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:
- b811a476a7324a98ce83a74c7f5527693738096f9374fc40fc48761a0b0306dd
- Size of remote file:
- 6.59 MB
- SHA256:
- aa935a97aec0a5a16d003d55f0ab8e2cf6236fa4632ea684375d651e65e7f2ea
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