Instructions to use dranzerstar/SD-textual-inversion-embeddings-repo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dranzerstar/SD-textual-inversion-embeddings-repo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dranzerstar/SD-textual-inversion-embeddings-repo", 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
Download ROsample.png from dranzerstar/SD-textual-inversion-embeddings-repo: direct link, hf CLI and curl.
- Browser
- Download file 3.3 MB
-
https://huggingface.co/dranzerstar/SD-textual-inversion-embeddings-repo/resolve/main/ROsample.png
- Command line
-
hf download hf://dranzerstar/SD-textual-inversion-embeddings-repo/ROsample.png
-
curl -L -o ROsample.png https://huggingface.co/dranzerstar/SD-textual-inversion-embeddings-repo/resolve/main/ROsample.png
3.3 MB

- Xet hash:
- ea7b2e61f4af785cf6abd250b4f5d7094e736065091500406fc9eeeff797c065
- Size of remote file:
- 3.3 MB
- SHA256:
- d196cb98a3c35da37fea088cd3f14f69c26dce058bf1fb19355d5df8b57ef2c7
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