Instructions to use AlanB/TransformersStyle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AlanB/TransformersStyle with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("AlanB/TransformersStyle") prompt = "UNICODE\u0000\u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000T\u0000r\u0000a\u0000n\u0000s\u0000f\u0000o\u0000r\u0000m\u0000e\u0000r\u0000s\u0000S\u0000t\u0000y\u0000l\u0000e\u0000:\u00001\u0000.\u00000\u0000>\u0000 \u0000T\u0000r\u0000a\u0000n\u0000s\u0000f\u0000o\u0000r\u0000m\u0000e\u0000r\u0000s\u0000S\u0000t\u0000y\u0000l\u0000e\u0000 \u0000L\u0000i\u0000g\u0000h\u0000t\u0000_\u0000p\u0000i\u0000n\u0000k\u0000,\u0000 \u0000G\u0000r\u0000e\u0000e\u0000n\u0000,\u0000 \u0000b\u0000e\u0000e\u0000t\u0000l\u0000e\u0000 \u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000L\u0000o\u0000w\u0000R\u0000a\u0000:\u00000\u0000.\u00003\u0000>\u0000 \u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000a\u0000d\u0000d\u0000_\u0000d\u0000e\u0000t\u0000a\u0000i\u0000l\u0000:\u00000\u0000.\u00005\u0000>\u0000" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 9f66d108f7e772cdacac5a0f2e2ad9a8cc4a8939d49c4742b819a1886ce15750
- Size of remote file:
- 151 MB
- SHA256:
- d66e7eea3b17f661066a1a4d19a87fea942a000238c3c7e9b5e44cb4c66c549c
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