Instructions to use silvertuanzi/first-train-archive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use silvertuanzi/first-train-archive with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("silvertuanzi/first-train-archive", 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 checkpoint_2000/text_encoder/pytorch_model.bin from silvertuanzi/first-train-archive: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/silvertuanzi/first-train-archive/resolve/main/checkpoint_2000/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://silvertuanzi/first-train-archive/checkpoint_2000/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/silvertuanzi/first-train-archive/resolve/main/checkpoint_2000/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 9d6ef9f75f73e57c1c8148026ec6ecd470263d402fef80ac05a1f73049ebc514
- Size of remote file:
- 492 MB
- SHA256:
- 5a536a73223d0aad893fd29e3b6095ac9b71ba6dafcb799c88f0abb4f173931f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.