Instructions to use iskandre/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use iskandre/output 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/output") prompt = "a photo of cat" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 87553ad8431501a10447e4da3ee07ed91e416d454d297df0cbf885b117114531
- Size of remote file:
- 6.59 MB
- SHA256:
- 264d1675571abb2af4a78b858e44fb9899f51b3186912a8d13f31ad288a156b6
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