  The emergence of generative models has revolutionized the field of remote
sensing (RS) image generation. Despite generating high-quality images, existing
methods are limited in relying mainly on text control conditions and thus don't
always generate images accurately and stablely. In this paper, we propose
CRS-Diff, a new RS generative foundation framework specifically tailored for RS
image generation, leveraging the inherent advantages of diffusion models while
integrating more advanced control mechanisms. Specifically, CRS-Diff can
simultaneously support text-condition, metadata-condition, and image-condition
control inputs, thus enabling more precise control to refine the generation
process. To effectively integrate multiple condition control information, we
introduce a new conditional control mechanism to achieve multi-scale feature
fusion, thus enhancing the guiding effect of control conditions. To our
knowledge, CRS-Diff is the first multiple-condition controllable generative RS
foundation model. Experimental results in single-condition and
multiple-condition cases have demonstrated the superior ability of our CRS-Diff
to generate RS images both quantitatively and qualitatively compared with
previous methods. Additionally, our CRS-Diff can serve as a data engine that
generates high-quality training data for downstream tasks, e.g., road
extraction. The code is available at https://github.com/Sonettoo/CRS-Diff.
