Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
textual_inversion
diffusers-training
Instructions to use JJSLL/Nupzook_MIT_object with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JJSLL/Nupzook_MIT_object with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_textual_inversion("JJSLL/Nupzook_MIT_object") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 83eb9cea240cd0a3be7f8c135b59d35df9e0e9a9fc41e09f6c43ea2ac9276995
- Size of remote file:
- 304 MB
- SHA256:
- 2d85f085874f5a5fba124c793da80a634fa586562e1d05c4bc0118208a5f0b47
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.