  The strength of modern generative models lies in their ability to be
controlled through text-based prompts. Typical "hard" prompts are made from
interpretable words and tokens, and must be hand-crafted by humans. There are
also "soft" prompts, which consist of continuous feature vectors. These can be
discovered using powerful optimization methods, but they cannot be easily
interpreted, re-used across models, or plugged into a text-based interface.
  We describe an approach to robustly optimize hard text prompts through
efficient gradient-based optimization. Our approach automatically generates
hard text-based prompts for both text-to-image and text-to-text applications.
In the text-to-image setting, the method creates hard prompts for diffusion
models, allowing API users to easily generate, discover, and mix and match
image concepts without prior knowledge on how to prompt the model. In the
text-to-text setting, we show that hard prompts can be automatically discovered
that are effective in tuning LMs for classification.
