Instructions to use ishikawa/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ishikawa/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("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ishikawa/model") prompt = "a painting of sks terada" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- bdc689110a1b6f8d62de62bf4c2825d931a59f2c4752abb997ed76cb75d875a2
- Size of remote file:
- 6.59 MB
- SHA256:
- c82a2064e34a21fa396b59df33aa2ebeffc8e0beefad2ee0b210bccf2a61c98e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.