Instructions to use Muapi/table-humping with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/table-humping 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/table-humping") 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:
- 7764286e9b4b1ad7cfb5b2b499b6606c531ee5d22bb08d573205ebf123876d2a
- Size of remote file:
- 1.57 MB
- SHA256:
- 0b53036259c9ac75a0f5d6b22e6d8804189c2774505a9fd81b0dff296c4c5f44
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