Text-to-Image
Diffusers
PyTorch
StableDiffusionPipeline
stable-diffusion
diffusion-models-class
dreambooth-hackathon
landscape
Instructions to use bethecloud/golf-course-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bethecloud/golf-course-generator with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bethecloud/golf-course-generator", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of golf course with the Acropolis from Ancient Greece in the background" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- eeafd4ce9937ede69f00c21b03af0877a0736f7a30c18b50777051e391622947
- Size of remote file:
- 3.44 GB
- SHA256:
- 6e367c95c28f1beb0012e59c8a1ab3f9ee49c6c50b6c3658b3a9b49ab21a276d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.