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:
- 9c330e94b6aebb093e6a1a6e6f193d8cbb59d3eb88d63a832c1f803e39f961b1
- Size of remote file:
- 3.29 MB
- SHA256:
- 90e1ee9e65d736139a12c13a6085c62351c92997a21c327b43c88908d9dc98cd
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