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:
- ad9aa024c28f38431c4aced9162dda0250e875e4ee4f881008d50f87099de37e
- Size of remote file:
- 6.59 MB
- SHA256:
- 2292d03d8714b40374e69f9f3d28637ba727c409e8160faaeba219077f0bf172
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