UNIC-Adapter: Unified Image-instruction Adapter with Multi-modal Transformer for Image Generation Article Swipe
Lunhao Duan
,
Shanshan Zhao
,
Wenjun Yan
,
Yinglun Li
,
Qing-Guo Chen
,
Xu Zhao
,
Weihua Luo
,
Kaifu Zhang
,
Mingming Gong
,
Gui-Song Xia
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.18928
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.18928
Recently, text-to-image generation models have achieved remarkable advancements, particularly with diffusion models facilitating high-quality image synthesis from textual descriptions. However, these models often struggle with achieving precise control over pixel-level layouts, object appearances, and global styles when using text prompts alone. To mitigate this issue, previous works introduce conditional images as auxiliary inputs for image generation, enhancing control but typically necessitating specialized models tailored to different types of reference inputs. In this paper, we explore a new approach to unify controllable generation within a single framework. Specifically, we propose the unified image-instruction adapter (UNIC-Adapter) built on the Multi-Modal-Diffusion Transformer architecture, to enable flexible and controllable generation across diverse conditions without the need for multiple specialized models. Our UNIC-Adapter effectively extracts multi-modal instruction information by incorporating both conditional images and task instructions, injecting this information into the image generation process through a cross-attention mechanism enhanced by Rotary Position Embedding. Experimental results across a variety of tasks, including pixel-level spatial control, subject-driven image generation, and style-image-based image synthesis, demonstrate the effectiveness of our UNIC-Adapter in unified controllable image generation.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.18928
- https://arxiv.org/pdf/2412.18928
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405901452
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405901452Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2412.18928Digital Object Identifier
- Title
-
UNIC-Adapter: Unified Image-instruction Adapter with Multi-modal Transformer for Image GenerationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-25Full publication date if available
- Authors
-
Lunhao Duan, Shanshan Zhao, Wenjun Yan, Yinglun Li, Qing-Guo Chen, Xu Zhao, Weihua Luo, Kaifu Zhang, Mingming Gong, Gui-Song XiaList of authors in order
- Landing page
-
https://arxiv.org/abs/2412.18928Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2412.18928Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2412.18928Direct OA link when available
- Concepts
-
Adapter (computing), Transformer, Computer science, Modal, Artificial intelligence, Electrical engineering, Engineering, Computer hardware, Voltage, Materials science, Polymer chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.remarkable | 6 |
| abstract_inverted_index.synthesis, | 165 |
| abstract_inverted_index.Transformer | 98 |
| abstract_inverted_index.conditional | 48, 126 |
| abstract_inverted_index.demonstrate | 166 |
| abstract_inverted_index.effectively | 118 |
| abstract_inverted_index.generation, | 55, 161 |
| abstract_inverted_index.generation. | 176 |
| abstract_inverted_index.information | 122, 133 |
| abstract_inverted_index.instruction | 121 |
| abstract_inverted_index.multi-modal | 120 |
| abstract_inverted_index.pixel-level | 29, 156 |
| abstract_inverted_index.specialized | 61, 114 |
| abstract_inverted_index.Experimental | 148 |
| abstract_inverted_index.UNIC-Adapter | 117, 171 |
| abstract_inverted_index.appearances, | 32 |
| abstract_inverted_index.controllable | 80, 104, 174 |
| abstract_inverted_index.facilitating | 12 |
| abstract_inverted_index.high-quality | 13 |
| abstract_inverted_index.particularly | 8 |
| abstract_inverted_index.Specifically, | 86 |
| abstract_inverted_index.advancements, | 7 |
| abstract_inverted_index.architecture, | 99 |
| abstract_inverted_index.descriptions. | 18 |
| abstract_inverted_index.effectiveness | 168 |
| abstract_inverted_index.incorporating | 124 |
| abstract_inverted_index.instructions, | 130 |
| abstract_inverted_index.necessitating | 60 |
| abstract_inverted_index.text-to-image | 1 |
| abstract_inverted_index.(UNIC-Adapter) | 93 |
| abstract_inverted_index.subject-driven | 159 |
| abstract_inverted_index.cross-attention | 141 |
| abstract_inverted_index.image-instruction | 91 |
| abstract_inverted_index.style-image-based | 163 |
| abstract_inverted_index.Multi-Modal-Diffusion | 97 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile |