Leveraging Diffusion Models for Automated and Creative Digital Media Asset Generation
Year: 2026
Type: conference-paper
Abstract: This paper proposes the Creative Constrained Diffusion Model (CCDM), a novel diffusion-based generative framework designed to achieve high-fidelity image synthesis under multi-level creative constraints. CCDM extends the standard Denoising Diffusion Probabilistic Model by introducing a Multi-Fidelity Context Encoding (MFCE) mechanism, which injects high-level semantic guidance, mid-level quantitative constraints, and low-level spatial structures in a coordinated manner. In addition, a Constraint-Aware Classifier-Free Guidance (CA-CFG) strategy is proposed to dynamically adjust guidance strength based on estimated constraint adherence, effectively balancing controllability and visual quality while reducing over-guidance artifacts. Extensive experiments on multiple datasets demonstrate that CCDM significantly outperforms existing controllable diffusion methods in terms of generation quality, semantic alignment, constraint satisfaction, and robustness. The results validate CCDM as a practical and scalable solution for automated, high-precision digital media asset generation.
Author: Xin Chen
Institution: Kunyu Vocational and Technical College
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7224948670