Instructions to use Muapi/pole-dancing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/pole-dancing with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cocktailpeanut/pony-diffusion-v6-xl", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/pole-dancing") 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:
- c9c617e1775583596a16a535be9d098a48f548638bb436a0334a22178e9ea39c
- Size of remote file:
- 57.4 MB
- SHA256:
- 18b2730afc90fdfc05fb3b98e6e9080b0058a74e111bca4eaac5aeeeb2d9398a
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