  Image generation from scene description is a cornerstone technique for the
controlled generation, which is beneficial to applications such as content
creation and image editing. In this work, we aim to synthesize images from
scene description with retrieved patches as reference. We propose a
differentiable retrieval module. With the differentiable retrieval module, we
can (1) make the entire pipeline end-to-end trainable, enabling the learning of
better feature embedding for retrieval; (2) encourage the selection of mutually
compatible patches with additional objective functions. We conduct extensive
quantitative and qualitative experiments to demonstrate that the proposed
method can generate realistic and diverse images, where the retrieved patches
are reasonable and mutually compatible.
