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
- Draw Things
- DiffusionBee
- Xet hash:
- 001873d3da3b1232306dde3641453d38bdd8ddd9adc664b6fd87472dd4a75c08
- Size of remote file:
- 1.36 GB
- SHA256:
- 7b57eef2276cb32ae2b95ad9bfded12e2115505d4792a0731da5feb181437d68
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