DiM: Diffusion Mamba for Efficient High-Resolution Image Synthesis
Authors: name: Yao Teng, name: Yue Wu, name: Han Shi, name: Xuefei Ning, name: Guohao Dai, name: Yu Wang, name: Zhenguo Li, name: Xihui Liu

Diffusion models have achieved great success in image generation, with the
backbone evolving from U-Net to Vision Transformers. However, the computational
cost of Transformers is quadratic to the number of tokens, leading to
significant challenges when dealing with high-resolution images. In this work,
we propose Diffusion Mamba (DiM), which combines the efficiency of Mamba, a
sequence model based on State Space Models (SSM), with the expressive power of
diffusion models for efficient high-resolution image synthesis. To address the
challenge that Mamba cannot generalize to 2D signals, we make several
architecture designs including multi-directional scans, learnable padding
tokens at the end of each row and column, and lightweight local feature
enhancement. Our DiM architecture achieves inference-time efficiency for
high-resolution images. In addition, to further improve training efficiency for
high-resolution image generation with DiM, we investigate ``weak-to-strong''
training strategy that pretrains DiM on low-resolution images (256times 256)
and then finetune it on high-resolution images (512 times 512). We further
explore training-free upsampling strategies to enable the model to generate
higher-resolution images (e.g., 1024times 1024 and 1536times 1536)
without further fine-tuning. Experiments demonstrate the effectiveness and
efficiency of our DiM.