作者 | 张云聪 编辑 | 自动驾驶之心 原文链接： https://zhuanlan.zhihu.com/p/28731517852 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 目前不少讲Flow Matching的文章都上来一大堆概念，一大堆公式，搞得人头皮发麻，但实际上这个算法没 那么复杂，代码也很容易理解。 本文不推导公式、无高深数学概念即可理解flow matching算法，并完成一个简单的代码实战。 算法原理 Related Works Flow Matching是一种 生成式模型 。 最简单的生成式模型，目标就是没输入的情况下，就能生成与给定目标集中的样本相近的样本。 举个例子，可以直接无提示的用diffusion模型来生成图片。 带提示的生成式任务是可以基于无提示的生成式任务简单实现的，这里我们先只考虑无提示的生成式任 务。 由于我们一般学的是一个映射，拿一个空输入映射成不同的样本不太符合映射的定义，因此，我们一般实 际上会生成一堆随机值作为输入， ...

Algorithm Overview - Flow Matching is a generative model that aims to generate samples similar to a given target set without any input [3][4] - The model learns a direction of movement from a source point to a target point, effectively generating new samples by iteratively adjusting the position towards the target [14][17] Training and Inference - During training, the model samples points along the line connecting source and target, learning the average slope from multiple connections [16][17] - In inference, the model starts from a noise point and moves towards the target, gradually collapsing to a specific state as it approaches the target [17][18] Code Implementation - The implementation involves generating random inputs, predicting the slope using a neural network, and applying an optimization process to minimize the loss between predicted and target slopes [18][19] - The code includes hyperparameters for dimensions, sample sizes, and training epochs, demonstrating a straightforward approach to implementing the Flow Matching algorithm [19][25] Advanced Applications - The model can be adapted to generate samples based on prompts, allowing for more controlled generation by segmenting the target distribution [24][29] - A more complex example includes generating handwritten digits from the MNIST dataset, showcasing the model's versatility in handling different types of data [30][32] Model Architecture - The architecture includes a UNet backbone for predicting the velocity field, which enhances performance through multi-scale feature fusion [32][34] - The model incorporates conditional inputs to refine the generation process, ensuring that the output aligns with specified conditions [34][35] Training Process - The training loop involves generating dynamic noise, calculating the loss based on the difference between predicted and actual images, and updating the model parameters accordingly [40][41] - The model is designed to visualize generated samples periodically, providing insights into its performance and output quality [40][41]

Core Insights - A new method for generating satellite imagery using geographic climate prompts and Stable Diffusion 3 (SD3) has been proposed by teams from the Technical University of Munich and ETH Zurich, resulting in the creation of the largest and most comprehensive remote sensing dataset, EcoMapper [1][2][4]. Dataset Overview - EcoMapper consists of over 2.9 million RGB satellite images collected from 104,424 global locations, covering 15 land cover types and corresponding climate records [2][5]. - The dataset includes a training set with 98,930 geographic points, each observed over a 24-month period, and a test set with 5,494 geographic points observed over 96 months [5][6]. Methodology - The research developed a text-image generation model based on fine-tuned SD3, which utilizes climate and land cover details to generate realistic synthetic images [4][8]. - A multi-condition model framework using ControlNet was also developed to map climate data or generate time series, simulating landscape evolution [4][12]. Model Performance - The study evaluated the performance of SD3 and DiffusionSat models in generating climate-aware satellite images, with metrics indicating significant improvements over baseline models [14][19]. - The SD3-FT-HR model achieved the lowest Fréchet Inception Distance (FID) score of 49.48, indicating high realism in generated images [15][16]. Climate Sensitivity Analysis - The generated vegetation density was found to be significantly correlated with climate changes, with performance varying under extreme weather conditions [16][18]. Applications and Future Directions - EcoMapper provides a framework for simulating satellite images based on climate variables, offering new opportunities for visualizing climate change impacts and enhancing integration of satellite and climate data for downstream models [22][26].