Instructions to use dpredrag/AiGeneratorModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dpredrag/AiGeneratorModel with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dpredrag/AiGeneratorModel", 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
- Draw Things
- DiffusionBee
- Xet hash:
- ba6488a25f1b2b34f2b5dae20d9614a342206884471760a52382e9fde758cfca
- Size of remote file:
- 167 MB
- SHA256:
- fe24a5d9cb0132584c85ba8803c7c8434a7ca9645817ffc7f9cfede651c70d55
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.