LinFusion: 1 GPU, 1 Minute, 16K Image
Authors: name: Songhua Liu, name: Weihao Yu, name: Zhenxiong Tan, name: Xinchao Wang

Modern diffusion models, particularly those utilizing a Transformer-based
UNet for denoising, rely heavily on self-attention operations to manage complex
spatial relationships, thus achieving impressive generation performance.
However, this existing paradigm faces significant challenges in generating
high-resolution visual content due to its quadratic time and memory complexity
with respect to the number of spatial tokens. To address this limitation, we
aim at a novel linear attention mechanism as an alternative in this paper.
Specifically, we begin our exploration from recently introduced models with
linear complexity, e.g., Mamba, Mamba2, and Gated Linear Attention, and
identify two key features-attention normalization and non-causal inference-that
enhance high-resolution visual generation performance. Building on these
insights, we introduce a generalized linear attention paradigm, which serves as
a low-rank approximation of a wide spectrum of popular linear token mixers. To
save the training cost and better leverage pre-trained models, we initialize
our models and distill the knowledge from pre-trained StableDiffusion (SD). We
find that the distilled model, termed LinFusion, achieves performance on par
with or superior to the original SD after only modest training, while
significantly reducing time and memory complexity. Extensive experiments on
SD-v1.5, SD-v2.1, and SD-XL demonstrate that LinFusion delivers satisfactory
zero-shot cross-resolution generation performance, generating high-resolution
images like 16K resolution. Moreover, it is highly compatible with pre-trained
SD components, such as ControlNet and IP-Adapter, requiring no adaptation
efforts. Codes are available at https://github.com/Huage001/LinFusion.