  We present personalized residuals and localized attention-guided sampling for
efficient concept-driven generation using text-to-image diffusion models. Our
method first represents concepts by freezing the weights of a pretrained
text-conditioned diffusion model and learning low-rank residuals for a small
subset of the model's layers. The residual-based approach then directly enables
application of our proposed sampling technique, which applies the learned
residuals only in areas where the concept is localized via cross-attention and
applies the original diffusion weights in all other regions. Localized sampling
therefore combines the learned identity of the concept with the existing
generative prior of the underlying diffusion model. We show that personalized
residuals effectively capture the identity of a concept in ~3 minutes on a
single GPU without the use of regularization images and with fewer parameters
than previous models, and localized sampling allows using the original model as
strong prior for large parts of the image.
