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:
- 182835bac29e91d245f0c89c825ddf0fc90f87778b62ca41f4d8944c9954d24c
- Size of remote file:
- 2.58 MB
- SHA256:
- 8833b0ef74c64dfc17835931ce30dba630277576cd1580dc28f2dc440599a511
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