Instructions to use Shakker-Labs/AWPortraitCN2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Shakker-Labs/AWPortraitCN2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Shakker-Labs/AWPortraitCN2", 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:
- 4dd7f3d73c5079adbeff7b2da1b838112648651dc1e2a4f46cf4695ca873eab0
- Size of remote file:
- 1.85 MB
- SHA256:
- 4569ff6307471eb4629bbd10d51d2656a7832068bbd66febcb47e7c259321b4b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.