Release MultiCompose models and MSP-Bench on Hugging Face

[NielsRogge]

Hi @I2-Multimedia-Lab 🤗

Niels here from the open-source team at Hugging Face. I discovered your work on Arxiv (MultiCompose: Multi-Concept Personalized Composition with Per-Subject Attribute Binding) and was wondering whether you would like to submit it to hf.co/papers to improve its discoverability. If you are one of the authors, you can submit it at https://huggingface.co/papers/2608.03708

The paper page lets people discuss your paper and find related artifacts (such as your models, datasets, or demo). You can also claim the paper as yours which will show up on your public profile at HF, and add your GitHub repository.

When you release the code and artifacts for MultiCompose, it'd be great to make the model checkpoints and the MSP-Bench dataset available on the 🤗 hub to improve their discoverability and visibility!

Uploading models

See here for a guide: https://huggingface.co/docs/hub/models-uploading.

In this case, we could leverage the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to any custom nn.Module. Alternatively, one can leverage the hf_hub_download one-liner to download a checkpoint from the hub.

We encourage researchers to push each model checkpoint to a separate model repository so that download stats work accurately. We can then also link the checkpoints to the paper page.

Uploading dataset

Would be awesome to make the MSP-Bench dataset available on 🤗 so that people can easily load it via:

from datasets import load_dataset

dataset = load_dataset("your-hf-org-or-username/msp-bench")

See here for a guide: https://huggingface.co/docs/datasets/loading.

Besides that, there's the dataset viewer which allows people to quickly explore the first few rows of the data directly in the browser.

Let me know if you're interested/need any help regarding this!

Cheers,

Niels
ML Engineer @ HF 🤗