Instructions to use ProfKakeru/Jarvis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ProfKakeru/Jarvis with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nvidia/Cosmos3-Super-Text2Image", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ProfKakeru/Jarvis") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download jarvis-main.zip from ProfKakeru/Jarvis: direct link, hf CLI and curl.
- Browser
- Download file 5.45 MB
-
https://huggingface.co/ProfKakeru/Jarvis/resolve/main/jarvis-main.zip
- Command line
-
hf download hf://ProfKakeru/Jarvis/jarvis-main.zip
-
curl -L -o jarvis-main.zip https://huggingface.co/ProfKakeru/Jarvis/resolve/main/jarvis-main.zip
5.45 MB
- Xet hash:
- 2eb77deb5fe90d4b1345d6362ab38f10f25b3ac09baeb3620929be9d0da7a926
- Size of remote file:
- 5.45 MB
- SHA256:
- 4fa731ecb7a093ce464cd482a791d900689eb8a06b6ff14f0b609809e312f30e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.