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Published on: April 16, 2014
Prompt-Guided Semantic Latent Direction Learning in Diffusion Models for Abstract Visual Concept Manipulation
Mahzaib Khalid1, Fangli Ying1, Al-Garadi Ahmed Mohammed Atef1
1Department of Computer Science, East China University of Science and Technology, Shanghai 200237, China.
Journal of Imaging
|July 27, 2026
Summary
This study introduces a new framework for controlling abstract concepts in diffusion models. It enables semantic image manipulation while preserving structure, offering a parameter-efficient approach for concept-level editing.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Diffusion models generate high-fidelity images but struggle with abstract concept control.
- Textual descriptions for concepts are often ambiguous, hindering precise manipulation.
- Existing methods require extensive human annotations or specific data.
Purpose of the Study:
- To develop a prompt-guided framework for controllable manipulation of abstract concepts in diffusion models.
- To enable concept editing without human-annotated image pairs, segmentation masks, or identity labels.
- To achieve semantic modification of real images while preserving structural integrity.
Main Methods:
- Introduced a learnable concept vector optimized in the bottleneck feature space of a pretrained Stable Diffusion U-Net.
- Froze all pretrained model parameters, ensuring parameter efficiency.
- Employed a multi-prompt data generation strategy with paired positive and neutral prompts for weak semantic guidance.
- Applied the learned vector in an image-to-image setting via controlled noise injection and concept-guided denoising.
- Utilized scaling parameters γ and β to control concept strength and image-to-image noise, balancing semantic modification and structural fidelity.
Main Results:
- Demonstrated improved semantic alignment and structural preservation compared to baseline Stable Diffusion image-to-image methods.
- Quantitative evaluations using SSIM, LPIPS, and CLIP similarity validated the method's effectiveness.
- Human preference studies showed significant user preference for concept-injected outputs (76.0% for perfect skin, 85.7% for peaceful lake).
- Ablation studies confirmed the framework's controllability and robustness.
Conclusions:
- The proposed method offers a simple, parameter-efficient, and interpretable approach for concept-level manipulation in diffusion models.
- It effectively addresses the challenge of controlling abstract visual concepts without extensive manual annotations.
- The framework facilitates controllable semantic modification of images while maintaining structural fidelity.
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