  Text-to-image based object customization, aiming to generate images with the
same identity (ID) as objects of interest in accordance with text prompts and
reference images, has made significant progress. However, recent customizing
research is dominated by specialized tasks, such as human customization or
virtual try-on, leaving a gap in general object customization. To this end, we
introduce AnyMaker, an innovative zero-shot object customization framework
capable of generating general objects with high ID fidelity and flexible text
editability. The efficacy of AnyMaker stems from its novel general ID
extraction, dual-level ID injection, and ID-aware decoupling. Specifically, the
general ID extraction module extracts sufficient ID information with an
ensemble of self-supervised models to tackle the diverse customization tasks
for general objects. Then, to provide the diffusion UNet with the extracted ID
as much while not damaging the text editability in the generation process, we
design a global-local dual-level ID injection module, in which the global-level
semantic ID is injected into text descriptions while the local-level ID details
are injected directly into the model through newly added cross-attention
modules. In addition, we propose an ID-aware decoupling module to disentangle
ID-related information from non-ID elements in the extracted representations
for high-fidelity generation of both identity and text descriptions. To
validate our approach and boost the research of general object customization,
we create the first large-scale general ID dataset, Multi-Category
ID-Consistent (MC-IDC) dataset, with 315k text-image samples and 10k
categories. Experiments show that AnyMaker presents remarkable performance in
general object customization and outperforms specialized methods in
corresponding tasks. Code and dataset will be released soon.
