High-Resolution Semantic Image Fill Using Sparse Attention
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Solution Overview
Problem
Conventional image fill techniques for high-resolution images require excessive computational resources and fail to accurately model long-range dependencies, resulting in unrealistic outputs.
Innovation Solution
A semantic fill system generates a guidance attention map at a lower resolution to guide the creation of a sparse attention map at the original resolution, leveraging autoregressive transformers to efficiently capture long-range dependencies and reduce computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image fill techniques are applied to high-resolution images, then the fill content can be generated, but large amounts of computational resources are required resulting in inhibitive user wait times
Solution Approach 1:
The patent segments the attention computation into two parts: (1) a guidance attention map computed at downsampled resolution, and (2) a sparse attention map computed at full resolution using only key regions identified from the guidance map. This segmentation allows the system to capture global dependencies efficiently while maintaining high-resolution detail where needed, reducing overall computational time.
Solution Approach 2:
The patent performs preliminary computation of the guidance attention map at downsampled resolution before computing the final sparse attention map at full resolution. This preliminary action identifies key regions in advance, allowing the subsequent high-resolution attention computation to be focused only on relevant areas, thereby reducing total computation time.
2Productivity
If conventional image fill techniques based on surrounding pixels are used, then the process is computationally efficient, but the techniques fail to accurately determine long-range dependencies resulting in unrealistic outputs for complicated scenes
Solution Approach 1:
The patent transitions from local pixel-based attention to a multi-resolution attention mechanism by computing attention at both downsampled and full resolutions. This dimensional change allows the model to capture long-range dependencies across the entire image while maintaining computational efficiency through the guidance map, producing realistic outputs for complicated scenes.
3Manufacturing precision
If full-resolution attention maps are computed for all regions, then long-range dependencies can be captured, but the computational resources required become prohibitive
Solution Approach 1:
The patent applies local quality by computing full-resolution sparse attention maps only for key regions identified from the guidance attention map, rather than computing attention for all regions uniformly. This selective approach maintains accurate long-range dependency modeling where needed while reducing computational resource consumption in less critical areas.
Solution Approach 2:
The patent performs partial attention computation by focusing full-resolution attention only on key regions (a subset of the entire image) rather than computing attention for all regions. This partial action is sufficient to capture essential long-range dependencies while significantly reducing computational resource requirements.
Data Source
AI summary
Semantic fill techniques are described that support generating fill and editing images from semantic inputs. A user input, for example, is received by a semantic fill system that indicates a selection of a first region of a digital image and a corresponding semantic label. The user input is utilized by the semantic fill system to generate a guidance attention map of the digital image. The semantic fill system leverages the guidance attention map to generate a sparse attention map of a second region of the digital image. A semantic fill of pixels is generated for the first region based on the semantic label and the sparse attention map. The edited digital image is displayed in a user interface.