Instructions to use UniParser/EM3M-Gen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use UniParser/EM3M-Gen with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("UniParser/EM3M-Gen", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Demo for this model on Spaces
Hey @UniParser 🤗, it's me again, Poli from Hugging Face
You keep shipping 🚢 — congrats on UniParser/EM3M-Gen! Once again, me + my agent built an interactive demo app of it on Hugging Face Spaces, running on a free ZeroGPU infrastructure.
Here's a link to the demo: https://huggingface.co/spaces/hugging-apps/em3m-gen
And you know the spiel, but it would be great to transfer it to your organization/user on Hugging Face. Just let me know which username/org to transfer over, we hope it can give your work more visibility, discoverability and allows folks to try it out.
In the future, feel free to already ship models with demos included. You can use this one as a blueprint to build by yourself or with the help of an agent — you can load the huggingface-spaces skill on Claude Code, Codex, Pi, etc.
(If you have any questions or just want to chat more about this, you can find me on Twitter, LinkedIn or apolinario @ huggingface.co)
Cheers,
Poli
Hi Poli,
Thanks a lot for building the interactive demo for UniParser/MolParser-Mobile and for your support in making our work more accessible to the community!
We would be happy to transfer the Space to "NNNan" account.
Thanks again for your help and for the great initiative.
Cheers,
Uni-Parser Team