Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perceptual data such as images and videos. Rectified flow is a recent generative model formulation that connects data and noise in a straight line. Despite its better theoretical properties and conceptual simplicity, it is not yet decisively established as standard practice. In this work, we improve existing noise sampling techniques for training rectified flow models by biasing them towards perceptually relevant scales. Through a large-scale study, we demonstrate the superior performance of this approach compared to established diffusion formulations for high-resolution text-to-image synthesis. Additionally, we present a novel transformer-based architecture for text-to-image generation that uses separate weights for the two modalities and enables a bidirectional flow of information between image and text tokens, improving text comprehension, typography, and human preference ratings. We demonstrate that this architecture follows predictable scaling trends and correlates lower validation loss to improved text-to-image synthesis as measured by various metrics and human evaluations. Our largest models outperform state-of-the-art models, and we will make our experimental data, code, and model weights publicly available.

翻译：扩散模型通过反转数据向噪声的前向路径，从噪声生成数据，已成为处理图像和视频等高维感知数据的强大生成建模技术。修正流是一种最近的生成模型范式，以直线方式连接数据与噪声。尽管其具有更优的理论特性与概念简洁性，尚未被确立为标准实践。本研究通过将现有用于训练修正流模型的噪声采样技术偏向感知相关尺度，改进了该方法。通过大规模研究，我们证明了该方法在高分辨率文生图合成中相较于传统扩散范式的优越性能。此外，我们提出了一种新颖的基于变换器的文生图架构，该架构为图像和文本两种模态使用独立权重，并支持图像与文本令牌间的双向信息流，从而提升文本理解、排版质量及人类偏好评分。我们证明该架构遵循可预测的缩放趋势，且更低的验证损失与通过多种指标和人工评估衡量的改进文生图合成效果相关。我们的最大模型超越了当前最优模型，并将公开实验数据、代码及模型权重。