Understanding the concept of dilated diffusion in DemoFusion. #TechnologyTrends

Dilated diffusion concept from DemoFusion | by Ula La Paris | Jun, 2024

The content discusses a code implementation for dilated sampling, which involves setting up a grid for sparse sampling using views and views_batch. The code gathers global information about an image by focusing on broader context rather than local details. It initializes count_global and value_global to aggregate global information and iterates through views_batch to pick pixels with a gap determined by current_scale_num. Additionally, a Gaussian filter is applied to smooth the image before sampling.

The code also implements smart blending by combining global and local details to retain both broader context and finer details in the final denoised image. Denoised views are blended with global information to create the final latent representation. The global and local values are combined and normalized to refine the image.

In conclusion, the code effectively implements dilated sampling, Gaussian filtering, and smart blending to create a denoised image that strikes a balance between capturing the big picture and refining the details. The process involves shifting the starting point for sampling to cover different parts of the image and combining global context with local details for a comprehensive result. The full code can be found on the author’s GitHub repository.

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