  Text-to-image generation has achieved astonishing results, yet precise
spatial controllability and prompt fidelity remain highly challenging. This
limitation is typically addressed through cumbersome prompt engineering, scene
layout conditioning, or image editing techniques which often require hand drawn
masks. Nonetheless, pre-existing works struggle to take advantage of the
natural instance-level compositionality of scenes due to the typically flat
nature of rasterized RGB output images. Towards adressing this challenge, we
introduce MuLAn: a novel dataset comprising over 44K MUlti-Layer ANnotations of
RGB images as multilayer, instance-wise RGBA decompositions, and over 100K
instance images. To build MuLAn, we developed a training free pipeline which
decomposes a monocular RGB image into a stack of RGBA layers comprising of
background and isolated instances. We achieve this through the use of
pretrained general-purpose models, and by developing three modules: image
decomposition for instance discovery and extraction, instance completion to
reconstruct occluded areas, and image re-assembly. We use our pipeline to
create MuLAn-COCO and MuLAn-LAION datasets, which contain a variety of image
decompositions in terms of style, composition and complexity. With MuLAn, we
provide the first photorealistic resource providing instance decomposition and
occlusion information for high quality images, opening up new avenues for
text-to-image generative AI research. With this, we aim to encourage the
development of novel generation and editing technology, in particular
layer-wise solutions. MuLAn data resources are available at
https://MuLAn-dataset.github.io/.
