Get What You Want, Not What You Don't: Image Content Suppression for
  Text-to-Image Diffusion Models
Senmao Li Joost van de Weijer Taihang Hu Fahad Shahbaz Khan Qibin Hou Yaxing Wang Jian Yang
The success of recent text-to-image diffusion models is largely due to their
capacity to be guided by a complex text prompt, which enables users to
precisely describe the desired content. However, these models struggle to
effectively suppress the generation of undesired content, which is explicitly
requested to be omitted from the generated image in the prompt. In this paper,
we analyze how to manipulate the text embeddings and remove unwanted content
from them. We introduce two contributions, which we refer to as
$\textit{soft-weighted regularization}$ and $\textit{inference-time text
embedding optimization}$. The first regularizes the text embedding matrix and
effectively suppresses the undesired content. The second method aims to further
suppress the unwanted content generation of the prompt, and encourages the
generation of desired content. We evaluate our method quantitatively and
qualitatively on extensive experiments, validating its effectiveness.
Furthermore, our method is generalizability to both the pixel-space diffusion
models (i.e. DeepFloyd-IF) and the latent-space diffusion models (i.e. Stable
Diffusion).