Instructions to use vdo/potat1-30000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vdo/potat1-30000 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/potat1-30000", 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/potat1-30000: direct link, hf CLI and curl.
- Browser
- Download file 681 MB
-
https://huggingface.co/vdo/potat1-30000/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://vdo/potat1-30000/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vdo/potat1-30000/resolve/main/text_encoder/pytorch_model.bin
681 MB
- Xet hash:
- 37a2979947abcd97c90081c2161b05b1493f771f88f860e8514bd1b336a93472
- Size of remote file:
- 681 MB
- SHA256:
- f4b8a8f2763c0f3949aefb0da2f6dccc9ac47970da34bf1434414c43eea2b954
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