Instructions to use snow-leopard/init_baseline_test_transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use snow-leopard/init_baseline_test_transformer with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("snow-leopard/init_baseline_test_transformer", 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-5000/optimizer.bin from snow-leopard/init_baseline_test_transformer: direct link, hf CLI and curl.
- Browser
- Download file 2.41 GB
-
https://huggingface.co/snow-leopard/init_baseline_test_transformer/resolve/main/checkpoint-5000/optimizer.bin
- Command line
-
hf download hf://snow-leopard/init_baseline_test_transformer/checkpoint-5000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/snow-leopard/init_baseline_test_transformer/resolve/main/checkpoint-5000/optimizer.bin
2.41 GB
- Xet hash:
- 4bf8b02c48889c623b2c24c010a90ddc53da1a9029b39e140ca891a59d31255a
- Size of remote file:
- 2.41 GB
- SHA256:
- 544d42d978b58874253bba11150758fd611f21a4a7141be3f8a3b9a098552b9c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.