  In recent developments, the Mamba architecture, known for its selective state
space approach, has shown potential in the efficient modeling of long
sequences. However, its application in image generation remains underexplored.
Traditional diffusion transformers (DiT), which utilize self-attention blocks,
are effective but their computational complexity scales quadratically with the
input length, limiting their use for high-resolution images. To address this
challenge, we introduce a novel diffusion architecture, Diffusion Mamba (DiM),
which foregoes traditional attention mechanisms in favor of a scalable
alternative. By harnessing the inherent efficiency of the Mamba architecture,
DiM achieves rapid inference times and reduced computational load, maintaining
linear complexity with respect to sequence length. Our architecture not only
scales effectively but also outperforms existing diffusion transformers in both
image and video generation tasks. The results affirm the scalability and
efficiency of DiM, establishing a new benchmark for image and video generation
techniques. This work advances the field of generative models and paves the way
for further applications of scalable architectures.
