SeaCache: Revolutionary Spectral-Aware Caching for Faster AI Image Generation

📄 SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Models

🎯 What This Research Is About

Diffusion models have become the backbone of modern AI image generation, powering tools like Stable Diffusion and DALL-E. However, they suffer from a critical limitation: slow inference speed due to their sequential denoising process. SeaCache introduces a breakthrough approach by understanding and leveraging how images naturally evolve during generation.

The key insight is that diffusion models don't just randomly remove noise – they follow a spectral evolution pattern. Low-frequency structures (overall shapes and composition) appear early in the generation process, while high-frequency details (textures and fine features) are refined later. Traditional caching methods miss this pattern by treating all features equally.

💡 Why This Matters

• Training-Free Acceleration: SeaCache requires no retraining or fine-tuning – it can be applied to existing diffusion models immediately, making faster image generation accessible to everyone.
• Smarter Caching Strategy: By using a Spectral-Evolution-Aware (SEA) filter, the system intelligently identifies which intermediate computations can be reused and which need recalculation, preserving content while suppressing noise.
• State-of-the-Art Performance: Extensive experiments show SeaCache achieves the best latency-quality trade-offs compared to existing acceleration methods, meaning you get faster generation without sacrificing image quality.
• Adaptive to Content: The dynamic scheduling adapts based on the actual content being generated, rather than using a fixed schedule for all images.

🔬 Technical Innovation

The core contribution is the SEA filter, which separates content-relevant components from noise when deciding whether to reuse cached features. This spectral-aware approach aligns with the fundamental physics of how diffusion models work, leading to more intelligent caching decisions throughout the generation process.

📖 Read Full Paper →

🌐 Project Page  |  💻 GitHub Repository

---

Curated from Hugging Face daily papers • Posted on February 26, 2026