Instructions to use Anytram/out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Anytram/out 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("Anytram/out") prompt = "a photo of me" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 1fcb2825aae6408020ec1afe4fa51aaded3151b1905b068be34d0d507050e6fe
- Size of remote file:
- 6.59 MB
- SHA256:
- 5cf8debd3dc4113f04155735c92d10724435120fe23d279677a072a6decdf9d2
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