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In addition to the unprecedented ability in imaginary creation, largetext-to-image models are expected to take customized concepts in imagegeneration. Existing works generally learn such concepts in anoptimization-based manner, yet bringing excessive computation or memory burden.In this paper, we instead propose a learning-based encoder, which consists of aglobal and a local mapping networks for fast and accurate customizedtext-to-image generation. In specific, the global mapping network projects thehierarchical features of a given image into multiple new words in the textualword embedding space, i.e., one primary word for well-editable concept andother auxiliary words to exclude irrelevant disturbances (e.g., background). Inthe meantime, a local mapping network injects the encoded patch features intocross attention layers to provide omitted details, without sacrificing theeditability of primary concepts. We compare our method with existingoptimization-based approaches on a variety of user-defined concepts, anddemonstrate that our method enables high-fidelity inversion and more robusteditability with a significantly faster encoding process. Our code is publiclyavailable at https://github.com/csyxwei/ELITE.

ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation