Instructions to use udg/popast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use udg/popast with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("udg/popast", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 36f593a0994b2cdef01f6e4b0da8df2e9658024c26e9d6c270a334d298d7eda1
- Size of remote file:
- 492 MB
- SHA256:
- 7e7e5166578422524b31b90e21f46f65683da1476e0b2349287469c0a8c1a90b
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