Instructions to use Acopa/sdxl_turbo_lora_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Acopa/sdxl_turbo_lora_test 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/sdxl-turbo", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Acopa/sdxl_turbo_lora_test") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- ec3750579aff2c4c5ebfe6f8331b8ad5491cde4c25c280c16b5ca1301be96f51
- Size of remote file:
- 23.4 MB
- SHA256:
- 13ba04928f46a859dd81d4f396d641aff24c051683572681c5e69342c6989c8b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.