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:
- 956e5922f677ca6bd1a92071825f2586ff03bb2b762a0b537b49fbae6e5c8f36
- Size of remote file:
- 6.59 MB
- SHA256:
- b46540cec3424af59a98e867bef23c1cfc7f30d384f3d6c7752a602dce6c980e
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