  Recently, there has been tremendous progress in visual synthesis and the
underlying generative models. Here, diffusion models (DMs) stand out
particularly, but lately, flow matching (FM) has also garnered considerable
interest. While DMs excel in providing diverse images, they suffer from long
training and slow generation. With latent diffusion, these issues are only
partially alleviated. Conversely, FM offers faster training and inference but
exhibits less diversity in synthesis. We demonstrate that introducing FM
between the Diffusion model and the convolutional decoder offers
high-resolution image synthesis with reduced computational cost and model size.
Diffusion can then efficiently provide the necessary generation diversity. FM
compensates for the lower resolution, mapping the small latent space to a
high-dimensional one. Subsequently, the convolutional decoder of the LDM maps
these latents to high-resolution images. By combining the diversity of DMs, the
efficiency of FMs, and the effectiveness of convolutional decoders, we achieve
state-of-the-art high-resolution image synthesis at $1024^2$ with minimal
computational cost. Importantly, our approach is orthogonal to recent
approximation and speed-up strategies for the underlying DMs, making it easily
integrable into various DM frameworks.
