  Typical layout-to-image synthesis (LIS) models generate images for a closed
set of semantic classes, e.g., 182 common objects in COCO-Stuff. In this work,
we explore the freestyle capability of the model, i.e., how far can it generate
unseen semantics (e.g., classes, attributes, and styles) onto a given layout,
and call the task Freestyle LIS (FLIS). Thanks to the development of
large-scale pre-trained language-image models, a number of discriminative
models (e.g., image classification and object detection) trained on limited
base classes are empowered with the ability of unseen class prediction.
Inspired by this, we opt to leverage large-scale pre-trained text-to-image
diffusion models to achieve the generation of unseen semantics. The key
challenge of FLIS is how to enable the diffusion model to synthesize images
from a specific layout which very likely violates its pre-learned knowledge,
e.g., the model never sees "a unicorn sitting on a bench" during its
pre-training. To this end, we introduce a new module called Rectified
Cross-Attention (RCA) that can be conveniently plugged in the diffusion model
to integrate semantic masks. This "plug-in" is applied in each cross-attention
layer of the model to rectify the attention maps between image and text tokens.
The key idea of RCA is to enforce each text token to act on the pixels in a
specified region, allowing us to freely put a wide variety of semantics from
pre-trained knowledge (which is general) onto the given layout (which is
specific). Extensive experiments show that the proposed diffusion network
produces realistic and freestyle layout-to-image generation results with
diverse text inputs, which has a high potential to spawn a bunch of interesting
applications. Code is available at https://github.com/essunny310/FreestyleNet.
