Cocktail: Mixing Multi-Modality Controls for Text-Conditional Image
  Generation
Authors: name: Minghui Hu, name: Jianbin Zheng, name: Daqing Liu, name: Chuanxia Zheng, name: Chaoyue Wang, name: Dacheng Tao, name: Tat-Jen Cham

Text-conditional diffusion models are able to generate high-fidelity images
with diverse contents. However, linguistic representations frequently exhibit
ambiguous descriptions of the envisioned objective imagery, requiring the
incorporation of additional control signals to bolster the efficacy of
text-guided diffusion models. In this work, we propose Cocktail, a pipeline to
mix various modalities into one embedding, amalgamated with a generalized
ControlNet (gControlNet), a controllable normalisation (ControlNorm), and a
spatial guidance sampling method, to actualize multi-modal and
spatially-refined control for text-conditional diffusion models. Specifically,
we introduce a hyper-network gControlNet, dedicated to the alignment and
infusion of the control signals from disparate modalities into the pre-trained
diffusion model. gControlNet is capable of accepting flexible modality signals,
encompassing the simultaneous reception of any combination of modality signals,
or the supplementary fusion of multiple modality signals. The control signals
are then fused and injected into the backbone model according to our proposed
ControlNorm. Furthermore, our advanced spatial guidance sampling methodology
proficiently incorporates the control signal into the designated region,
thereby circumventing the manifestation of undesired objects within the
generated image. We demonstrate the results of our method in controlling
various modalities, proving high-quality synthesis and fidelity to multiple
external signals.