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:
- 9ca08ff760fa04f178807d69625e9ecb7d8334131fd7f8a17e05dae0c370c821
- Size of remote file:
- 6.59 MB
- SHA256:
- 60fa89cafc855611fad9855aaf20f7c5a1890ce41a30b6f2a34ac830a623f7be
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