Instructions to use gdvstd/trained-sd3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use gdvstd/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("gdvstd/trained-sd3-lora") prompt = "a storyboard image in sks style" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 9957de46cbd8f416f1d608b8db10d3378eee3b05d3634554dd16ba571255f244
- Size of remote file:
- 1 kB
- SHA256:
- a557586bcb3fb0ae17fbf2365015705b4ec0b753f7e5355496ee9f773dbbbb4f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.