MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation Article Swipe
Marco Bellagente
,
Manuel Brack
,
Hannah Teufel
,
F. Friedrich
,
Björn Deiseroth
,
Constantin Eichenberg
,
Andrew M. Dai
,
Robert Baldock
,
Souradeep Nanda
,
Koen Oostermeijer
,
Andrés Felipe Cruz-Salinas
,
Patrick Schramowski
,
Kristian Kersting
,
Samuel Weinbach
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.15296
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.15296
The recent popularity of text-to-image diffusion models (DM) can largely be attributed to the intuitive interface they provide to users. The intended generation can be expressed in natural language, with the model producing faithful interpretations of text prompts. However, expressing complex or nuanced ideas in text alone can be difficult. To ease image generation, we propose MultiFusion that allows one to express complex and nuanced concepts with arbitrarily interleaved inputs of multiple modalities and languages. MutliFusion leverages pre-trained models and aligns them for integration into a cohesive system, thereby avoiding the need for extensive training from scratch. Our experimental results demonstrate the efficient transfer of capabilities from individual modules to the downstream model. Specifically, the fusion of all independent components allows the image generation module to utilize multilingual, interleaved multimodal inputs despite being trained solely on monomodal data in a single language.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2305.15296
- https://arxiv.org/pdf/2305.15296
- OA Status
- green
- Cited By
- 6
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378510419
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4378510419Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2305.15296Digital Object Identifier
- Title
-
MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image GenerationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-24Full publication date if available
- Authors
-
Marco Bellagente, Manuel Brack, Hannah Teufel, F. Friedrich, Björn Deiseroth, Constantin Eichenberg, Andrew M. Dai, Robert Baldock, Souradeep Nanda, Koen Oostermeijer, Andrés Felipe Cruz-Salinas, Patrick Schramowski, Kristian Kersting, Samuel WeinbachList of authors in order
- Landing page
-
https://arxiv.org/abs/2305.15296Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2305.15296Direct 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
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https://arxiv.org/pdf/2305.15296Direct OA link when available
- Concepts
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Computer science, Image (mathematics), Modal, Artificial intelligence, Scratch, Natural language processing, Interface (matter), Modalities, Human–computer interaction, Programming language, Bubble, Parallel computing, Social science, Sociology, Chemistry, Maximum bubble pressure method, Polymer chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 3Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.that | 57 |
| abstract_inverted_index.them | 81 |
| abstract_inverted_index.they | 16 |
| abstract_inverted_index.with | 29, 66 |
| abstract_inverted_index.alone | 46 |
| abstract_inverted_index.being | 132 |
| abstract_inverted_index.ideas | 43 |
| abstract_inverted_index.image | 52, 122 |
| abstract_inverted_index.model | 31 |
| abstract_inverted_index.aligns | 80 |
| abstract_inverted_index.allows | 58, 120 |
| abstract_inverted_index.fusion | 115 |
| abstract_inverted_index.inputs | 69, 130 |
| abstract_inverted_index.model. | 112 |
| abstract_inverted_index.models | 6, 78 |
| abstract_inverted_index.module | 124 |
| abstract_inverted_index.recent | 1 |
| abstract_inverted_index.single | 140 |
| abstract_inverted_index.solely | 134 |
| abstract_inverted_index.users. | 19 |
| abstract_inverted_index.complex | 40, 62 |
| abstract_inverted_index.despite | 131 |
| abstract_inverted_index.express | 61 |
| abstract_inverted_index.largely | 9 |
| abstract_inverted_index.modules | 108 |
| abstract_inverted_index.natural | 27 |
| abstract_inverted_index.nuanced | 42, 64 |
| abstract_inverted_index.propose | 55 |
| abstract_inverted_index.provide | 17 |
| abstract_inverted_index.results | 99 |
| abstract_inverted_index.system, | 87 |
| abstract_inverted_index.thereby | 88 |
| abstract_inverted_index.trained | 133 |
| abstract_inverted_index.utilize | 126 |
| abstract_inverted_index.However, | 38 |
| abstract_inverted_index.avoiding | 89 |
| abstract_inverted_index.cohesive | 86 |
| abstract_inverted_index.concepts | 65 |
| abstract_inverted_index.faithful | 33 |
| abstract_inverted_index.intended | 21 |
| abstract_inverted_index.multiple | 71 |
| abstract_inverted_index.prompts. | 37 |
| abstract_inverted_index.scratch. | 96 |
| abstract_inverted_index.training | 94 |
| abstract_inverted_index.transfer | 103 |
| abstract_inverted_index.diffusion | 5 |
| abstract_inverted_index.efficient | 102 |
| abstract_inverted_index.expressed | 25 |
| abstract_inverted_index.extensive | 93 |
| abstract_inverted_index.interface | 15 |
| abstract_inverted_index.intuitive | 14 |
| abstract_inverted_index.language, | 28 |
| abstract_inverted_index.language. | 141 |
| abstract_inverted_index.leverages | 76 |
| abstract_inverted_index.monomodal | 136 |
| abstract_inverted_index.producing | 32 |
| abstract_inverted_index.attributed | 11 |
| abstract_inverted_index.components | 119 |
| abstract_inverted_index.difficult. | 49 |
| abstract_inverted_index.downstream | 111 |
| abstract_inverted_index.expressing | 39 |
| abstract_inverted_index.generation | 22, 123 |
| abstract_inverted_index.individual | 107 |
| abstract_inverted_index.languages. | 74 |
| abstract_inverted_index.modalities | 72 |
| abstract_inverted_index.multimodal | 129 |
| abstract_inverted_index.popularity | 2 |
| abstract_inverted_index.MultiFusion | 56 |
| abstract_inverted_index.MutliFusion | 75 |
| abstract_inverted_index.arbitrarily | 67 |
| abstract_inverted_index.demonstrate | 100 |
| abstract_inverted_index.generation, | 53 |
| abstract_inverted_index.independent | 118 |
| abstract_inverted_index.integration | 83 |
| abstract_inverted_index.interleaved | 68, 128 |
| abstract_inverted_index.pre-trained | 77 |
| abstract_inverted_index.capabilities | 105 |
| abstract_inverted_index.experimental | 98 |
| abstract_inverted_index.Specifically, | 113 |
| abstract_inverted_index.multilingual, | 127 |
| abstract_inverted_index.text-to-image | 4 |
| abstract_inverted_index.interpretations | 34 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 14 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |