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", 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
Download sks.bin from SidXXD/Clean-m_213-2: direct link, hf CLI and curl.
- Browser
- Download file 152 MB
-
https://huggingface.co/SidXXD/Clean-m_213-2/resolve/main/sks.bin
- Command line
-
hf download hf://SidXXD/Clean-m_213-2/sks.bin
-
curl -L -o sks.bin https://huggingface.co/SidXXD/Clean-m_213-2/resolve/main/sks.bin
152 MB
- Xet hash:
- 3924a39ab74dd7dc519c79c4f9e47b0936987b1707f049c3c138cb12b1308129
- Size of remote file:
- 152 MB
- SHA256:
- 607e9be9476b5b72303579295383fe695edb005a58144ebd074886743d170d8c
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