Abstract: Current research on text-conditional image generation shows parallel performance with ordinary painters but still has much room for improvement when compared to that of artist-ability paintings, which usually represent multilevel semantics by gathering features ofmultiple objects into one object. In a preliminary experiment, we confirm this and then seek the opinions of three groups of individuals with varying levels of art appreciation ability to determine the distinctions that exist between painters and artists. We then use these opinions to improve an artificial intelligence (AI) painting system from painter-level image generation toward artistic-level image generation. Specifically, we propose a multistage text-conditioned approach without any further pretraining to help the diffusion model (DM) move toward multilevel semantic representation in a generated image. Both machine and manual evaluations of the main experiment verify the effectiveness of our approach. In addition, different from previous onestage guidance, our method is able to control the extent to which features of an object are represented in a painting by controlling guiding steps between the different stages.

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