Instructions to use poppuppy/secant-expectation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use poppuppy/secant-expectation with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("poppuppy/secant-expectation", 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
Improve model card: Add pipeline tag, library name, paper & code links
#1
by nielsr HF Staff - opened
This PR improves the model card by:
- Adding the
pipeline_tag: unconditional-image-generationto ensure the model is discoverable under this task. - Adding
library_name: diffusersas the model is compatible with the Diffusers library, which enables the "How to use" widget. - Adding a direct link to the paper: https://huggingface.co/papers/2505.14502.
- Adding a link to the GitHub repository: https://github.com/poppuppy/secant_expectation.
Please review and merge this PR if everything looks good.
Hi, thanks for your PR. I wonder if unconditional-image-generation should be class-conditional?
That tag does not exist :/there's only text-to-image. You could of course add "tags: class-conditional-generation", it's just not one of the default tasks.
ok thank you
poppuppy changed pull request status to merged