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-3000/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-3000/optimizer.bin
- Command line
-
hf download hf://snow-leopard/init_baseline_test_transformer/checkpoint-3000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/snow-leopard/init_baseline_test_transformer/resolve/main/checkpoint-3000/optimizer.bin
2.41 GB
- Xet hash:
- eb5746f453ccababc9845bdd2ba8e63bb3dcbfe672b9b27e718490943f9206f2
- Size of remote file:
- 2.41 GB
- SHA256:
- 2ca208da8b35d06e8f6ac470c03d31d48510f553c37aa56215ea2a1d6c04e9eb
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