Instructions to use ankurcw/TrainedLoRAs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ankurcw/TrainedLoRAs 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/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ankurcw/TrainedLoRAs") prompt = "Kurukshetra" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 9b57faaa1eaed079d60a87b92caf5b4bc17e4d35fcece6e17bf770cd9ad3252b
- Size of remote file:
- 15 MB
- SHA256:
- 8e3dafd0eff472342673a7847eca99f4d71960f5fbfdd15ff4fb9bd074bfd938
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