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:
- 9afa83a742776fa17752a1bd873a261a49050aa17a74a0ea95c5324690446fde
- Size of remote file:
- 1.97 MB
- SHA256:
- 073e0b681e4618708712b46761e18b93448eb3045deb2305538c6ec411edbbe4
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