Instructions to use iskandre/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use iskandre/output 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/output") prompt = "a photo of cat" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 3f09d8813c3526780ab228079d71fa234ff67c32a2fad23f540939c434dc30b7
- Size of remote file:
- 3.29 MB
- SHA256:
- 468adb61eab355a4e16760934bf54abebef285dcdec5615a1900e1599338a492
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