Instructions to use Aminrabi/diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Aminrabi/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("Aminrabi/diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Loaders
Adapters (textual inversion, LoRA, hypernetworks) allow you to modify a diffusion model to generate images in a specific style without training or finetuning the entire model. The adapter weights are typically only a tiny fraction of the pretrained model's which making them very portable. 🤗 Diffusers provides an easy-to-use LoaderMixin API to load adapter weights.
🧪 The LoaderMixins are highly experimental and prone to future changes. To use private or gated models, log-in with huggingface-cli login.
UNet2DConditionLoadersMixin
[[autodoc]] loaders.UNet2DConditionLoadersMixin
TextualInversionLoaderMixin
[[autodoc]] loaders.TextualInversionLoaderMixin
LoraLoaderMixin
[[autodoc]] loaders.LoraLoaderMixin
FromSingleFileMixin
[[autodoc]] loaders.FromSingleFileMixin
FromOriginalControlnetMixin
[[autodoc]] loaders.FromOriginalControlnetMixin
FromOriginalVAEMixin
[[autodoc]] loaders.FromOriginalVAEMixin