arxiv_ai 95% Match Research Paper Researchers in generative AI,Developers of text-to-image models,Artists using AI tools 1 week ago

Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models

generative-ai › diffusion

📄 Abstract

Abstract: Text-to-image diffusion models rely on text embeddings from a pre-trained text encoder, but these embeddings remain fixed across all diffusion timesteps, limiting their adaptability to the generative process. We propose Diffusion Adaptive Text Embedding (DATE), which dynamically updates text embeddings at each diffusion timestep based on intermediate perturbed data. We formulate an optimization problem and derive an update rule that refines the text embeddings at each sampling step to improve alignment and preference between the mean predicted image and the text. This allows DATE to dynamically adapts the text conditions to the reverse-diffused images throughout diffusion sampling without requiring additional model training. Through theoretical analysis and empirical results, we show that DATE maintains the generative capability of the model while providing superior text-image alignment over fixed text embeddings across various tasks, including multi-concept generation and text-guided image editing. Our code is available at https://github.com/aailab-kaist/DATE.

Authors (9)

Byeonghu Na

Minsang Park

Gyuwon Sim

Donghyeok Shin

HeeSun Bae

Mina Kang

+3 more

Submitted

October 28, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

Proposes Diffusion Adaptive Text Embedding (DATE), a training-free method that dynamically updates text embeddings at each diffusion timestep. By formulating an optimization problem to refine embeddings based on intermediate data, DATE improves text-image alignment and generation quality without requiring additional model training, enhancing control over the generative process.

Business Value

Enables the creation of more accurate and controllable text-to-image generation tools, leading to higher quality artistic outputs and more precise visual content creation for marketing, design, and entertainment.

Paper Metadata

Innovation Type

Methodological

Deployment Feasibility

High, as it's a training-free method that can be integrated into existing diffusion model inference pipelines.

Limitations Addressed

Fixed text embeddings in diffusion models that do not adapt to the generative process,Suboptimal alignment between text prompts and generated images

Performance Gains

Superior text-image alignment over fixed text embeddings.

Technical Tags

text-to-image diffusion modelsadaptive text embeddingdynamic text conditioningdiffusion timestepsoptimization problemtext-image alignmentmulti-concept generationtraining-free

Research Topics

Text-to-Image GenerationDiffusion ModelsConditional GenerationRepresentation LearningGenerative AI

Methods & Architectures

Diffusion Adaptive Text Embedding (DATE)Dynamic text embedding updatesOptimization-based refinementTraining-free adaptation Text-to-Image Diffusion Models

Applications & Tasks

Creative AI Content Generation Art Generation Fixed text embeddings limiting adaptabilitySuboptimal text-image alignmentDifficulty in controlling generation across timesteps Improved text-to-image generationEnhanced text-image alignmentMulti-concept image synthesis

Related Fields

Generative AIDiffusion ModelsNatural Language ProcessingComputer Vision

Keywords

text-to-imagediffusion modelstext embeddingadaptivedynamic conditioninggenerative AItraining-freetext-image alignmentmulti-concept generationinference time adaptation

Academic Context

#Text-to-Image Generation#Diffusion Models#Conditional Generation#Representation Learning#Generative AI

Technology Stack

Frameworks & Libraries

PyTorch

Programming Languages

Python

Commercial Potential

Potential Products

Enhanced text-to-image generation APIsCreative tools for artistsPersonalized content generation platforms

Target Industries

Media and EntertainmentAdvertisingDesignGaming

Use Case Examples

Generating images that precisely match complex or nuanced text descriptions.Creating variations of an image based on slightly modified text prompts.Synthesizing images with multiple distinct objects described in the text.

Competitive Edge

Improves upon standard text-to-image diffusion models by offering dynamic text conditioning during inference, leading to better alignment and control without the need for retraining.

Market Opportunity

Large market for generative AI and creative tools.

Revenue Models

Integration into existing AI serviceslicensing to tool developers.

Resource Requirements

Compute Needs

Moderate increase during inference compared to standard diffusion models.

Data Requirements

Leverages existing pre-trained text-to-image diffusion models; no new large-scale dataset collection required for DATE itself.

Deployment Constraints

Requires integration into the sampling loop of diffusion models.

Scalability

Scales with the underlying diffusion model's inference capabilities.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for integration into existing platforms.

Patent Potential

Moderate to High, for the DATE methodology.

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