Recent text-to-image (T2I) diffusion models show outstanding performance in
generating high-quality images conditioned on textual prompts. However, these
models fail to semantically align the generated images with the text
descriptions due to their limited compositional capabilities, leading to
attribute leakage, entity leakage, and missing entities. In this paper, we
propose a novel attention mask control strategy based on predicted object boxes
to address these three issues. In particular, we first train a BoxNet to
predict a box for each entity that possesses the attribute specified in the
prompt. Then, depending on the predicted boxes, unique mask control is applied
to the cross- and self-attention maps. Our approach produces a more
semantically accurate synthesis by constraining the attention regions of each
token in the prompt to the image. In addition, the proposed method is
straightforward and effective, and can be readily integrated into existing
cross-attention-diffusion-based T2I generators. We compare our approach to
competing methods and demonstrate that it not only faithfully conveys the
semantics of the original text to the generated content, but also achieves high
availability as a ready-to-use plugin.

本文提出了一种基于预测的物体框的注意力掩码控制策略，以解决文本到图像合成模型在语义上的局限性，尤其是属性泄漏、实体泄漏和缺失实体，该方法通过约束查询语句中每个标记的注意区域来实现更准确的语义综合，此外，该方法简单而有效，可以轻松集成到现有的跨注意力扩散 T2I 生成器中，并成功地在生成内容中传达了原始文本的语义，并作为可用的插件得到了高可用性的证明。

扩散模型的注意力映射控制组合式文本到图像合成

Compositional Text-to-Image Synthesis with Attention Map Control of  Diffusion Models

Diffusion models have demonstrated remarkable progress in image generation
quality, especially when guidance is used to control the generative process.
However, guidance requires a large amount of image-annotation pairs for
training and is thus dependent on their availability, correctness and
unbiasedness. In this paper, we eliminate the need for such annotation by
instead leveraging the flexibility of self-supervision signals to design a
framework for self-guided diffusion models. By leveraging a feature extraction
function and a self-annotation function, our method provides guidance signals
at various image granularities: from the level of holistic images to object
boxes and even segmentation masks. Our experiments on single-label and
multi-label image datasets demonstrate that self-labeled guidance always
outperforms diffusion models without guidance and may even surpass guidance
based on ground-truth labels, especially on unbalanced data. When equipped with
self-supervised box or mask proposals, our method further generates visually
diverse yet semantically consistent images, without the need for any class,
box, or segment label annotation. Self-guided diffusion is simple, flexible and
expected to profit from deployment at scale.

本文介绍了一种自导扩散模型的方法，它可以利用自我监督信号来提供图像生成的指导，而无需大量的图像注释对。通过结合特征提取功能和自注释功能，我们的方法可以为各种图像颗粒度提供指导信号，从整体图像到对象框甚至分割掩模。我们的实验表明，我们的方法在单标签和多标签图像数据集上总是优于无指导的扩散模型，甚至可能超过基于基础真实标签的指导，特别是在不平衡数据上。