Instructions to use kaimoonstar/SD1.5_imageNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kaimoonstar/SD1.5_imageNet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kaimoonstar/SD1.5_imageNet", 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 Settings
- Draw Things
- DiffusionBee
Download classifier.pt from kaimoonstar/SD1.5_imageNet: direct link, hf CLI and curl.
- Browser
- Download file 473 kB
-
https://huggingface.co/kaimoonstar/SD1.5_imageNet/resolve/main/classifier.pt
- Command line
-
hf download hf://kaimoonstar/SD1.5_imageNet/classifier.pt
-
curl -L -o classifier.pt https://huggingface.co/kaimoonstar/SD1.5_imageNet/resolve/main/classifier.pt
473 kB
- Xet hash:
- 409754cb56985bfd3b66a7699f8ae0657a0f5a79aa054ad5a53370a3fa869d24
- Size of remote file:
- 473 kB
- SHA256:
- 75afb0d2e4c90664dfbf8c48c3196a6fb6a6fb11a46127ad88c3890aa412ea48
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