Instructions to use Yanqing2001/output_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Yanqing2001/output_model 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("Yanqing2001/output_model") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 0093ca42caf6f9dfd363702f7984ab995165e1d744b747afcee8be6f5e7eca5b
- Size of remote file:
- 3.29 MB
- SHA256:
- e85f849a19810d03e65728d4b749b8d0e203ef8fba83ebcf8772d1e433103327
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