Instructions to use aompong/diffusion-butterfly-pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aompong/diffusion-butterfly-pipeline with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("aompong/diffusion-butterfly-pipeline", 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
Model Card for Unit 1 of the Diffusion Models Class 🧨
This model is a diffusion model for unconditional image generation of cute 🦋.
Usage
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('aompong/diffusion-butterfly-pipeline')
image = pipeline().images[0]
image
- Downloads last month
- 1
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support