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Dynamic VLM-Guided Negative Prompting for Diffusion Models

computer-vision › diffusion-models

📄 Abstract

Abstract: We propose a novel approach for dynamic negative prompting in diffusion models that leverages Vision-Language Models (VLMs) to adaptively generate negative prompts during the denoising process. Unlike traditional Negative Prompting methods that use fixed negative prompts, our method generates intermediate image predictions at specific denoising steps and queries a VLM to produce contextually appropriate negative prompts. We evaluate our approach on various benchmark datasets and demonstrate the trade-offs between negative guidance strength and text-image alignment.

Authors (3)

Hoyeon Chang

Seungjin Kim

Yoonseok Choi

Submitted

October 30, 2025

arXiv Category

cs.CV

arXiv PDF

Key Contributions

This paper introduces a novel dynamic negative prompting method for diffusion models that leverages VLMs to adaptively generate negative prompts during denoising. This approach allows for more contextually relevant negative guidance compared to fixed prompts, leading to improved text-image alignment and control over generated content.

Business Value

Enhances creative tools for artists and designers by providing finer control over AI-generated imagery, potentially leading to more efficient content creation workflows.

Paper Metadata

Innovation Type

Algorithmic Improvement

Deployment Feasibility

Moderate. Requires integration of VLM capabilities, which can be computationally intensive.

Limitations Addressed

Limitations of fixed negative prompts in diffusion models, which can be suboptimal or irrelevant to the current generation step.

Technical Tags

negative promptingdiffusion modelsvision-language modelsdenoising processadaptive promptingtext-image alignmentintermediate predictionsguidance strength

Research Topics

Generative ModelsImage SynthesisConditional GenerationModel AdaptationPrompt Engineering

Methods & Architectures

Dynamic Negative PromptingVision-Language Model (VLM) integrationDenoising step analysis Diffusion ModelsVision-Language Models (VLMs)

Applications & Tasks

Image Generation Content Creation Controlling image generationImproving text-image alignmentReducing undesirable outputs Text-to-image synthesisImage editing

Datasets & Benchmarks

Datasets

benchmark datasets

text-image alignmentnegative guidance strength

Related Fields

Computer VisionNatural Language ProcessingGenerative AI

Keywords

diffusion modelsnegative promptingvision-language modelstext-to-imagegenerative AIdenoisingadaptive controlimage synthesisprompt engineeringconditional generation

Academic Context

#Generative Models#Image Synthesis#Conditional Generation#Model Adaptation#Prompt Engineering

Commercial Potential

Potential Products

Advanced image generation toolsContent creation platforms

Target Industries

Media and EntertainmentAdvertisingDesign

Use Case Examples

Generating specific styles of imagesAvoiding unwanted elements in generated images

Competitive Edge

Offers a more dynamic and context-aware alternative to existing static negative prompting techniques.

Market Opportunity

Growing market for generative AI tools.

Revenue Models

Licensing of technologySaaS for creative tools.

Resource Requirements

Compute Needs

High (due to diffusion models and VLM integration)

Data Requirements

Large-scale image-text paired datasets for VLM training, and datasets for diffusion model training.

Deployment Constraints

Computational cost, latency for real-time applications.

Scalability

Scalability depends on the efficiency of the VLM and diffusion model implementations.

Production Readiness

Maturity Level

Research

Time to Market

1-3 years

Patent Potential

Moderate

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