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:
- 43169000022598a5c5cc3e6eb704592b51793c01e8f9759a5c20f3a43955b0f0
- Size of remote file:
- 563 Bytes
- SHA256:
- 73c9f2869c07c90d17ceeb5544f136285ce5860b640280590735df64042661f5
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