Instructions to use jqsl2012/test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jqsl2012/test_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("jqsl2012/test_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
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from jqsl2012/test_model: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/jqsl2012/test_model/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://jqsl2012/test_model/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jqsl2012/test_model/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- aa2ee1d6db4c90812880751e902de354998586594ca6d183f54673b1e4bea64f
- Size of remote file:
- 492 MB
- SHA256:
- e505230406d5526da13ca3c5fb1085b7a78c62bf30bf85c2334cfce3ea29e831
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