This paper describes a new computational technique called composable diffusion, which allows scientists to simultaneously processing and generating arbitrary combinations of modalities.
The goal of the study is to develop a model that can handle any mixture of input data and generate various output values.
In pursuit of this goal, CoDi has been developed for simultaneously processing multiple different input data types.
The basic idea behind CoDi is to allow scientists to effectively control the speed and power of their machine learning algorithms by dynamically varying the number of inputs and outputs from a given data set.
This paper demonstrates the utility of this approach to data generation and predicts the future use of this technology in both prediction and training neural nets.
Paper summary: CoDi, an efficient cross-modal generation model for any-to-any generation with state-of-the-art quality
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