Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models (2501.01423v1)

Abstract: Latent diffusion models with Transformer architectures excel at generating high-fidelity images. However, recent studies reveal an optimization dilemma in this two-stage design: while increasing the per-token feature dimension in visual tokenizers improves reconstruction quality, it requires substantially larger diffusion models and more training iterations to achieve comparable generation performance. Consequently, existing systems often settle for sub-optimal solutions, either producing visual artifacts due to information loss within tokenizers or failing to converge fully due to expensive computation costs. We argue that this dilemma stems from the inherent difficulty in learning unconstrained high-dimensional latent spaces. To address this, we propose aligning the latent space with pre-trained vision foundation models when training the visual tokenizers. Our proposed VA-VAE (Vision foundation model Aligned Variational AutoEncoder) significantly expands the reconstruction-generation frontier of latent diffusion models, enabling faster convergence of Diffusion Transformers (DiT) in high-dimensional latent spaces. To exploit the full potential of VA-VAE, we build an enhanced DiT baseline with improved training strategies and architecture designs, termed LightningDiT. The integrated system achieves state-of-the-art (SOTA) performance on ImageNet 256x256 generation with an FID score of 1.35 while demonstrating remarkable training efficiency by reaching an FID score of 2.11 in just 64 epochs--representing an over 21 times convergence speedup compared to the original DiT. Models and codes are available at: https://github.com/hustvl/LightningDiT.

Analyzing the Trade-offs in Latent Diffusion Models: A Study on Reconstruction and Generation Optimization

The paper "Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models" by Jingfeng Yao and Xinggang Wang explores a significant challenge faced by latent diffusion models (LDMs), which typically employ Transformer architectures for high-fidelity image generation. This investigation is centered on the optimization trade-offs inherent in two-stage designs involving visual tokenizers, particularly within high-dimensional latent spaces.

Key Insights and Contributions

Latent diffusion models leverage a variational autoencoder (VAE) as a visual tokenizer to compress visual signals, streamlining the computational requirements of generating high-resolution images. Despite their successes, LDMs encounter a persistent optimization dilemma: enhancements in reconstruction quality through increased feature dimensions within tokenizers often lead to deteriorated generation performance. This is primarily due to the need for significantly larger diffusion models and extended training durations to achieve an equilibrium between reconstruction and generation efficacy.

To address this conundrum, the authors propose a novel approach, aligning the latent space of VAEs with pre-trained vision foundation models. This solution is encapsulated in the Vision foundation model Aligned Variational AutoEncoder (VA-VAE), which expands the potential of LDMs. This adjustment enables Diffusion Transformers (DiT) to converge more rapidly when operating in high-dimensional latent spaces.

An essential contribution of the paper is the introduction of the Vision Foundation model alignment Loss (VF Loss). This module aligns latent representations with pre-trained vision models during tokenizer training. The VF Loss consists of two key components: marginal cosine similarity and marginal distance matrix similarity losses. These are designed to regularize high-dimensional latent spaces effectively, circumventing the excessive capacity limitation and overfitting issues often encountered in unconstrained environments.

Implications and Applications

The paper achieves a remarkable improvement in ImageNet 256×256 generation, recording a Fréchet Inception Distance (FID) score of 1.35. Notably, it also highlights an unprecedented efficiency, reaching an FID of 2.11 within just 64 epochs, marking an over 21x convergence speedup compared to traditional models. Such outcomes underscore the practical benefits of adopting the VF Loss approach in real-world applications, where computational resources and time are often constrained.

Practically, the adoption of the VA-VAE model has the potential to redefine the use of latent diffusion models in industries that require high-resolution image synthesis, such as gaming, virtual reality, and content generation. The proposed model effectively renegotiates the computational cost versus quality trade-off, making it feasible for broader commercial applications.

On a theoretical note, the research fosters a deeper understanding of latent space dynamics and optimization strategies within VAEs. By guiding this space with robust pre-trained models, the paper opens new avenues for efficient latent variable optimization, thereby inviting further exploration into the intricacies of diffusion model training.

Future Directions

While the paper meticulously addresses the balance between reconstruction and generation quality, future research could explore the scalability of VA-VAE to even higher-dimensional latent spaces and other modalities beyond image data. Additionally, integrating other state-of-the-art generative models and examining the interplay of different foundational model types in VF Loss could yield even more robust models.

In conclusion, the paper offers a comprehensive and technologically progressive solution to a persistent problem in diffusion model optimization. By aligning high-dimensional latent spaces with established visual models, it delineates a clear path towards achieving a fine balance between the reconstruction quality and generative capability of latent diffusion systems. These insights not only augment the current understanding within the community but also facilitate the efficient deployment of advanced generative systems in various domains.