Instructions to use vdo/potat1-50000-base-text-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vdo/potat1-50000-base-text-encoder 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/potat1-50000-base-text-encoder", 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:
- bb8ead2c92b74bb5c310c06507035a3e365472c21c47da018d0be7a1c9540fcc
- Size of remote file:
- 1.36 GB
- SHA256:
- 2188379b05015f531d61503e714234d00a64939792f3098b324e516547f0194f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.