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:
- 362f2e4274dc0405754c31ff7efb70f43354e9e099a45a5cb66e2188a0808096
- Size of remote file:
- 8.16 MB
- SHA256:
- 5b39f95e7441ce8fbc3265bfab5625b75e8d1c928c30d9b53f13a6b3a0ba1422
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