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:
- dbfb53d26771f6a6e0e71effdf765e7e8d97ec2b345caf9399cad52b82ace3a5
- Size of remote file:
- 3.46 GB
- SHA256:
- 02e89c8acb9e742bb3b7f514d2ca3850f1d6d38b4d0d7ea2eb0bc7de564974c2
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