Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
textual_inversion
diffusers-training
Instructions to use JJSLL/Cat_object with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JJSLL/Cat_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/Cat_object") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 91f5fa6c4a2c0f3be8ec1325fa29e598bd8c8a8ab9c7b9b397ef4ae2bc5b6a69
- Size of remote file:
- 304 MB
- SHA256:
- 74ebb4905dd4c0c59885a43142e494bbcb7c81cbebfa7f1584442a4c40b4de3e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.