  This paper proposes a method for generating images of customized objects
specified by users. The method is based on a general framework that bypasses
the lengthy optimization required by previous approaches, which often employ a
per-object optimization paradigm. Our framework adopts an encoder to capture
high-level identifiable semantics of objects, producing an object-specific
embedding with only a single feed-forward pass. The acquired object embedding
is then passed to a text-to-image synthesis model for subsequent generation. To
effectively blend a object-aware embedding space into a well developed
text-to-image model under the same generation context, we investigate different
network designs and training strategies, and propose a simple yet effective
regularized joint training scheme with an object identity preservation loss.
Additionally, we propose a caption generation scheme that become a critical
piece in fostering object specific embedding faithfully reflected into the
generation process, while keeping control and editing abilities. Once trained,
the network is able to produce diverse content and styles, conditioned on both
texts and objects. We demonstrate through experiments that our proposed method
is able to synthesize images with compelling output quality, appearance
diversity, and object fidelity, without the need of test-time optimization.
Systematic studies are also conducted to analyze our models, providing insights
for future work.
