MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing

• URL: http://arxiv.org/abs/2304.08465v1
• Date: Mon, 17 Apr 2023 17:42:19 GMT
• Title: MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing
• Authors: Mingdeng Cao, Xintao Wang, Zhongang Qi, Ying Shan, Xiaohu Qie, Yinqiang Zheng
• Abstract summary: We develop MasaCtrl, a tuning-free method to achieve consistent image generation and complex non-rigid image editing simultaneously. Specifically, MasaCtrl converts existing self-attention in diffusion models into mutual self-attention, so that it can query correlated local contents and textures from source images for consistency. Extensive experiments show that the proposed MasaCtrl can produce impressive results in both consistent image generation and complex non-rigid real image editing.
• Score: 54.712205852602736
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the success in large-scale text-to-image generation and text-conditioned image editing, existing methods still struggle to produce consistent generation and editing results. For example, generation approaches usually fail to synthesize multiple images of the same objects/characters but with different views or poses. Meanwhile, existing editing methods either fail to achieve effective complex non-rigid editing while maintaining the overall textures and identity, or require time-consuming fine-tuning to capture the image-specific appearance. In this paper, we develop MasaCtrl, a tuning-free method to achieve consistent image generation and complex non-rigid image editing simultaneously. Specifically, MasaCtrl converts existing self-attention in diffusion models into mutual self-attention, so that it can query correlated local contents and textures from source images for consistency. To further alleviate the query confusion between foreground and background, we propose a mask-guided mutual self-attention strategy, where the mask can be easily extracted from the cross-attention maps. Extensive experiments show that the proposed MasaCtrl can produce impressive results in both consistent image generation and complex non-rigid real image editing.

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