Text-to-Pattern Generation Using Circular Convolution for Seamless Tiling
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Solution Overview
Problem
Conventional generative models struggle to seamlessly integrate the technical problem of generating seamless, seamless, and generate high-quality images that can be repeated seamlessly, seamless images that can be repeated seamlessly, with existing methods like GANs and diffusion models often failing to produce tile-able patterns that match the technical problem of existing methods failing to generate seamless, tile-able patterns, and existing methods like GANs and diffusion models often failing to produce images that do not produce images that do not produce images that match the desired aesthetic or style.
Innovation Solution
A system that includes a generation prior model and an image generation model, where the generation prior model generates a latent vector in a region of latent space corresponding to tile-able patterns, and the image generation model, which performs a circular convolution operation, to produce images that can be seamlessly tiled.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional generative models (GANs, diffusion models) are used to generate images from text, then image generation capability is achieved, but the ability to produce seamless tile-able patterns is lost
Solution Approach 1:
The patent segments the image generation process into two distinct models: a generation prior model that ensures seamless tiling capability by operating in a dedicated latent space region, and an image generation model that provides flexibility in pattern and style generation. This segmentation allows each model to specialize in one aspect, resolving the contradiction between reliable tiling and versatile pattern generation.
Solution Approach 2:
The patent introduces a latent space as an intermediary between text prompts and final image generation. The generation prior model maps text embeddings to specific regions in this latent space that correspond to tile-able patterns, while the image generation model retrieves and generates images from these latent representations. This intermediary structure enables seamless tiling while maintaining generation flexibility.
2Ease of operation
If text-only conditioning is used in diffusion models, then generation simplicity is maintained, but control over aesthetic and style is reduced
Solution Approach 1:
The patent changes the parameter space by identifying and utilizing specific regions in the latent space that correspond to tile-able patterns. Instead of relying solely on text prompt engineering, the system modifies the latent vector to ensure it falls within the identified tile-able region, providing precise control over the aesthetic property of seamless tiling while maintaining operational simplicity.
3Productivity
If standard convolution operations are used in image generation, then computational efficiency is maintained, but seamless boundary matching is achieved
Solution Approach 1:
The patent changes the convolution operation from standard to circular convolution. This parameter change in the mathematical operation ensures that when generating tile-able patterns, the boundaries wrap around seamlessly, maintaining both computational efficiency and boundary continuity for seamless tiling.
Data Source
AI summary
Systems and methods for generating tile-able patterns from text include obtaining a text prompt and generating, by a generation prior model, a latent vector based on the text prompt, where the generation prior model is trained to output vectors within a distribution of tile-able patterns. An image generation model then generates an output image based on the latent vector. The output image comprises a tile-able pattern including an element from the text prompt.