Instructions to use QingyuShi/Muddit_dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use QingyuShi/Muddit_dev with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("QingyuShi/Muddit_dev", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download checkpoint-00.zip from QingyuShi/Muddit_dev: direct link, hf CLI and curl.
- Browser
- Download file 7.84 GB
-
https://huggingface.co/QingyuShi/Muddit_dev/resolve/main/checkpoint-00.zip
- Command line
-
hf download hf://QingyuShi/Muddit_dev/checkpoint-00.zip
-
curl -L -o checkpoint-00.zip https://huggingface.co/QingyuShi/Muddit_dev/resolve/main/checkpoint-00.zip
7.84 GB
- Xet hash:
- 1f988fac8fd6139b299556c734691840c55e3959b746d49f837404fabcff484f
- Size of remote file:
- 7.84 GB
- SHA256:
- 288ed0f518a2b294b75a0fbf6ea90b73aa8d501a4452cb8be4400030f23a2ec5
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