标题： 双过程图像生成 显示英文标题

标题： Dual-Process Image Generation

摘要： 先前用于控制图像生成的方法在学习新任务的能力上存在局限性。相比之下，视觉语言模型（VLMs）能够上下文学习任务，并为给定输入生成正确的输出。我们提出了一种双重过程蒸馏方案，使前馈图像生成器能够从深思熟虑的VLMs学习新任务。我们的方案使用VLM对生成的图像进行评分，并反向传播此梯度以更新图像生成器的权重。我们的通用框架通过相同的基于文本和图像的界面实现了各种新的控制任务。我们展示了这项技术在不同类型的控制信号（如常识推理和视觉提示）中的几种应用。通过我们的方法，用户可以在几分钟内实现多模态控制，例如颜色调色板、线条粗细、地平线位置以及相对深度等属性。项目页面：https://dual-process.github.io。 显示英文摘要

摘要： Prior methods for controlling image generation are limited in their ability to be taught new tasks. In contrast, vision-language models, or VLMs, can learn tasks in-context and produce the correct outputs for a given input. We propose a dual-process distillation scheme that allows feed-forward image generators to learn new tasks from deliberative VLMs. Our scheme uses a VLM to rate the generated images and backpropagates this gradient to update the weights of the image generator. Our general framework enables a wide variety of new control tasks through the same text-and-image based interface. We showcase a handful of applications of this technique for different types of control signals, such as commonsense inferences and visual prompts. With our method, users can implement multimodal controls for properties such as color palette, line weight, horizon position, and relative depth within a matter of minutes. Project page: https://dual-process.github.io.