Hi @HaoyChen 🤗
Niels here from the open-source team at Hugging Face. I discovered your work on Arxiv 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/submit.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models, datasets or demo for instance), you can also claim the paper as yours which will show up on your public profile at HF, add Github and project page URLs.
It'd be great to make the Any2Any checkpoints and the RST-1M dataset available on the 🤗 hub, to improve their discoverability and visibility. I noticed you are currently using Baidu Pan for the VAE weights; hosting on Hugging Face would make it much easier for the global community to download and use them directly.
Uploading models
See here for a guide: https://huggingface.co/docs/hub/models-uploading.
In this case, for your latent diffusion framework, you 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 (the VAE and the various diffusion scales) to separate model repositories so that download stats and model cards work effectively.
Uploading dataset
I see the million-scale RST-1M dataset is "Coming Soon." It would be awesome to host this on 🤗, so that people can easily load it via the library:
from datasets import load_dataset
dataset = load_dataset("your-hf-org-or-username/RST-1M")See here for a guide: https://huggingface.co/docs/datasets/loading.
Besides that, there's the dataset viewer which allows researchers to quickly explore these remote sensing modalities directly in the browser.
Let me know if you're interested or need any help regarding this!
Kind regards,
Niels
ML Engineer @ HF 🤗
Hi @HaoyChen 🤗
Niels here from the open-source team at Hugging Face. I discovered your work on Arxiv 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/submit.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models, datasets or demo for instance), you can also claim the paper as yours which will show up on your public profile at HF, add Github and project page URLs.
It'd be great to make the Any2Any checkpoints and the RST-1M dataset available on the 🤗 hub, to improve their discoverability and visibility. I noticed you are currently using Baidu Pan for the VAE weights; hosting on Hugging Face would make it much easier for the global community to download and use them directly.
Uploading models
See here for a guide: https://huggingface.co/docs/hub/models-uploading.
In this case, for your latent diffusion framework, you could leverage the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto any customnn.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 (the VAE and the various diffusion scales) to separate model repositories so that download stats and model cards work effectively.
Uploading dataset
I see the million-scale RST-1M dataset is "Coming Soon." It would be awesome to host this on 🤗, so that people can easily load it via the library:
See here for a guide: https://huggingface.co/docs/datasets/loading.
Besides that, there's the dataset viewer which allows researchers to quickly explore these remote sensing modalities directly in the browser.
Let me know if you're interested or need any help regarding this!
Kind regards,
Niels
ML Engineer @ HF 🤗