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", torch_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
Models
🤗 Diffusers provides pretrained models for popular algorithms and modules to create custom diffusion systems. The primary function of models is to denoise an input sample as modeled by the distribution .
All models are built from the base [ModelMixin] class which is a torch.nn.Module providing basic functionality for saving and loading models, locally and from the Hugging Face Hub.
ModelMixin
[[autodoc]] ModelMixin
FlaxModelMixin
[[autodoc]] FlaxModelMixin
PushToHubMixin
[[autodoc]] utils.PushToHubMixin