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:
- afb42258896993ab2d4cfdbf39ebecfe750dbf4bf9dadfdfc9ade70988aae3f8
- Size of remote file:
- 1.37 MB
- SHA256:
- 1be2df9e3606c10dfc3bfad30cb9f6d2c2a4034f3dd0e9ad6fff765ea5da3c7f
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