Gaussian-Process Diffusion Models for Alias-Free Image and Video
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Solution Overview
Problem
Conventional diffusion models for image and video generation exhibit aliasing effects in the form of texture-sticking or flickering artifacts, which are typically addressed through ad-hoc post-processing steps.
Innovation Solution
The implementation of alias-free diffusion models using Gaussian processes and translation equivariant mechanisms, such as U-Net networks and Denoising Diffusion Implicit Models (DDIM), which minimize artifacts by leveraging Gaussian processes and Random Fourier Features to generate higher quality images and videos without aliasing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional diffusion models are used for image and video generation, then the generation process can be completed, but aliasing artifacts such as texture-sticking or flickering appear in the output
Solution Approach 1:
The patent applies preliminary action by performing anti-aliasing filtering operations during the diffusion model generation process itself, rather than as a separate post-processing step. The low-pass filter is integrated into the sampling process, preventing aliasing artifacts from forming in the first place while maintaining generation efficiency.
2Object-generated harmful factors
If ad-hoc post-processing steps are applied to remove aliasing artifacts, then the artifacts can be filtered out, but the process complexity and additional processing time increase
Solution Approach 1:
The patent merges the anti-aliasing filtering function with the existing diffusion model sampling process. The low-pass filter operation is integrated into the denoising steps, combining artifact removal with the generative process into a single unified operation, thereby eliminating the need for separate post-processing stages.
Solution Approach 2:
The diffusion model is modified to perform its own anti-aliasing through integrated low-pass filtering during sampling. The model serves its dual purpose of both generation and artifact prevention through self-contained filtering operations within the sampling loop, eliminating dependency on external post-processing systems.
Data Source
AI summary
Alias-free diffusion neural network models configured to convert input Gaussian noise and additional conditioning signals to images or video utilizing translation equivariant layers and noise signals generated by continuous Gaussian processes. The models may comprise a U-net encoder/decoder structure with noise samples derived from a Gaussian process using techniques such as Random Fourier Features approximation.