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:
- d45423e16040229d56c96d2891b5cabd906ea8f8963ca4e4d8971ace415d1acd
- Size of remote file:
- 6.59 MB
- SHA256:
- de8b53c1a274192062367ead0b9e21bf68a3761aa2de1141df8771985b44925d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.