Hi @anticdimi 馃
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work through Hugging Face's daily papers as yours got featured: https://huggingface.co/papers/2604.20800.
The paper page lets people discuss about your paper and find related artifacts (your models 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.
I noticed in your GitHub roadmap that you plan to release inference code, pre-trained models, and even a Gradio demo on Hugging Face Spaces. That's fantastic!
Would you like to host the pre-trained checkpoints for LEXIS and LEXIS-Flow on https://huggingface.co/models? Hosting on Hugging Face will give your work more visibility and enable better discoverability through metadata tags (like image-to-3d).
If you're down, I'm leaving a guide here. For custom PyTorch models, you can use the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to the model, making it very easy for others to use your work.
Regarding the Gradio demo on Spaces, we can provide you a ZeroGPU grant, which gives you A100 GPUs for free to power your demo.
Let me know if you're interested or need any guidance!
Kind regards,
Niels
ML Engineer @ HF 馃
Hi @anticdimi 馃
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work through Hugging Face's daily papers as yours got featured: https://huggingface.co/papers/2604.20800.
The paper page lets people discuss about your paper and find related artifacts (your models 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.
I noticed in your GitHub roadmap that you plan to release inference code, pre-trained models, and even a Gradio demo on Hugging Face Spaces. That's fantastic!
Would you like to host the pre-trained checkpoints for LEXIS and LEXIS-Flow on https://huggingface.co/models? Hosting on Hugging Face will give your work more visibility and enable better discoverability through metadata tags (like
image-to-3d).If you're down, I'm leaving a guide here. For custom PyTorch models, you can use the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto the model, making it very easy for others to use your work.Regarding the Gradio demo on Spaces, we can provide you a ZeroGPU grant, which gives you A100 GPUs for free to power your demo.
Let me know if you're interested or need any guidance!
Kind regards,
Niels
ML Engineer @ HF 馃