Instructions to use cornpip/result_model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cornpip/result_model2 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("cornpip/result_model2") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- ffcd91d4b3c66871c93c97d465e10081f82006d8bcb91348ea6458c236bf1d0d
- Size of remote file:
- 6.59 MB
- SHA256:
- dfcbd5e2dc8501cad7cde5d80f44555c5e8cd7cc477b4ae27a56e08dfb435795
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