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The fashion industry has diverse applications in multi-modal image generationand editing. It aims to create a desired high-fidelity image with themulti-modal conditional signal as guidance. Most existing methods learndifferent condition guidance controls by introducing extra models or ignoringthe style prior knowledge, which is difficult to handle multiple signalcombinations and faces a low-fidelity problem. In this paper, we adapt bothstyle prior knowledge and flexibility of multi-modal control into one unifiedtwo-stage framework, M6-Fashion, focusing on the practical AI-aided Fashiondesign. It decouples style codes in both spatial and semantic dimensions toguarantee high-fidelity image generation in the first stage. M6-Fashionutilizes self-correction for the non-autoregressive generation to improveinference speed, enhance holistic consistency, and support various signalcontrols. Extensive experiments on a large-scale clothing dataset M2C-Fashiondemonstrate superior performances on various image generation and editingtasks. M6-Fashion model serves as a highly potential AI designer for thefashion industry.

M6-Fashion: High-Fidelity Multi-modal Image Generation and Editing