The increasing use of generative models in everyday life requires effective methods to control their generation, for example, to display safe content or provide users with tools to test style changes. Ideally, such methods should require a low volume of inappropriate data (ie, without clear selection), and should be cheap, time to save, while maintaining the quality of the output, while maintaining the quality of the output. Recent studies have shown that such methods can be found with effective interventions, with the aim of correcting the difference distributed between the performance of vs. In addition to collecting more, the loss used to train the lines can be automatically selected by the neuron. It only needs a few negative samples to work, and it beats the same bastelines in the language models, which are dependent on the methods that have strong access. including new concepts in the output of one-step-to-image models.
- ‡ Equal contribution
- † Sapienza University of Rome
- ** Work Done while at Apple
