Instructions to use SteveWCG/trained-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SteveWCG/trained-6 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-6") prompt = "A photo of a narrow pathway accommodates a bike lane, fully surrounded by greenery on both sides, within the suburban area. " image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download image_3.png from SteveWCG/trained-6: direct link, hf CLI and curl.
- Browser
- Download file 1.89 MB
-
https://huggingface.co/SteveWCG/trained-6/resolve/main/image_3.png
- Command line
-
hf download hf://SteveWCG/trained-6/image_3.png
-
curl -L -o image_3.png https://huggingface.co/SteveWCG/trained-6/resolve/main/image_3.png
1.89 MB

- Xet hash:
- a5a4a7ceea7ba2f00bbb9f4b661df327f0c26538e708f405b7b7be046b1d7799
- Size of remote file:
- 1.89 MB
- SHA256:
- 6c5bb5a2e525727799b8345216af9c7b8b3bdb82417935eb14f1cd0a12ff4190
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.