Instructions to use pqt33/ELDiff_SD14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pqt33/ELDiff_SD14 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("pqt33/ELDiff_SD14", 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
Add model card, pipeline tag, license, and sample usage
#1
by nielsr HF Staff - opened
This PR improves the model card for ELDiff_SD14 to make it discoverable and usable on the Hugging Face Hub:
- Adds YAML metadata including
pipeline_tag: text-to-image,library_name: diffusers, andlicense: apache-2.0. - Links the model to the paper ELDiff: When Evidential Learning Meets Text-to-Image Diffusion.
- Adds a link to the official GitHub Repository.
- Includes a sample usage code snippet from the repository's README to allow users to easily run inference.