Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
dreambooth
Instructions to use JwonP/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JwonP/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("JwonP/model", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of sks bowl" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 68570e6c6bc58a25722b24d41c0dafb69766260788e3667bfc1ee7741df5b6bc
- Size of remote file:
- 1.36 GB
- SHA256:
- fc3a8540dd02572a3ed0d94c7cb24f38e64a44963ad4621eb167ac4f5cb3db21
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.