Instructions to use Muapi/testicle-sucking-concept with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/testicle-sucking-concept 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", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/testicle-sucking-concept") 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:
- 42d3bd44f79c4c6f1bd97bab3f21d7ddb19b56b996d07889b1260302108a9b00
- Size of remote file:
- 1.03 MB
- SHA256:
- c2c84b31eda70118555a6f2da2b4fdcef100fab2864d0934d47eaba12e8d9ac6
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