Instructions to use SteveWCG/trained_protect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SteveWCG/trained_protect with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("SteveWCG/trained_protect") prompt = "A photo of a bike lane with a buffer zone of protective bollards separating cyclists from moving car traffic." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 783c0c5dd39192da6ed0308edc671b0dbe0e784a415fc6e70421229c9d916832
- Size of remote file:
- 1.06 kB
- SHA256:
- 346da97ff96dad7500d32aad5cd286bb3bfaa030458ee7ff5ba0e84f00267e9b
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