Text Embedding Adapter for Complex Image Generation
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Solution Overview
Problem
Conventional text-to-image generation models struggle with accurately depicting complex relationships and configurations in text prompts due to domain gaps in text embeddings and resource-intensive fine-tuning requirements, leading to suboptimal image generation results.
Innovation Solution
The use of an adaptor network with an ensemble architecture that generates adapted text embeddings to augment the text encoder, enabling the image generation model to accurately produce synthetic images that reflect the content described in long and complex text prompts, while reducing the need for fine-tuning and enhancing model robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional text-to-image generation models are used, then image generation can be performed, but the accuracy in depicting complex relationships and configurations in text prompts is insufficient due to domain gaps in text embeddings
Solution Approach 1:
The patent introduces an adaptor network as an intermediary component between the text encoder and the image generation model. This adaptor network transforms preliminary text embeddings into adapted text embeddings that are better suited for image generation tasks, thereby bridging the domain gap and improving the accuracy of depicting complex relationships in text prompts without requiring modifications to the original text encoder or image generation model
Solution Approach 2:
The adaptor network modifies the parameters of text embeddings by learning a transformation that adapts preliminary text embeddings to the specific requirements of image generation models. This parameter transformation enables the system to capture complex relationships and configurations more accurately by changing the representation space of text embeddings rather than altering the underlying models
2Measurement precision
If fine-tuning is applied to improve image generation accuracy, then performance improves, but resource requirements and computational costs increase significantly
Solution Approach 1:
The patent extracts the fine-tuning requirement from the entire text-to-image generation system and isolates it to a small, dedicated adaptor network. This adaptor network contains only the necessary parameters for adapting text embeddings, separating the adaptation function from the large-scale pre-trained models. This extraction enables accurate image generation while minimizing computational resources, as only the small adaptor network requires training rather than the entire system
3Measurement precision
If the text encoder is augmented to handle complex text prompts, then image generation accuracy improves, but model complexity and training requirements increase
Solution Approach 1:
The patent segments the text processing function into two distinct components: a pre-trained text encoder that generates preliminary embeddings and a small adaptor network that adapts these embeddings for image generation. This segmentation allows the system to handle complex text prompts accurately by dedicating only the necessary adaptor network to the specific task, rather than increasing the complexity of the entire text encoder architecture
4Ease of manufacture
If pre-trained text encoders are used, then resource requirements are reduced, but the ability to capture complex relationships in text prompts is limited due to domain gaps
Solution Approach 1:
The adaptor network serves as a mediator that enables pre-trained text encoders to work effectively with image generation models. It translates the general-purpose text embeddings from pre-trained encoders into task-specific adapted embeddings, allowing the system to maintain resource efficiency while achieving high adaptability to image generation tasks through the intermediary transformation layer
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining a text prompt. A text encoder encodes the text prompt to obtain a preliminary text embedding. An adaptor network generates an adapted text embedding based on the preliminary text embedding. In some cases, the adaptor network is trained to adapt the preliminary text embedding for generating an input to an image generation model. The image generation model generates a synthetic image based on the adapted text embedding. In some cases, the synthetic image includes content described by the text prompt.