Instructions to use lstep1/reglisse-sheltie-sheepdog with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lstep1/reglisse-sheltie-sheepdog with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("lstep1/reglisse-sheltie-sheepdog") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 2a7d2b13295d4fe106264bc18b1c11203b13dd34fc2c4715f4c8ab11b0d6d735
- Size of remote file:
- 18.2 MB
- SHA256:
- 11d3eeb11800d7e9513692bb818d340268305918a1b8cbc797e4907f2cfc9db4
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