ResilPhase: Plug-and-Play Phase Mapping and Noise-Resilient Macro-Trajectory Extrapolation for Diffusion Acceleration
arXiv:2606.26769v1 Announce Type: new Abstract: The adoption of powerful diffusion models is hindered by their significant inference latency. Recent ``cache-then-forecast'' schemes alleviate this issue by accelerating DiTs using derivative-based polynomials, but they suffer from severe quality degradation at high acceleration ratios. Our analysis reveals its root cause: the discrete extrapolation performed on representations that are misaligned with the continuous diffusion trajectory and are numerically unstable. Thus, accelerated DiTs suffer from accumulated spatial errors, noisy derivative
The continuous push to enhance diffusion model efficiency necessitates innovative solutions for inference acceleration without compromising quality, making advancements in phase mapping and noise resilience timely.
Improving the inference speed of diffusion models has significant implications for their widespread adoption and scalability across various AI applications, reducing computational costs and time-to-result.
This research introduces methodologies that promise to significantly accelerate diffusion models while maintaining quality, addressing a key bottleneck in their practical deployment.
- · AI developers using diffusion models
- · Cloud computing providers
- · Industries relying on generative AI
- · Hardware manufacturers for AI acceleration
- · Inefficient diffusion model architectures
- · Organizations with high inference latency requirements
Faster diffusion models lead to more rapid content generation and iterative design processes.
Reduced computational costs empower broader access and experimentation with advanced generative AI applications.
The widespread, rapid deployment of high-quality generative AI could accelerate innovation across numerous sectors, potentially leading to new product categories and business models.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI