the lighting vector to the discriminative network to update weights of the generative network.