Stable diffusion is the mainstay of the text-to-image (T2I) synthesis in the community due to its generation performance and open-source nature. Recently, Stable Diffusion XL~(SDXL), the successor of stable diffusion, has received a lot of attention due to its significant performance improvements with a higher resolution of 1024x1024 and a larger model. However, its increased computation cost and model size require higher-end hardware (e.g., bigger VRAM GPU) for end-users, incurring higher costs of operation. To address this problem, in this work, we propose an efficient latent diffusion model for text-to-image synthesis obtained by distilling the knowledge of SDXL. To this end, we first perform an in-depth analysis of the denoising U-Net in SDXL, which is the main bottleneck of the model, and then design a more efficient U-Net based on the analysis. Secondly, we explore how to effectively distill the generation capability of SDXL into an efficient U-Net and eventually identify four essential factors, the core of which is that self-attention is the most important part. With our efficient U-Net and self-attention-based knowledge distillation strategy, we build our efficient T2I models, called KOALA-1B &-700M, while reducing the model size up to 54% and 69% of the original SDXL model. In particular, the KOALA-700M is more than twice as fast as SDXL while still retaining a decent generation quality. We hope that due to its balanced speed-performance tradeoff, our KOALA models can serve as a cost-effective alternative to SDXL in resource-constrained environments.

Latency and memory usage comparison on different GPUs.

We measure the inference time of SDM-v2.0 with 768x768 resolution and the other models with 1024x1024 using a variety of consumer-grade GPUs: NVIDIA 3060Ti (8GB), 2080Ti (11GB), and 4090 (24GB). We use 25 denoising steps and FP16/FP32 precisions. OOM means Out-of-Memory. Note that SDXL-Base cannot operate in the 8GB-GPU.


Qualitative comparison

We follow the official inference setups of each model (SDM-v2.0 and SDXL-Base-1.0) in the huggingface repository. Specifically, SDM-v2.0 is set to generate with DDIM scheduler with 25 steps and SDXL and ours are set to use Euler discrete scheduler with 25 steps. And we set all models to use the classifier-free guidance with 7.5. Except DALLE-2, we generate all images with FP16 precision. For DALLE-2, we get generated images via OpenAI API.

human 3d painting

Qualitative comparison with SDM-v2.0 and SDXL-Base-1.0

For each prompt, we use 4 random seeds to generate images, while all models are generated with the same seed for each image.



    title={KOALA: Self-Attention Matters in Knowledge Distillation of Latent Diffusion Models for Memory-Efficient and Fast Image Synthesis}, 
    author={Youngwan Lee and Kwanyong Park and Yoorhim Cho and Yong-Ju Lee and Sung Ju Hwang},