Recent text-to-image diffusion models have reached an unprecedented level in
generating high-quality images. However, their exclusive reliance on textual
prompts often falls short in accurately conveying fine-grained spatial
compositions. In this paper, we propose LoCo, a training-free approach for
layout-to-image synthesis that excels in producing high-quality images aligned
with both textual prompts and spatial layouts. Our method introduces a
Localized Attention Constraint to refine cross-attention for individual
objects, ensuring their precise placement in designated regions. We further
propose a Padding Token Constraint to leverage the semantic information
embedded in previously neglected padding tokens, thereby preventing the
undesired fusion of synthesized objects. LoCo seamlessly integrates into
existing text-to-image and layout-to-image models, significantly amplifying
their performance and effectively addressing semantic failures observed in
prior methods. Through extensive experiments, we showcase the superiority of
our approach, surpassing existing state-of-the-art training-free
layout-to-image methods both qualitatively and quantitatively across multiple
benchmarks.

本文中，我们提出了一种名为 LoCo 的无需训练的布局到图像合成方法，能够通过引入局部化注意约束和填充令牌约束，对个体对象进行精确定位并防止合成对象的不良融合，并成功地集成到现有的文本到图像和布局到图像模型中，在多个基准测试中定性和定量地超越了既有的最先进的无需训练的布局到图像方法，展示了我们方法的优越性。

LoCo：局部受限无训练布局到图像合成

LoCo: Locally Constrained Training-Free Layout-to-Image Synthesis

Bayesian optimization has been challenged by datasets with large-scale,
high-dimensional, and non-stationary characteristics, which are common in
real-world scenarios. Recent works attempt to handle such input by applying
neural networks ahead of the classical Gaussian process to learn a latent
representation. We show that even with proper network design, such learned
representation often leads to collision in the latent space: two points with
significantly different observations collide in the learned latent space,
leading to degraded optimization performance. To address this issue, we propose
LOCo, an efficient deep Bayesian optimization framework which employs a novel
regularizer to reduce the collision in the learned latent space and encourage
the mapping from the latent space to the objective value to be Lipschitz
continuous. LOCo takes in pairs of data points and penalizes those too close in
the latent space compared to their target space distance. We provide a rigorous
theoretical justification for LOCo by inspecting the regret of this
dynamic-embedding-based Bayesian optimization algorithm, where the neural
network is iteratively retrained with the regularizer. Our empirical results
demonstrate the effectiveness of LOCo on several synthetic and real-world
benchmark Bayesian optimization tasks.

提出 LOCo，一种有效的深度贝叶斯优化框架，其采用一种新的正则化方法来降低学习到的潜在空间中的冲突，并促进从潜在空间到目标值的映射具有 Lipschitz 连续性，通过将数据点分成不同距离空间，来惩罚太接近的数据点，该算法的实验结果在多项绩效测试中表现出非常明显的效果。

基于无碰撞正则化的贝叶斯优化学习表示

Learning Representation for Bayesian Optimization with Collision-free Regularization

We propose LOCO, an algorithm for large-scale ridge regression which
distributes the features across workers on a cluster. Important dependencies
between variables are preserved using structured random projections which are
cheap to compute and must only be communicated once. We show that LOCO obtains
a solution which is close to the exact ridge regression solution in the fixed
design setting. We verify this experimentally in a simulation study as well as
an application to climate prediction. Furthermore, we show that LOCO achieves
significant speedups compared with a state-of-the-art distributed algorithm on
a large-scale regression problem.

LOCO 是一种用于大规模岭回归的算法，通过将特征分布在集群工作进程中来实现。算法保留变量之间重要的依赖关系，并采用结构化随机投影来计算，仅需通信一次。仿真和气候预测应用验证实验结果，表明 LOCO 相对于最先进的分布式算法实现了显著的加速。