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:
- 69cf0dcf13c038143d84cdd876678066d2dde4aa10e72f9c00236eb01729b9f3
- Size of remote file:
- 6.59 MB
- SHA256:
- 07c9d9c8172a0b0bed980bea9ed4a76877a5a671679f88fad4f91c59a937a565
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