Instructions to use sagetae/trained_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sagetae/trained_model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("sagetae/trained_model", 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-15000/optimizer.bin from sagetae/trained_model: direct link, hf CLI and curl.
- Browser
- Download file 6.88 GB
-
https://huggingface.co/sagetae/trained_model/resolve/main/checkpoint-15000/optimizer.bin
- Command line
-
hf download hf://sagetae/trained_model/checkpoint-15000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/sagetae/trained_model/resolve/main/checkpoint-15000/optimizer.bin
6.88 GB
- Xet hash:
- 8cba9de5f267dde87594df9b0c352edcff58983065543c349c9094237b60b973
- Size of remote file:
- 6.88 GB
- SHA256:
- f64f3f2fb3340e0527038bd5824af1aeda876e7bbb0d76a75e9f97dac50b5c70
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