Instructions to use licyk/invokeai-core-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use licyk/invokeai-core-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("licyk/invokeai-core-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 pypatchmatch/clip_python_package.zip from licyk/invokeai-core-model: direct link, hf CLI and curl.
- Browser
- Download file 4.32 MB
-
https://huggingface.co/licyk/invokeai-core-model/resolve/main/pypatchmatch/clip_python_package.zip
- Command line
-
hf download hf://licyk/invokeai-core-model/pypatchmatch/clip_python_package.zip
-
curl -L -o clip_python_package.zip https://huggingface.co/licyk/invokeai-core-model/resolve/main/pypatchmatch/clip_python_package.zip
4.32 MB
- Xet hash:
- c3925f522e6842cb1f96599e6a965f2c118dbf0023a6ed8af83931cdd12cc4bc
- Size of remote file:
- 4.32 MB
- SHA256:
- b5842c25da441d6c581b53a5c60e0c2127ebafe0f746f8e15561a006c6c3be6a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.