  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.
