Unconditional Image Generation
Diffusers
TensorBoard
Safetensors
PyTorch
DDPMPipeline
diffusion-models-class
Instructions to use afshr/camnormal_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use afshr/camnormal_test with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("afshr/camnormal_test", 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:
- 0fb9518cc59ad877c32ef137a4e87eee01c6ba237d23ebc106997a32b5b643de
- Size of remote file:
- 143 MB
- SHA256:
- 0ced3ce461670bffda9ebe7146ae7a03eec71049b34cff04088ef8aef2192b43
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.