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Heat Diffusion Models -- Interpixel Attention Mechanism

Created by
  • Haebom

Author

Pengfei Zhang, Shouqing Jia

Outline

In this paper, we propose a heat diffusion model (HDM) to overcome the limitations of the denoising diffusion probabilistic model (DDPM). While DDPM processes the image as a whole, HDM introduces an attention mechanism between pixels to preserve image details and generate more realistic images, considering that adjacent pixels are more likely to belong to the same object. HDM integrates the discrete form of the two-dimensional heat equation into the diffusion and generative formulas of DDPM to compute the relationship between adjacent pixels during image processing. Experimental results show that HDM generates higher quality samples than models such as DDPM, consistent diffusion model (CDM), latent diffusion model (LDM), and vector quantized generative adversarial network (VQGAN).

Takeaways, Limitations

Takeaways:
We demonstrate that considering the relationships between pixels can better preserve image details and produce more realistic images.
A novel approach to improve the performance of DDPM-based models is presented.
It shows superior performance compared to various image generation models.
Limitations:
Lack of analysis of the computational complexity of HDM.
Additional performance evaluations on various types of image datasets are needed.
Lack of detailed description of the specific design and efficiency of the attention mechanism.
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