Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Article Swipe
Eric Hanchen Jiang
,
Yasi Zhang
,
Zhi Zhang
,
Yixin Wan
,
Andrew Lizarraga
,
Shufan Li
,
Ying Wu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.17472
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.17472
Text-to-image (T2I) diffusion models have revolutionized generative modeling by producing high-fidelity, diverse, and visually realistic images from textual prompts. Despite these advances, existing models struggle with complex prompts involving multiple objects and attributes, often misaligning modifiers with their corresponding nouns or neglecting certain elements. Recent attention-based methods have improved object inclusion and linguistic binding, but still face challenges such as attribute misbinding and a lack of robust generalization guarantees. Leveraging the PAC-Bayes framework, we propose a Bayesian approach that designs custom priors over attention distributions to enforce desirable properties, including divergence between objects, alignment between modifiers and their corresponding nouns, minimal attention to irrelevant tokens, and regularization for better generalization. Our approach treats the attention mechanism as an interpretable component, enabling fine-grained control and improved attribute-object alignment. We demonstrate the effectiveness of our method on standard benchmarks, achieving state-of-the-art results across multiple metrics. By integrating custom priors into the denoising process, our method enhances image quality and addresses long-standing challenges in T2I diffusion models, paving the way for more reliable and interpretable generative models.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.17472
- https://arxiv.org/pdf/2411.17472
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404988814
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404988814Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2411.17472Digital Object Identifier
- Title
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Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian TheoryWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-25Full publication date if available
- Authors
-
Eric Hanchen Jiang, Yasi Zhang, Zhi Zhang, Yixin Wan, Andrew Lizarraga, Shufan Li, Ying WuList of authors in order
- Landing page
-
https://arxiv.org/abs/2411.17472Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2411.17472Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2411.17472Direct OA link when available
- Concepts
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Bayesian probability, Diffusion, Image (mathematics), Statistical physics, Computer science, Econometrics, Artificial intelligence, Mathematical economics, Economics, Physics, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.attributes, | 32 |
| abstract_inverted_index.benchmarks, | 136 |
| abstract_inverted_index.demonstrate | 128 |
| abstract_inverted_index.guarantees. | 68 |
| abstract_inverted_index.integrating | 144 |
| abstract_inverted_index.misaligning | 34 |
| abstract_inverted_index.properties, | 88 |
| abstract_inverted_index.fine-grained | 121 |
| abstract_inverted_index.Text-to-image | 0 |
| abstract_inverted_index.corresponding | 38, 98 |
| abstract_inverted_index.distributions | 84 |
| abstract_inverted_index.effectiveness | 130 |
| abstract_inverted_index.interpretable | 118, 171 |
| abstract_inverted_index.long-standing | 158 |
| abstract_inverted_index.generalization | 67 |
| abstract_inverted_index.high-fidelity, | 10 |
| abstract_inverted_index.regularization | 106 |
| abstract_inverted_index.revolutionized | 5 |
| abstract_inverted_index.attention-based | 45 |
| abstract_inverted_index.generalization. | 109 |
| abstract_inverted_index.attribute-object | 125 |
| abstract_inverted_index.state-of-the-art | 138 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |