Instructions to use tzvc/benjamincode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tzvc/benjamincode with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tzvc/benjamincode", dtype=torch.bfloat16, device_map="cuda") prompt = "sdcid" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 1c67cfe6844a0270965665df4407b5e3fd3874ead9639eadffdbbf4df82925f8
- Size of remote file:
- 492 MB
- SHA256:
- 7470f53fe9011fff439e37918e68a1698cdf99f799d8d131df039c2dc0b6df73
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