Instructions to use mkshing/lora-sdxl-dog with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mkshing/lora-sdxl-dog with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("mkshing/lora-sdxl-dog") prompt = "a sbu dog in a bucket" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 0f6dca65208f5da38baba04e5585566b0855fff2614030d66d79e9d78c90b531
- Size of remote file:
- 744 MB
- SHA256:
- f4461eac72daaf318ace83b66e5677383a432babc80d9b7f41ee2206b71f4250
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