  Inspired by the software industry's practice of offering different editions
or versions of a product tailored to specific user groups or use cases, we
propose a novel task, namely, training-free editioning, for text-to-image
models. Specifically, we aim to create variations of a base text-to-image model
without retraining, enabling the model to cater to the diverse needs of
different user groups or to offer distinct features and functionalities. To
achieve this, we propose that different editions of a given text-to-image model
can be formulated as concept subspaces in the latent space of its text encoder
(e.g., CLIP). In such a concept subspace, all points satisfy a specific user
need (e.g., generating images of a cat lying on the grass/ground/falling
leaves). Technically, we apply Principal Component Analysis (PCA) to obtain the
desired concept subspaces from representative text embedding that correspond to
a specific user need or requirement. Projecting the text embedding of a given
prompt into these low-dimensional subspaces enables efficient model editioning
without retraining. Intuitively, our proposed editioning paradigm enables a
service provider to customize the base model into its "cat edition" (or other
editions) that restricts image generation to cats, regardless of the user's
prompt (e.g., dogs, people, etc.). This introduces a new dimension for product
differentiation, targeted functionality, and pricing strategies, unlocking
novel business models for text-to-image generators. Extensive experimental
results demonstrate the validity of our approach and its potential to enable a
wide range of customized text-to-image model editions across various domains
and applications.
