Instructions to use abdd68/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use abdd68/output 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("abdd68/output") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 508e9bc321fb5dcf099b01be531978eb7386fd1b51f7efa8cb61af14ee10b69b
- Size of remote file:
- 563 Bytes
- SHA256:
- 906bdd391189fd16d3160fd1e198eb53687dae580f930cf3f64365609fd5f606
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