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:
- 7e6b1167e8111e76bb2f4d292e182a4150922a1d69feadf42440fff582c86d93
- Size of remote file:
- 1.26 MB
- SHA256:
- 8c3d3adba9521331c1d22c71e38b6844957d6e65c3b35e8d9f04fefab3212cf3
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