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:
- 6ddc8dbfd75a488a96381202432558b7e3c77f3bce1517286d27edd40da7426b
- Size of remote file:
- 2.58 MB
- SHA256:
- 8a8e3998943821aaeb44ceb8c646e175ef46f8e7a4f802fdc14eb9af49cc2425
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.