Instructions to use iskandre/output1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use iskandre/output1 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("iskandre/output1") prompt = "a photo of Haro" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 68ce829cd601ace304daef545c1631cbb85f9136f862c889d55f2574144eebf4
- Size of remote file:
- 6.59 MB
- SHA256:
- f52773cb7d393cc624c112edd5c411d638c112eabff262951130a6095ff6d237
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.