Semantic Image Fill With Sparse Attention for High-Resolution Editing
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Solution Overview
Problem
Conventional image fill techniques face challenges in handling high-resolution images, requiring excessive computational resources and failing to accurately model long-range dependencies, resulting in unrealistic outputs for complex scenes.
Innovation Solution
A semantic fill system generates a guidance attention map at a lower resolution to guide the generation of a high-resolution sparse attention map, leveraging autoregressive transformers to efficiently capture both short- and long-range dependencies, reducing computational resources and improving image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image fill techniques are applied to high-resolution images, then image fill functionality is achieved, but computational resources required increase excessively
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, resolving the contradiction between fill quality and computational cost.
Solution Approach 2:
The patent introduces a resolution dimension by computing the guidance attention map at a downsampled version of the image and then using it to guide sparse attention at the original high resolution. This multi-resolution approach allows efficient capture of long-range dependencies without the quadratic computational cost of full-resolution attention, resolving the contradiction between quality and resource usage.
2Reliability
If conventional image fill techniques based on surrounding pixels are used, then local detail is preserved, but ability to determine long-range dependencies is lost
Solution Approach 1:
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 step identifies key regions and long-range dependencies that guide the subsequent high-resolution attention computation, ensuring both long-range accuracy and local detail preservation.
Solution Approach 2:
The patent applies different quality levels at different regions: the guidance attention map provides coarse-grained long-range dependency information at downsampled resolution, while the sparse attention map provides fine-grained local detail at full resolution for key regions. This local quality differentiation resolves the contradiction between capturing long-range dependencies and preserving local realism.
3Manufacturing precision
If full-resolution attention maps are computed for semantic image fill, then accurate content generation is achieved, but processing time increases significantly
Solution Approach 1:
The patent segments the attention computation process into a fast guidance phase at downsampled resolution and a targeted sparse attention phase at full resolution. This segmentation computes only the necessary high-resolution attention for key regions identified by the guidance map, achieving accurate content generation without the prohibitive processing time of full-resolution attention maps.
Solution Approach 2:
The patent computes partial attention at full resolution rather than complete attention everywhere. By using the guidance attention map to identify key regions, the system performs excessive action (computing guidance map at downsampled resolution) to enable partial but sufficient action (sparse attention at full resolution only where needed), resolving the contradiction between accuracy and processing time.
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.