Instructions to use na1taneja2821/diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use na1taneja2821/diffusers 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("na1taneja2821/diffusers") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
K-Diffusion
k-diffusion is a popular library created by Katherine Crowson. We provide StableDiffusionKDiffusionPipeline and StableDiffusionXLKDiffusionPipeline that allow you to run Stable DIffusion with samplers from k-diffusion.
Note that most the samplers from k-diffusion are implemented in Diffusers and we recommend using existing schedulers. You can find a mapping between k-diffusion samplers and schedulers in Diffusers here
StableDiffusionKDiffusionPipeline
[[autodoc]] StableDiffusionKDiffusionPipeline
StableDiffusionXLKDiffusionPipeline
[[autodoc]] StableDiffusionXLKDiffusionPipeline