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:
- d528a7a1f30152d2c21f8ff432d599f7c21045a5ebb4db405e0296e081569d34
- Size of remote file:
- 2.13 GB
- SHA256:
- 920d805fbed91daba7d5c703b39c3e5398cd2ede50e7475d9ad1c7f266d72445
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