Instructions to use willhx/train_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use willhx/train_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("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("willhx/train_lora") prompt = "a photo of sofa" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 32cf718982ca2e4386d924276a6afc4a5311b7b5a2a6cda13da1b6c8608d82b9
- Size of remote file:
- 6.59 MB
- SHA256:
- b6e7af47c4e293ceefdd33e5b7241f8055ca2ec051ae5e94dc5c8cec214eec41
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