GENERATIVE AI

Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation propose Make-an-Audio 2, a latent diffusion-based T2A method that builds on the success of Make-an-Audio. Our approach includes several techniques to improve semantic alignment and temporal consistency: Firstly, we use

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Nested Diffusion Processes for Anytime Image Generation propose an anytime diffusion-based method that can generate viable images when stopped at arbitrary times before completion. Using existing pretrained diffusion models, we show that the generation scheme can be recomposed

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Real-World Image Variation by Aligning Diffusion Inversion Chain propose a novel inference pipeline called Real-world Image Variation by ALignment (RIVAL) that utilizes diffusion models to generate image variations from a single image exemplar. Our pipeline enhances the

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