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:
- ae10e7a83886938013fead557331903cb6c4d75212310cea1012b7c40a31cf7c
- Size of remote file:
- 57.4 MB
- SHA256:
- 27a35b01ba95a63cc2bcd52c6d4751ca2c838a6bae37bdf0dc61861d4e5511ad
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