  Text-to-image generative models have made remarkable advancements in
generating high-quality images. However, generated images often contain
undesirable artifacts or other errors due to model limitations. Existing
techniques to fine-tune generated images are time-consuming (manual editing),
produce poorly-integrated results (inpainting), or result in unexpected changes
across the entire image (variation selection and prompt fine-tuning). In this
work, we present Diffusion Brush, a Latent Diffusion Model-based (LDM) tool to
efficiently fine-tune desired regions within an AI-synthesized image. Our
method introduces new random noise patterns at targeted regions during the
reverse diffusion process, enabling the model to efficiently make changes to
the specified regions while preserving the original context for the rest of the
image. We evaluate our method's usability and effectiveness through a user
study with artists, comparing our technique against other state-of-the-art
image inpainting techniques and editing software for fine-tuning AI-generated
imagery.
