SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing

TL;DR

SCEdit introduces an efficient method for image diffusion generation by editing skip connections in U-Net, enabling controllable synthesis with reduced computational resources and improved performance.

Contribution

The paper proposes a novel lightweight tuning framework, SCEdit, that modifies skip connections in U-Net for efficient and controllable image diffusion generation.

Findings

Reduces training parameters, memory, and computation.

Achieves superior performance in text-to-image and controllable synthesis tasks.

Enables multi-condition image synthesis with simple extensions.

Abstract

Image diffusion models have been utilized in various tasks, such as text-to-image generation and controllable image synthesis. Recent research has introduced tuning methods that make subtle adjustments to the original models, yielding promising results in specific adaptations of foundational generative diffusion models. Rather than modifying the main backbone of the diffusion model, we delve into the role of skip connection in U-Net and reveal that hierarchical features aggregating long-distance information across encoder and decoder make a significant impact on the content and quality of image generation. Based on the observation, we propose an efficient generative tuning framework, dubbed SCEdit, which integrates and edits Skip Connection using a lightweight tuning module named SC-Tuner. Furthermore, the proposed framework allows for straightforward extension to controllable image synthesis by injecting different conditions with Controllable SC-Tuner, simplifying and unifying the network design for multi-condition inputs. Our SCEdit substantially reduces training parameters, memory usage, and computational expense due to its lightweight tuners, with backward propagation only passing to the decoder blocks. Extensive experiments conducted on text-to-image generation and controllable image synthesis tasks demonstrate the superiority of our method in terms of efficiency and performance. Project page: \url{https://scedit.github.io/}