Instructions to use EcoCy/jultest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use EcoCy/jultest with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("EcoCy/jultest") prompt = "jultest01" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-800/pytorch_model.bin from EcoCy/jultest: direct link, hf CLI and curl.
- Browser
- Download file 3.42 MB
-
https://huggingface.co/EcoCy/jultest/resolve/main/checkpoint-800/pytorch_model.bin
- Command line
-
hf download hf://EcoCy/jultest/checkpoint-800/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/EcoCy/jultest/resolve/main/checkpoint-800/pytorch_model.bin
3.42 MB
- Xet hash:
- 66cff94fdc746cba7180519552914e29b3cfa4c4d2fdc3cbde1a067011358784
- Size of remote file:
- 3.42 MB
- SHA256:
- af4ae0988adf8fd400ea56d61e56ecbab2dd7c89e37a201f2dd3458c53dc3e29
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