Inference-Time Optimization of Prompt Embeddings in Diffusion Models: A Comparison of sep-CMA-ES and Adam
Abstract
Deep generative models have progressively improved image generation, particularly in terms of realism and semantic alignment. Despite this, there is still room for improvement, as illustrated by strategies such as prompt engineering. In this context, we studied the application of inference-time prompt-embedding optimization for text-to-image generation using Stable Diffusion XL Turbo. We compared the gradient-based Adam optimizer with the evolutionary sep-CMA-ES using a weighted objective that combined the LAION Aesthetic Predictor V2 and CLIPScore. sep-CMA-ES consistently achieved higher objective scores than Adam while requiring fewer computational resources. The findings indicate that evolutionary optimization is a robust method for exploring embedding spaces without the need for gradients or model fine-tuning.
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