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:
- 57006769c7563675ceeecddb6a0fa0a73efcfd2f5ac12dc49c5bf31f261069c9
- Size of remote file:
- 3.44 GB
- SHA256:
- bdb94b8570679d2a0a5300fd064df14a9cbaab19908a7548910918b1aff988de
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