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:
- 510e8a985940fa8ba53f4fc53c111cd01af9e88db6f87dc63133dd113e1d6c77
- Size of remote file:
- 1.19 MB
- SHA256:
- cd18d9a7afcb25347e49b685dedd75bb7d92666afe1753ec133bc221800bee4f
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