Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models (2410.10733v3)

Abstract: We present Deep Compression Autoencoder (DC-AE), a new family of autoencoder models for accelerating high-resolution diffusion models. Existing autoencoder models have demonstrated impressive results at a moderate spatial compression ratio (e.g., 8x), but fail to maintain satisfactory reconstruction accuracy for high spatial compression ratios (e.g., 64x). We address this challenge by introducing two key techniques: (1) Residual Autoencoding, where we design our models to learn residuals based on the space-to-channel transformed features to alleviate the optimization difficulty of high spatial-compression autoencoders; (2) Decoupled High-Resolution Adaptation, an efficient decoupled three-phases training strategy for mitigating the generalization penalty of high spatial-compression autoencoders. With these designs, we improve the autoencoder's spatial compression ratio up to 128 while maintaining the reconstruction quality. Applying our DC-AE to latent diffusion models, we achieve significant speedup without accuracy drop. For example, on ImageNet 512x512, our DC-AE provides 19.1x inference speedup and 17.9x training speedup on H100 GPU for UViT-H while achieving a better FID, compared with the widely used SD-VAE-f8 autoencoder. Our code is available at https://github.com/mit-han-lab/efficientvit.

• The paper introduces DC-AE, a method that effectively handles high spatial compression challenges by learning residuals via space-to-channel transformations.
• It decouples high-resolution adaptation from local refinement, significantly boosting training and inference speeds on datasets like ImageNet 512x512.
• DC-AE achieves up to 19.1-fold inference and 17.9-fold training speedup, offering scalable solutions for efficient image synthesis in diffusion models.

Overview of "Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models"

The paper introduces the "Deep Compression Autoencoder" (DC-AE), an innovative approach for enhancing the efficiency of high-resolution diffusion models. The authors address the limitations of existing autoencoders when applied to high spatial compression ratios, proposing solutions that maintain reconstruction accuracy while offering substantial improvements in processing speed and resource utilization.

Key Contributions

1. Challenges with High Spatial Compression: Existing autoencoders, often used with a spatial compression ratio of 8, struggle with maintaining accuracy at higher compression levels, such as 64 or beyond. This is a critical limitation for scalable diffusion models that require efficient token management.
2. Proposed Innovations:
   • Residual Autoencoding: By designing models that learn residuals through space-to-channel transformations, this approach effectively mitigates optimization difficulties inherent to high compression ratios.
   • Decoupled High-Resolution Adaptation: A training strategy that separates high-resolution adaptation and local refinement allows these autoencoders to efficiently manage high spatial compression while maintaining accuracy.
3. Performance Improvements: DC-AE significantly increases training and inference speed with negligible loss in performance. On the ImageNet dataset at a resolution of 512x512, the DC-AE achieves a 19.1-fold inference speedup and a 17.9-fold training speedup on the H100 GPU compared to the standard SD-VAE-f8 autoencoder.

Theoretical and Practical Implications

The advancements introduced in this paper have substantial theoretical implications for the design of future high-resolution diffusion models. By addressing the challenges associated with token handling efficiency directly at the autoencoder level, this work shifts the computational burden away from the diffusion models themselves, allowing them to focus purely on denoising tasks.

Practically, these developments enable more scalable and efficient image synthesis and transformation workflows, particularly for applications requiring high-resolution outputs, such as graphic design or 3D modeling.

Numerical Results and Claims

The paper provides robust numerical evidence demonstrating the superiority of DC-AE. For example, its application results in improved FID scores on ImageNet 512x512, highlighting the qualitative improvements in image reconstruction accuracy even at higher spatial compression ratios.

Future Developments and Speculation

Looking forward, this research opens new avenues for exploring the scalability of autoencoders in handling ever-increasing image resolutions and even broader types of data inputs beyond images. It also encourages further exploration into optimizing autoencoder architectures for specific applications, potentially leveraging hybrid strategies that incorporate elements from emerging AI paradigms.

Conclusion

The development of Deep Compression Autoencoder represents a significant leap forward in the design and implementation of diffusion models for high-resolution tasks. Through innovative techniques that maintain reconstruction quality while vastly improving efficiency, this work provides a solid foundation for future research into accelerating complex AI models without compromising their output fidelity.