lta/ModelScopeT2V.yaml at preview · datalets/lta · GitHub

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---
# Thank you for contributing!
# In filling out this yaml file, please follow the criteria as described here:
# https://osai-index.eu/contribute

# You're free to build on this work and reuse the data. It is licensed under CC-BY 4.0, with the
# stipulation that attribution should come in the form of a link to https://osai-index.eu/
# and a citation to the peer-reviewed paper in which the dataset & criteria were published:

# Liesenfeld, A. and Dingemanse, M., 2024. Rethinking open source generative AI: open-washing and the EU AI Act. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 1774-1787).

# Organization tags:
# - National origin: China
# - Contributor type: Non-academic (Chinese Big Tech)

system:
    name: ModelScopeT2V
    link: https://modelscope.cn/models/iic/text-to-video-synthesis/summary
    type: video
    performanceclass: limited
    basemodelname: Stable Diffusion (version unknown)
    endmodelname: ModelScopeT2V-V1.5
    endmodellicense: CC-BY-NC-ND
    releasedate: 2023-02
    notes: Video-generation model based on Stable Diffusion.

org:
    name: Tongyi Lab
    link: https://careers-tongyi.alibaba.com/home
    notes: Tonyi Lab, a lab under Alibaba.

# availability:
datasources_basemodel:
    class: partial
    link: https://arxiv.org/abs/2210.08402
    notes: Originally trained on LAION-5B, which is ostensibly open. In general the language used is not very clear, however.

datasources_endmodel:
    class: partial
    link: https://arxiv.org/pdf/2308.06571
    notes: Trained on LAION-5B, WebVid, and MSR-VTT. Unclear what data was used to train V1.5.

weights_basemodel:
    class: partial
    link: https://huggingface.co/stabilityai/stable-diffusion-3.5-large
    notes: Available through HuggingFace, however requires providing contact information and agreeing to a license agreement. Version is also unknown.

weights_endmodel:
    class: open
    link: https://modelscope.cn/models/iic/text-to-video-synthesis/summary
    notes: Model made available through ModelScope.

trainingcode:
    class: open
    link: https://github.com/ali-vilab/VGen
    notes: Code made available on GitHub.

# documentation:
code:
    class: open
    link: https://github.com/ali-vilab/VGen
    notes: Repo thoroughly documented.

hardware_architecture:
    class: open
    link: ["https://arxiv.org/pdf/2308.06571", "https://github.com/ali-vilab/VGen/blob/main/configs/t2v_train.yaml"]
    notes: Training setup disclosed in paper, config published on GitHub.

preprint:
    class: open
    link: https://arxiv.org/pdf/2308.06571
    notes: Preprint available on arXiv.

paper:
    class: closed
    link:
    notes: No peer-reviewed paper found.

modelcard:
    class: partial
    link: https://modelscope.cn/models/iic/text-to-video-synthesis/summary
    notes: Model card primarily contain information about inference, with some description of training data.

datasheet:
    class: closed
    link:
    notes: No datasheet found.

# access:
package:
    class: closed
    link:
    notes: No package found.

api:
    class: closed
    link:
    notes: No API found.
    metaprompts:

licenses:
    class: open
    link: https://modelscope.cn/models/iic/text-to-video-synthesis/summary
    notes: CC-BY-NC-ND