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:
- 3053312b83c0ea720d328bb5778bd6df37daacf4a330b640bbba1ace6d93d1fe
- Size of remote file:
- 8.16 MB
- SHA256:
- c7331efdd9e435d4b98bd625cfb0f973b40fce0e2dde3d4e96f6f2dbef8b63a0
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