Instructions to use SidXXD/34 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SidXXD/34 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SidXXD/34", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a <v1*> person" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 058c187885ca2c761ccb2db013015709688cb43801f39bf68ed7117a128315c9
- Size of remote file:
- 76.7 MB
- SHA256:
- 36813330c2079d3995e99d008c0fbe87d878fb37d60a8ba83a353f92eb8a7c0a
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