Instructions to use xiangjx/MuPaD-512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xiangjx/MuPaD-512 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("xiangjx/MuPaD-512", 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:
- a44895e97a408941d004166c7ec5997c7907ca53d1b1cb2879f8b908c51eb610
- Size of remote file:
- 2.36 MB
- SHA256:
- 6c96f1636c373ab0e81270b17aa25b162f43c5243ef7b2007ade8c1acc66bb34
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