Instructions to use vdo/potat1-10000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vdo/potat1-10000 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/potat1-10000", 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/potat1-10000: direct link, hf CLI and curl.
- Browser
- Download file 681 MB
-
https://huggingface.co/vdo/potat1-10000/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://vdo/potat1-10000/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vdo/potat1-10000/resolve/main/text_encoder/pytorch_model.bin
681 MB
- Xet hash:
- 8ccacaee5df8958b8e94996c04921f5b3dffe8a494b3539565a86718504f649c
- Size of remote file:
- 681 MB
- SHA256:
- 36f0885277abe21ebdcf4e3a19a28263246ab9b319fa3b5d231ad8f944bbf988
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