Instructions to use kxic/EscherNet_demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kxic/EscherNet_demo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kxic/EscherNet_demo", torch_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
- Xet hash:
- 4fa0879aca17e37382266ff335d69d43fc4406ad8288476decdf8cf938832d8b
- Size of remote file:
- 112 MB
- SHA256:
- ce857dbd0eb848c2a212f78f4b5e6a72ce300ace1a83e9dffb69e14cd7be8e2f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.