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
- Draw Things
- DiffusionBee
- Xet hash:
- 09283961e2797f0119736bf5859759061455a61400f09484727d17294fa7132d
- Size of remote file:
- 563 Bytes
- SHA256:
- d6ab1339da08e3fa015ca19e5a001977bd59cee7c7573120ae85950b1c7964a0
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