Instructions to use PSJJ/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use PSJJ/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("PSJJ/output") prompt = "sonny" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 0f1e0f9f9ef59b1a722763251b49ad6422ac6ec754f50a40b9123804a4317c04
- Size of remote file:
- 6.64 MB
- SHA256:
- bfde5198eb626a96732f76c8e58bc588773080cce588dd5b959485b4da2f3e29
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