Instructions to use SteveWCG/trained_green with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SteveWCG/trained_green 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_green") prompt = "A photo of a green-painted bike lane." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download image_3.png from SteveWCG/trained_green: direct link, hf CLI and curl.
- Browser
- Download file 1.7 MB
-
https://huggingface.co/SteveWCG/trained_green/resolve/main/image_3.png
- Command line
-
hf download hf://SteveWCG/trained_green/image_3.png
-
curl -L -o image_3.png https://huggingface.co/SteveWCG/trained_green/resolve/main/image_3.png
1.7 MB

- Xet hash:
- 29082a6be8f2dcb1d52fc072c5680cb2000145682b4a44e4ab0da9da890a9c3e
- Size of remote file:
- 1.7 MB
- SHA256:
- 83b050151c77dbfa813d2c9896553ba87c4adbf585f13eb5fff859061f8a1f89
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.