Instructions to use rafaared/trained-sd3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rafaared/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("rafaared/trained-sd3-lora", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of a person laying in the grass, lgrass" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 3620a4b212cdffa9018e5b6094308725057d621c0c0da41cc1dbe528c9f2afa5
- Size of remote file:
- 18.9 MB
- SHA256:
- 26df9be0b181721bdf456fdccdd15fab5e257159900135ead4b48485c17339ea
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