Instructions to use memj/trained-sd3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use memj/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("memj/trained-sd3-lora", dtype=torch.bfloat16, device_map="cuda") prompt = "A photo of sks dog in a bucket" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- bd8b32868208e37192c9b71be3f5f5e3c37b7e250e8fdceb1f31e7f238f705d0
- Size of remote file:
- 1.27 MB
- SHA256:
- 66a6a71b81bfed86184d3afffc0d8084e7e7e0345579bcca7af824921fa8aa6f
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