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

- Xet hash:
- 2699bf9cf615b6a706036a2dddea582b667af65b0fe47a075f41e3c88ec6f8b1
- Size of remote file:
- 3.33 MB
- SHA256:
- 23690a01b58bb6b7d7627c65ba0f8b5e0c871ba61aa4bdfbafe19a6a9d472aa1
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