Instructions to use SidXXD/Clean-m_213-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SidXXD/Clean-m_213-2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SidXXD/Clean-m_213-2", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a sks person" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 1cece0cfde8d03b88c6994a982215bd9328734f9274879db728b39ee58b158bb
- Size of remote file:
- 76.7 MB
- SHA256:
- 177b498420fcf9e07c1ca84604198cdf6d2112d56e93f91345b610f133005699
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