Interpretable Diffusion Models with B-cos Networks
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915372279529472 |
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| author | Bernold, Nicola Vandenhirtz, Moritz Bizeul, Alice Vogt, Julia E. |
| author_facet | Bernold, Nicola Vandenhirtz, Moritz Bizeul, Alice Vogt, Julia E. |
| contents | Text-to-image diffusion models generate images by iteratively denoising random noise, conditioned on a prompt. While these models have enabled impressive progress in image generation, they often fail to accurately reflect all semantic information described in the prompt -- failures that are difficult to detect automatically. In this work, we introduce a diffusion model architecture built with B-cos modules that offers inherent interpretability. Our approach provides insight into how individual prompt tokens affect the generated image by producing explanations that highlight the pixel regions influenced by each token. We demonstrate that B-cos diffusion models can produce high-quality images while providing meaningful insights into prompt-image alignment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03846 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpretable Diffusion Models with B-cos Networks Bernold, Nicola Vandenhirtz, Moritz Bizeul, Alice Vogt, Julia E. Computer Vision and Pattern Recognition Machine Learning Text-to-image diffusion models generate images by iteratively denoising random noise, conditioned on a prompt. While these models have enabled impressive progress in image generation, they often fail to accurately reflect all semantic information described in the prompt -- failures that are difficult to detect automatically. In this work, we introduce a diffusion model architecture built with B-cos modules that offers inherent interpretability. Our approach provides insight into how individual prompt tokens affect the generated image by producing explanations that highlight the pixel regions influenced by each token. We demonstrate that B-cos diffusion models can produce high-quality images while providing meaningful insights into prompt-image alignment. |
| title | Interpretable Diffusion Models with B-cos Networks |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2507.03846 |