FlexEControl: Flexible and Efficient Multimodal Control for Text-to-Image Generation
Abstract: Controllable text-to-image (T2I) diffusion models generate images conditioned on both text
prompts and semantic inputs of other modalities like edge maps. Nevertheless, current
controllable T2I methods commonly face challenges related to efficiency and faithfulness,
especially when conditioning on multiple inputs from either the same or diverse modalities. In
this paper, we propose a novel Flexible and Efficient method, FlexEControl, for controllable
T2I generation. At the core of FlexEControl is a unique weight decomposition strategy, which
allows for streamlined integration of various input types. This approach not only enhances
the faithfulness of the generated image to the control, but also significantly reduces the
computational overhead typically associated with multimodal conditioning. Our approach
achieves a reduction of 41% in trainable parameters and 30% in memory usage compared
with Uni-ControlNet. Moreover, it doubles data efficiency and can flexibly generate images
under the guidance of multiple input conditions of various modalities.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Hongsheng_Li3
Submission Number: 2559
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