Language Model Text Encoding for Complex Prompt Image Generation
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Solution Overview
Problem
Existing text-to-image (T2I) models suffer from compromised image quality and compositional limitations due to text encoders trained only on short prompts, requiring proper prompts and lacking flexibility in understanding complex inputs.
Innovation Solution
A modularized T2I model (M-T2I) employs a pre-trained language model as a text encoder, aligned with a target text encoder, allowing a small translation network encoder and decoder to be finetuned while the large language model remains frozen, improving computational efficiency and image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a pre-trained language model is used as text encoder, then image quality and language controllability are improved, but device complexity and computational resources increase
Solution Approach 1:
The system segments the text encoder into two parts: a frozen pre-trained language model for feature extraction and a smaller translation network for adaptation. This segmentation allows using the powerful language model without retraining it, reducing computational overhead while maintaining image quality.
Solution Approach 2:
The language model is pre-trained on large corpora before being used in the T2I system. This preliminary action enables the model to capture rich language semantics upfront, so that during T2I generation, only the translation network needs fine-tuning rather than the entire system.
2Loss of time
If text encoder is trained only on short prompts, then training time is reduced, but understanding of complex prompts deteriorates
Solution Approach 1:
The language model undergoes preliminary pre-training on extensive text data, acquiring general language understanding capabilities before being adapted to image generation tasks. This allows the system to handle complex prompts without extensive retraining.
Solution Approach 2:
The translation network acts as an intermediary between the pre-trained language model and the image generator. It translates semantic features from the language model into formats suitable for image synthesis, enabling complex prompt understanding without retraining the language model itself.
3Reliability
If existing T2I models use proper prompts, then plausible output images are achieved, but flexibility and ease of operation are reduced
Solution Approach 1:
The pre-trained language model provides universal language understanding capabilities that work across diverse prompt types and languages. Users can input natural language descriptions without following strict formatting rules, and the system reliably generates plausible images.
Solution Approach 2:
The translation network serves as a flexible intermediary that can interpret various prompt formats and languages, translating them into semantic representations that the image generator can process reliably. This decouples prompt flexibility from output reliability.
Data Source
AI summary
Embodiments described herein provide a mechanism for replacing existing text encoders in text-to-image generation models with more powerful pre-trained language models. Specifically, a translation network is trained to map features from the pre-trained language model output into the space of the target text encoder. The training preserves the rich structure of the pre-trained language model while allowing it to operate within the text-to-image generation model. The resulting modularized text-to-image model receives prompt and generates an image representing the features contained in the prompt.