  Image composition involves seamlessly integrating given objects into a
specific visual context. Current training-free methods rely on composing
attention weights from several samplers to guide the generator. However, since
these weights are derived from disparate contexts, their combination leads to
coherence confusion and loss of appearance information. These issues worsen
with their excessive focus on background generation, even when unnecessary in
this task. This not only impedes their swift implementation but also
compromises foreground generation quality. Moreover, these methods introduce
unwanted artifacts in the transition area. In this paper, we formulate image
composition as a subject-based local editing task, solely focusing on
foreground generation. At each step, the edited foreground is combined with the
noisy background to maintain scene consistency. To address the remaining
issues, we propose PrimeComposer, a faster training-free diffuser that
composites the images by well-designed attention steering across different
noise levels. This steering is predominantly achieved by our Correlation
Diffuser, utilizing its self-attention layers at each step. Within these
layers, the synthesized subject interacts with both the referenced object and
background, capturing intricate details and coherent relationships. This prior
information is encoded into the attention weights, which are then integrated
into the self-attention layers of the generator to guide the synthesis process.
Besides, we introduce a Region-constrained Cross-Attention to confine the
impact of specific subject-related words to desired regions, addressing the
unwanted artifacts shown in the prior method thereby further improving the
coherence in the transition area. Our method exhibits the fastest inference
efficiency and extensive experiments demonstrate our superiority both
qualitatively and quantitatively.
