Instructions to use ATH-MaaS/TeEFusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ATH-MaaS/TeEFusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ATH-MaaS/TeEFusion", 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
Improve model card: Add pipeline_tag and enhance sample usage
#3
by nielsr HF Staff - opened
This PR improves the model card by:
- Adding
pipeline_tag: text-to-imageto the metadata for better discoverability on the Hugging Face Hub. - Updating the
tagslist to be empty, astext-to-imageis now specified inpipeline_tag. - Enhancing the 'Quick Start' sample usage by adding
trust_remote_code=Trueto thefrom_pretrainedmethod. This is essential for correctly loading custom pipelines likeTeEFusionSD3Pipeline.
Flourish changed pull request status to merged