Instructions to use HuggingJaeuk/trained-sd3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use HuggingJaeuk/trained-sd3-lora 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("HuggingJaeuk/trained-sd3-lora") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 505a577df3405d476e75343841c13d6d2f5126f42900cd19e5ea7c34d0dd54e2
- Size of remote file:
- 2.61 MB
- SHA256:
- 86edfca394772d8c4e7da4c5748e1fd6fc87b77f05dc86158e32bf2a79de6b98
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.