Instructions to use vdo/potat1-20000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vdo/potat1-20000 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vdo/potat1-20000", 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 vdo/potat1-20000: direct link, hf CLI and curl.
- Browser
- Download file 681 MB
-
https://huggingface.co/vdo/potat1-20000/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://vdo/potat1-20000/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vdo/potat1-20000/resolve/main/text_encoder/pytorch_model.bin
681 MB
- Xet hash:
- 8212c0b8d96dc63be2d24e458e254f3927f9db6208fe5ec168a61a1c5ed397cc
- Size of remote file:
- 681 MB
- SHA256:
- 5b189e7150a50bb0d01c3269c7de1d042112a1592d7cca4d9fa5d0399aa18620
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