LoRA-Enhanced Distillation on Guided Diffusion Models
This work addresses efficiency issues for users of diffusion models like Stable Diffusion, offering a practical optimization without trade-offs, though it is incremental as it builds on existing distillation and LoRA techniques.
The paper tackled the high computational and memory costs of classifier-free guided diffusion models by combining Low-Rank Adaptation (LoRA) with model distillation, resulting in a significant reduction in inference time and a 50% decrease in memory consumption while maintaining image quality.
Diffusion models, such as Stable Diffusion (SD), offer the ability to generate high-resolution images with diverse features, but they come at a significant computational and memory cost. In classifier-free guided diffusion models, prolonged inference times are attributed to the necessity of computing two separate diffusion models at each denoising step. Recent work has shown promise in improving inference time through distillation techniques, teaching the model to perform similar denoising steps with reduced computations. However, the application of distillation introduces additional memory overhead to these already resource-intensive diffusion models, making it less practical. To address these challenges, our research explores a novel approach that combines Low-Rank Adaptation (LoRA) with model distillation to efficiently compress diffusion models. This approach not only reduces inference time but also mitigates memory overhead, and notably decreases memory consumption even before applying distillation. The results are remarkable, featuring a significant reduction in inference time due to the distillation process and a substantial 50% reduction in memory consumption. Our examination of the generated images underscores that the incorporation of LoRA-enhanced distillation maintains image quality and alignment with the provided prompts. In summary, while conventional distillation tends to increase memory consumption, LoRA-enhanced distillation offers optimization without any trade-offs or compromises in quality.