  Text-guided diffusion models (TDMs) are widely applied but can fail
unexpectedly. Common failures include: (i) natural-looking text prompts
generating images with the wrong content, or (ii) different random samples of
the latent variables that generate vastly different, and even unrelated,
outputs despite being conditioned on the same text prompt. In this work, we aim
to study and understand the failure modes of TDMs in more detail. To achieve
this, we propose SAGE, the first adversarial search method on TDMs that
systematically explores the discrete prompt space and the high-dimensional
latent space, to automatically discover undesirable behaviors and failure cases
in image generation. We use image classifiers as surrogate loss functions
during searching, and employ human inspections to validate the identified
failures. For the first time, our method enables efficient exploration of both
the discrete and intricate human language space and the challenging latent
space, overcoming the gradient vanishing problem. Then, we demonstrate the
effectiveness of SAGE on five widely used generative models and reveal four
typical failure modes: (1) We find a variety of natural text prompts that
generate images failing to capture the semantics of input texts. We further
discuss the underlying causes and potential solutions based on the results. (2)
We find regions in the latent space that lead to distorted images independent
of the text prompt, suggesting that parts of the latent space are not
well-structured. (3) We also find latent samples that result in natural-looking
images unrelated to the text prompt, implying a possible misalignment between
the latent and prompt spaces. (4) By appending a single adversarial token
embedding to any input prompts, we can generate a variety of specified target
objects. Project page: https://sage-diffusion.github.io/
