Instructions to use p1atdev/plat-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use p1atdev/plat-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("p1atdev/plat-diffusion", 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:
- adafa850296e3bd2d5cf0b5fe6823345997f52e1123e871dedbc1840f78d95f0
- Size of remote file:
- 1.36 GB
- SHA256:
- e150a734015803fc60b495d0657b89c17e1d9fb193236d2db476556097b89139
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