Unconditional Image Generation
Diffusers
Safetensors
PyTorch
StableDiffusionXLPipeline
diffusion-models-class
Instructions to use shellypeng/atomixl_realistic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use shellypeng/atomixl_realistic with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("shellypeng/atomixl_realistic", 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:
- dc1c89920af3f87818b7bd014a8f4fb6f40ed11ec86762c373cd3d64c1df9c29
- Size of remote file:
- 246 MB
- SHA256:
- 8eb11924b9e34989c8f20ac432d5e95e1baafe5e18ff2e5c7397e5d2c4f3ca45
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.