Instructions to use abdd68/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use abdd68/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("abdd68/output") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 7e0749a745cf75746436b6f1779756a6c5e63ba3d9a49e4a29f09df9af1fb08b
- Size of remote file:
- 6.59 MB
- SHA256:
- 11977077075aaddacb3768c9b39482d366cec0fe7cefe0821ef0e4717f9c6c16
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