Instructions to use Muapi/sweat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/sweat 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/sweat") 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:
- 14d3e2ce725c2c5d99c0ffd61127393fa6a00badcd8c26aa9d4e697d0e4ab6a1
- Size of remote file:
- 1.19 MB
- SHA256:
- 9096a82b5f0e1e0c2ee8103f4601ecccfcdb03a0cd0e0efcd833453ec57080c5
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