Instructions to use iskandre/output1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use iskandre/output1 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", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("iskandre/output1") prompt = "a photo of Haro" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 0be5a28673f4e19bb3fa7c25d0c05ac02d26021872496c3023e5614eff6daaab
- Size of remote file:
- 3.29 MB
- SHA256:
- acbc0ddac68ded1cd56aba9e444710b8acf50fad8cc51243a65c6e8900afaada
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