arXiv:2606.03393v1 Announce Type: new Abstract: We propose a novel diffusion model, Flicker-DDPM, which incorporates flicker (1/f) noise inspired by self-organized criticality (SOC), a widely observed phenomenon in natural systems. Unlike denoising diffusion probabilistic models (DDPMs), which employ isotropic white noise in the forward process, Flicker-DDPM adopts colored noise with power-law spectra to better match the spectral statistics of natural images, whose power spectra typically follow P(k) proportional to 1/k^{\alpha}. To this end, we develop a colored-noise module based on a spatia
The paper was published on arXiv, indicating a current development in AI research aimed at improving diffusion models.
This development could significantly accelerate AI model training and generation, enhancing efficiency and potentially lowering compute requirements for image synthesis.
Diffusion models may become more efficient and faster, leading to quicker development cycles and broader adoption in various AI applications.
- · AI developers
- · Generative AI companies
- · Cloud computing providers
- · Digital content creators
- · Inefficient diffusion model architectures
- · Companies reliant on slow generative processes
Faster and more realistic image generation through AI.
Reduced computational costs for training and deploying generative AI models.
Accelerated innovation in areas like computer vision, synthetic data generation, and digital twin technology.
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