https://www.arxiv.org/abs/2408.11039

close this message
arXiv Accessibility Forum 2024

Skip to main content
Cornell University

This week: the arXiv Accessibility Forum

Forum Schedule
We gratefully acknowledge support from the Simons Foundation, member
institutions, and all contributors. Donate
 
arxiv logo > cs > arXiv:2408.11039
[                    ]

Help | Advanced Search

[All fields        ]
Search
arXiv logo
Cornell University Logo
[                    ] GO
quick links

  * Login
  * Help Pages
  * About

Computer Science > Artificial Intelligence

arXiv:2408.11039 (cs)
[Submitted on 20 Aug 2024]

Title:Transfusion: Predict the Next Token and Diffuse Images with One
Multi-Modal Model

Authors:Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro
Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer,
Omer Levy
View a PDF of the paper titled Transfusion: Predict the Next Token
and Diffuse Images with One Multi-Modal Model, by Chunting Zhou and
Lili Yu and Arun Babu and Kushal Tirumala and Michihiro Yasunaga and
Leonid Shamis and Jacob Kahn and Xuezhe Ma and Luke Zettlemoyer and
Omer Levy
View PDF HTML (experimental)

    Abstract:We introduce Transfusion, a recipe for training a
    multi-modal model over discrete and continuous data. Transfusion
    combines the language modeling loss function (next token
    prediction) with diffusion to train a single transformer over
    mixed-modality sequences. We pretrain multiple Transfusion models
    up to 7B parameters from scratch on a mixture of text and image
    data, establishing scaling laws with respect to a variety of uni-
    and cross-modal benchmarks. Our experiments show that Transfusion
    scales significantly better than quantizing images and training a
    language model over discrete image tokens. By introducing
    modality-specific encoding and decoding layers, we can further
    improve the performance of Transfusion models, and even compress
    each image to just 16 patches. We further demonstrate that
    scaling our Transfusion recipe to 7B parameters and 2T
    multi-modal tokens produces a model that can generate images and
    text on a par with similar scale diffusion models and language
    models, reaping the benefits of both worlds.

Comments: 23 pages
Subjects: Artificial Intelligence (cs.AI); Computer Vision and
          Pattern Recognition (cs.CV)
Cite as:  arXiv:2408.11039 [cs.AI]
          (or arXiv:2408.11039v1 [cs.AI] for this version)
          https://doi.org/10.48550/arXiv.2408.11039
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Chunting Zhou [view email]
[v1] Tue, 20 Aug 2024 17:48:20 UTC (14,114 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled Transfusion: Predict the Next
    Token and Diffuse Images with One Multi-Modal Model, by Chunting
    Zhou and Lili Yu and Arun Babu and Kushal Tirumala and Michihiro
    Yasunaga and Leonid Shamis and Jacob Kahn and Xuezhe Ma and Luke
    Zettlemoyer and Omer Levy
  * View PDF
  * HTML (experimental)
  * TeX Source
  * Other Formats

license icon view license
Current browse context:
cs.AI
< prev   |   next >
new | recent | 2024-08
Change to browse by:
cs
cs.CV

References & Citations

  * NASA ADS
  * Google Scholar
  * Semantic Scholar

a export BibTeX citation Loading...

BibTeX formatted citation

x
[loading...          ]
Data provided by:

Bookmark

BibSonomy logo Reddit logo
(*) Bibliographic Tools

Bibliographic and Citation Tools

[ ] Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
[ ] Litmaps Toggle
Litmaps (What is Litmaps?)
[ ] scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
( ) Code, Data, Media

Code, Data and Media Associated with this Article

[ ] Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
[ ] DagsHub Toggle
DagsHub (What is DagsHub?)
[ ] GotitPub Toggle
Gotit.pub (What is GotitPub?)
[ ] Links to Code Toggle
Papers with Code (What is Papers with Code?)
[ ] ScienceCast Toggle
ScienceCast (What is ScienceCast?)
( ) Demos

Demos

[ ] Replicate Toggle
Replicate (What is Replicate?)
[ ] Spaces Toggle
Hugging Face Spaces (What is Spaces?)
[ ] Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
( ) Related Papers

Recommenders and Search Tools

[ ] Link to Influence Flower
Influence Flower (What are Influence Flowers?)
[ ] Connected Papers Toggle
Connected Papers (What is Connected Papers?)
[ ] Core recommender toggle
CORE Recommender (What is CORE?)

  * Author
  * Venue
  * Institution
  * Topic

( ) About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and
share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have
embraced and accepted our values of openness, community, excellence,
and user data privacy. arXiv is committed to these values and only
works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?
Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is
MathJax?)

  * About
  * Help

  * Click here to contact arXiv Contact
  * Click here to subscribe Subscribe

  * Copyright
  * Privacy Policy

  * Web Accessibility Assistance
  * arXiv Operational Status
    Get status notifications via email or slack