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:
- 494bde8f26c250e0678d0269d03d59dca1d2abec1ffd7938effd16e8868e3e0e
- Size of remote file:
- 1.54 MB
- SHA256:
- bdf02bee32f0cad5bf22bf26caf5480b868cf4a7241b937cfec82f19b4a1128e
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