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Ctrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance

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arxiv 2406.07540 v2 pith:HHXOOKUB submitted 2024-06-11 cs.CV cs.LG

Kuan Heng Lin , Sicheng Mo , Ben Klingher , Fangzhou Mu , Bolei Zhou This is my paper · ORCID
classification cs.CVcs.LG
keywords appearancectrl-xstructurediffusioncontrolgenerationimagetransfer
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent controllable generation approaches such as FreeControl and Diffusion Self-Guidance bring fine-grained spatial and appearance control to text-to-image (T2I) diffusion models without training auxiliary modules. However, these methods optimize the latent embedding for each type of score function with longer diffusion steps, making the generation process time-consuming and limiting their flexibility and use. This work presents Ctrl-X, a simple framework for T2I diffusion controlling structure and appearance without additional training or guidance. Ctrl-X designs feed-forward structure control to enable the structure alignment with a structure image and semantic-aware appearance transfer to facilitate the appearance transfer from a user-input image. Extensive qualitative and quantitative experiments illustrate the superior performance of Ctrl-X on various condition inputs and model checkpoints. In particular, Ctrl-X supports novel structure and appearance control with arbitrary condition images of any modality, exhibits superior image quality and appearance transfer compared to existing works, and provides instant plug-and-play functionality to any T2I and text-to-video (T2V) diffusion model. See our project page for an overview of the results: https://genforce.github.io/ctrl-x

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