Instructions to use jatmak/stein754 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jatmak/stein754 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("krea/Krea-2-Raw", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("jatmak/stein754") prompt = "A cinematic wide shot of a vzx woman wearing iridescent futuristic armor, standing amidst the neon-lit rain of a cyberpunk Tokyo street." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 2164b0f42048b0c9bf0619dcafc878d2ba7495bc94653b44db464fd7e7dbe965
- Size of remote file:
- 195 MB
- SHA256:
- 19ecd8f8b3abed3d55c3cf00d668cf96f41cc3cb2e960dadea2f96c364ea1d1a
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