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:
- 3d6f35d17c887a0c71cccc0bb62e341151bb200d8e3492878fff35dda07363f3
- Size of remote file:
- 1.76 MB
- SHA256:
- 4265ff98053a73c9c865cbe445fd07aea24dcf163adc121cf4825c053d6b7e90
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