Multimodal diffusion framework for collaborative text image audio generation and applications
Abstract This paper presents a novel framework for collaborative generation across text, image, and audio modalities using an enhanced diffusion model architecture. We introduce a Hierarchical Cross-modal Alignment Network that establishes unified representations while preserving modality-specific c...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-05794-4 |
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| _version_ | 1849238610002313216 |
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| author | Junhua Wang Ouya Zhang Yuan Jiang |
| author_facet | Junhua Wang Ouya Zhang Yuan Jiang |
| author_sort | Junhua Wang |
| collection | DOAJ |
| description | Abstract This paper presents a novel framework for collaborative generation across text, image, and audio modalities using an enhanced diffusion model architecture. We introduce a Hierarchical Cross-modal Alignment Network that establishes unified representations while preserving modality-specific characteristics, and a Cross-modal Conditional Diffusion Model that enables flexible generation pathways through innovative conditional embedding and attention-guided mechanisms. Our approach implements cross-modal mutual guidance and consistency optimization to ensure semantic coherence across generated modalities. Experimental evaluations demonstrate significant improvements over state-of-the-art baselines, with an average 11.65% increase in tri-modal semantic alignment. Applications in media content creation, assistive technology, and education show particular promise, with user evaluations confirming enhanced information accessibility and learning experiences. While computational efficiency and domain adaptation remain challenges, this work establishes a foundation for tri-modal collaborative generation that advances multimodal content creation capabilities. |
| format | Article |
| id | doaj-art-46c2bd55db9643199fe3959c8ab047f2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-46c2bd55db9643199fe3959c8ab047f22025-08-20T04:01:34ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-05794-4Multimodal diffusion framework for collaborative text image audio generation and applicationsJunhua Wang0Ouya Zhang1Yuan Jiang2School of Computer Science, South China Business College, Guangdong University of Foreign StudiesSchool of Information Technology and Engineering, Guangzhou College of CommerceSchool of Marxism, South China Business College, Guangdong University of Foreign StudiesAbstract This paper presents a novel framework for collaborative generation across text, image, and audio modalities using an enhanced diffusion model architecture. We introduce a Hierarchical Cross-modal Alignment Network that establishes unified representations while preserving modality-specific characteristics, and a Cross-modal Conditional Diffusion Model that enables flexible generation pathways through innovative conditional embedding and attention-guided mechanisms. Our approach implements cross-modal mutual guidance and consistency optimization to ensure semantic coherence across generated modalities. Experimental evaluations demonstrate significant improvements over state-of-the-art baselines, with an average 11.65% increase in tri-modal semantic alignment. Applications in media content creation, assistive technology, and education show particular promise, with user evaluations confirming enhanced information accessibility and learning experiences. While computational efficiency and domain adaptation remain challenges, this work establishes a foundation for tri-modal collaborative generation that advances multimodal content creation capabilities.https://doi.org/10.1038/s41598-025-05794-4Multimodal diffusion modelsText-image-audio generationCross-modal alignmentConditional generationAssistive technologyMedia content creation |
| spellingShingle | Junhua Wang Ouya Zhang Yuan Jiang Multimodal diffusion framework for collaborative text image audio generation and applications Scientific Reports Multimodal diffusion models Text-image-audio generation Cross-modal alignment Conditional generation Assistive technology Media content creation |
| title | Multimodal diffusion framework for collaborative text image audio generation and applications |
| title_full | Multimodal diffusion framework for collaborative text image audio generation and applications |
| title_fullStr | Multimodal diffusion framework for collaborative text image audio generation and applications |
| title_full_unstemmed | Multimodal diffusion framework for collaborative text image audio generation and applications |
| title_short | Multimodal diffusion framework for collaborative text image audio generation and applications |
| title_sort | multimodal diffusion framework for collaborative text image audio generation and applications |
| topic | Multimodal diffusion models Text-image-audio generation Cross-modal alignment Conditional generation Assistive technology Media content creation |
| url | https://doi.org/10.1038/s41598-025-05794-4 |
| work_keys_str_mv | AT junhuawang multimodaldiffusionframeworkforcollaborativetextimageaudiogenerationandapplications AT ouyazhang multimodaldiffusionframeworkforcollaborativetextimageaudiogenerationandapplications AT yuanjiang multimodaldiffusionframeworkforcollaborativetextimageaudiogenerationandapplications |