Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use KCS97/cat2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use KCS97/cat2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("KCS97/cat2", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of sks cat" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 31dd16a985787862e20e289bc66caea1770d8be12c9471ac014279718799098d
- Size of remote file:
- 6.88 GB
- SHA256:
- 899f3d0ed0ab69114af2d10127822d357d6d2bc6bf53aa6171a195e19b53837e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.