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", torch_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
- Xet hash:
- 1f988fac8fd6139b299556c734691840c55e3959b746d49f837404fabcff484f
- Size of remote file:
- 7.84 GB
- SHA256:
- 288ed0f518a2b294b75a0fbf6ea90b73aa8d501a4452cb8be4400030f23a2ec5
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