Instructions to use levihsu/ootd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use levihsu/ootd with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("levihsu/ootd", 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 text_encoder/pytorch_model.bin from levihsu/ootd: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/levihsu/ootd/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://levihsu/ootd/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/levihsu/ootd/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- d4671d879e5e34b1cf891e53a667f3c257fd783e596c5a2e9f57019307354093
- Size of remote file:
- 492 MB
- SHA256:
- 770a47a9ffdcfda0b05506a7888ed714d06131d60267e6cf52765d61cf59fd67
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