Instructions to use SteveWCG/trained-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SteveWCG/trained-5 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-5") prompt = "A photo of a separated cycling path runs parallel to the roadway, protected by bollards on the left and bordered by a hedge on the right, within the sunurban area. " image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-500/optimizer.bin from SteveWCG/trained-5: direct link, hf CLI and curl.
- Browser
- Download file 127 MB
-
https://huggingface.co/SteveWCG/trained-5/resolve/main/checkpoint-500/optimizer.bin
- Command line
-
hf download hf://SteveWCG/trained-5/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/SteveWCG/trained-5/resolve/main/checkpoint-500/optimizer.bin
127 MB
- Xet hash:
- e7bc3b677fc62d8ce469c3c9334b1b97c13b7ab2957e30083361129c0a0f27ad
- Size of remote file:
- 127 MB
- SHA256:
- 1fad1329454557202e83a0a67351fdc7eb93d05e6cf7b0fae3f516ce5eb35b19
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