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:
- 948d4f2b3f999c71198fb25f19a85153e040a34f03a7f3a06e6815344ab1c877
- Size of remote file:
- 1.3 MB
- SHA256:
- b29c1ff28bff04f3fce4c53a23a5abb0b58b5cfc562cd3cb86d83d1667e245f5
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