Instructions to use Muapi/sweet-overload with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/sweet-overload with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/sweet-overload") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download preview.jpg from Muapi/sweet-overload: direct link, hf CLI and curl.
- Browser
- Download file 1.76 MB
-
https://huggingface.co/Muapi/sweet-overload/resolve/main/preview.jpg
- Command line
-
hf download hf://Muapi/sweet-overload/preview.jpg
-
curl -L -o preview.jpg https://huggingface.co/Muapi/sweet-overload/resolve/main/preview.jpg
1.76 MB

- Xet hash:
- 9ec870cbc7547cba3274fc02863a95af6797e6144baa67d21af5f07df8b76f0d
- Size of remote file:
- 1.76 MB
- SHA256:
- e9dcc5e87eb4f6ce89398f7af491bc56864316fa8b9fb86dae4e689cb413a1a4
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