Clockwork Diffusion: Efficient Generation With Model-Step Distillation (2312.08128v2)

Abstract: This work aims to improve the efficiency of text-to-image diffusion models. While diffusion models use computationally expensive UNet-based denoising operations in every generation step, we identify that not all operations are equally relevant for the final output quality. In particular, we observe that UNet layers operating on high-res feature maps are relatively sensitive to small perturbations. In contrast, low-res feature maps influence the semantic layout of the final image and can often be perturbed with no noticeable change in the output. Based on this observation, we propose Clockwork Diffusion, a method that periodically reuses computation from preceding denoising steps to approximate low-res feature maps at one or more subsequent steps. For multiple baselines, and for both text-to-image generation and image editing, we demonstrate that Clockwork leads to comparable or improved perceptual scores with drastically reduced computational complexity. As an example, for Stable Diffusion v1.5 with 8 DPM++ steps we save 32% of FLOPs with negligible FID and CLIP change.

• The paper introduces model-step distillation that reuses low-resolution feature maps to significantly reduce computational cost in diffusion models.
• The methodology employs an efficient adaptor design and a clockwork scheduling strategy to maintain high image quality while cutting FLOPs by up to 38%.
• Extensive experiments on benchmarks like MS-COCO demonstrate robust performance improvements, enabling scalable and resource-efficient text-to-image generation.

An Analysis of Clockwork Diffusion: Efficient Generation with Model-Step Distillation

The paper "Clockwork Diffusion: Efficient Generation with Model-Step Distillation" presents an innovative approach to increasing the efficiency of text-to-image diffusion models. Diffusion models, well-regarded for their ability to produce diverse and high-quality images from textual descriptions, often suffer from high computational costs due to the repeated execution of UNet-based denoising operations. This paper identifies a novel way to mitigate this computational overhead by leveraging the resilience of low-resolution feature maps in these models.

Key Contributions

1. Model-Step Distillation: The core idea proposed is termed "Clockwork Diffusion", a strategy that combines model and step distillation. By periodically reusing low-resolution computation from preceding steps, the method approximates subsequent low-resolution feature maps. This reduces computational demands by bypassing redundant denoising processes while preserving output quality.
2. Efficient Adaptor Design: The authors design an adaptor that effectively replaces significant portions of the UNet network. Unlike high-resolution layers sensitive to perturbations, lower-resolution layers can be approximated without significant degradation of the resultant image quality. This adaptor consists of a lightweight architecture that reduces computational costs and improves processing efficiency.
3. Training with Unrolled Trajectories: The paper introduces a unique training method for the adaptor based on unrolled trajectories rather than traditional forward noise processes. This unrolled approach allows the method to be trained effectively without an underlying image dataset, utilizing only noise samples and captioned text.
4. Clockwork Scheduling: An alternating schedule is proposed for denoising operations, where full UNet passes are alternated with approximated low-resolution passes. This counteracts the accumulation of errors typically associated with continuous approximation, ensuring robustness across multiple sampling steps.

Experimental Validation

The paper conducts extensive experiments on tasks such as text-to-image generation and text-guided image editing to demonstrate the efficacy of Clockwork Diffusion. On benchmarks like MS-COCO 2017 and ImageNet-R-TI2I, the approach shows significant reductions in both floating point operations (FLOPs) and latency, maintaining comparable Fréchet Inception Distance (FID) and CLIP scores. Notably, the methodology achieves a 38% reduction in FLOPs on a distilled and optimized Stable Diffusion model.

Additionally, the method compliments existing acceleration strategies such as step distillation and efficient sampler designs. For instance, it shows enhanced performance even when applied to already optimized diffusion models, emphasizing the versatility and scalability of this approach.

Implications and Future Directions

The implications of this work are significant for both theoretical and practical applications in diffusion models and AI-driven image synthesis. Practically, the method can be adopted for resource-constrained environments, such as mobile devices, without substantial quality loss, accelerating the deployment of AI applications in real-world scenarios.

Theoretically, Clockwork Diffusion opens avenues for further exploration into adaptive distillation strategies. Future work may delve into extending this methodology to alternative architectural paradigms such as transformer-based diffusion models, or its integration into other generative models beyond image synthesis.

In summary, the paper provides robust evidence that careful architectural and operational considerations in diffusion models can substantially enhance their computational efficiency. Clockwork Diffusion contributes to the ongoing discourse on making AI models more accessible and scalable through intelligent design choices.