Efficient Training-Free High-Resolution Synthesis with Energy Rectification in Diffusion Models (2503.02537v3)

Abstract: Diffusion models have achieved remarkable progress across various visual generation tasks. However, their performance significantly declines when generating content at resolutions higher than those used during training. Although numerous methods have been proposed to enable high-resolution generation, they all suffer from inefficiency. In this paper, we propose RectifiedHR, a straightforward and efficient solution for training-free high-resolution synthesis. Specifically, we propose a noise refresh strategy that unlocks the model's training-free high-resolution synthesis capability and improves efficiency. Additionally, we are the first to observe the phenomenon of energy decay, which may cause image blurriness during the high-resolution synthesis process. To address this issue, we introduce average latent energy analysis and find that tuning the classifier-free guidance hyperparameter can significantly improve generation performance. Our method is entirely training-free and demonstrates efficient performance. Furthermore, we show that RectifiedHR is compatible with various diffusion model techniques, enabling advanced features such as image editing, customized generation, and video synthesis. Extensive comparisons with numerous baseline methods validate the superior effectiveness and efficiency of RectifiedHR.

Overview of "RectifiedHR: Enable Efficient High-Resolution Image Generation via Energy Rectification"

The paper "RectifiedHR: Enable Efficient High-Resolution Image Generation via Energy Rectification" introduces a novel approach to high-resolution image generation without the need for additional model training. This work addresses a significant challenge in diffusion models: the decline in performance when generating images at resolutions higher than those encountered during training. Traditional methods for achieving high-resolution outputs often involve complex processes or inefficiencies. In contrast, RectifiedHR presents a straightforward, training-free methodology aimed at circumventing these challenges.

Key Contributions

1. Noise Refresh Strategy: The authors introduce a noise refresh technique that essentially overlays a noise correction mechanism onto existing diffusion models. This modification requires only a minimal amount of code to unlock the potential for high-resolution image generation, thereby enhancing computational efficiency.
2. Energy Rectification: A novel observation of energy decay during the image generation process is noted as a contributing factor to image blurriness at high resolutions. The authors propose an energy rectification strategy that modifies the hyperparameters of the classifier-free guidance to maintain energy levels and improve image clarity.
3. Training-Free Approach: A significant advantage of RectifiedHR is its training-free implementation, allowing for straightforward integration with existing diffusion models like SDXL. This contrasts with many current methods that either require retraining on high-resolution datasets or employ intricate alterations to the model architecture.

Methodological Advances

• Latent Energy Analysis: The paper introduces latent average energy as a measure during the sampling process, which helps to diagnose and address the blurring issues observed in high-resolution outputs.
• Classifier-Free Guidance Adjustment: By tuning the classifier-free guidance hyperparameter, the model can rectify the energy decay observed during noise refresh operations, leading to more detailed and clear image generation.

Numerical Results and Comparative Analysis

Extensive evaluations demonstrate that RectifiedHR outperforms several baseline methods in terms of both effectiveness and efficiency. In resolutions of 2048x2048 and 4096x4096, RectifiedHR exhibits superior performance metrics, achieving competitive FID, KID, and IS scores while maintaining lower computational overhead. Compared to methods like BSRGAN which provide fast but potentially less detailed outputs, RectifiedHR offers a balance of speed and image quality, positioning it as a strong contender for high-resolution applications.

Theoretical and Practical Implications

• Efficiency in Image Generation: The proposed method significantly reduces the computational cost associated with high-resolution image generation while maintaining high fidelity, making it ideal for real-time applications where computational resources are limited.
• Broad Applicability: Given its training-free nature, RectifiedHR can be readily applied across a wide range of diffusion models and tasks, from standard image generation to more specialized areas such as image editing and video synthesis.

Future Directions

The authors suggest that while RectifiedHR has proven effective for image generation, exploration into its application across other domains such as video generation or custom image editing may yield further insights and extensions. Additionally, integrating RectifiedHR with emerging multi-modal models could enhance its utility in more complex generative tasks.

In summary, the paper presents RectifiedHR as a robust and efficient solution to the challenges of high-resolution image generation in diffusion models, making significant strides in both theoretical understanding and practical implementation. This work not only enhances current methodologies but also opens new avenues for future research in AI-enhanced image synthesis.