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", 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:
- bd1955ab48aa208b4988d8a544c3e16e492e991084a2d70156548aaf1451010a
- Size of remote file:
- 6.59 MB
- SHA256:
- d740e69e537f616a0a6a7be2e929117315d88b7f186a209322d5658922cec525
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