Instructions to use kliyer/LoRAdapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kliyer/LoRAdapter with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("kliyer/LoRAdapter") 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
| library_name: diffusers | |
| tags: | |
| - stable diffusion | |
| - lora | |
| - loradapter | |
| - adapter | |
| - conditioning | |
| # Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models | |
| [](https://compvis.github.io/LoRAdapter/)[](https://arxiv.org/abs/2405.07913) | |
| This repository contains the weights for the paper "CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models". | |
| [Nick Stracke](https://twitter.com/nickstracke_), [Stefan Andreas Baumann](https://stefan-baumann.eu/), [Joshua Susskind](https://twitter.com/jsusskin), [Miguel Angel Bautista](https://twitter.com/itsbautistam), [Björn Ommer](https://ommer-lab.com/people/ommer/) | |
| We present LoRAdapter, an approach that unifies both style and structure conditioning under the same formulation using a novel conditional LoRA block that enables zero-shot control. | |
| LoRAdapter is an efficient, powerful, and architecture-agnostic approach to condition text-to-image diffusion models, which enables fine-grained control conditioning during generation and outperforms recent state-of-the-art approaches. | |
| ## 🎓 Citation | |
| If you use this codebase or otherwise found our work valuable, please cite our paper: | |
| ```bibtex | |
| @misc{stracke2024loradapter, | |
| title={CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models}, | |
| author={Nick Stracke and Stefan Andreas Baumann and Joshua Susskind and Miguel Angel Bautista and Björn Ommer}, | |
| year={2024}, | |
| eprint={2405.07913}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV} | |
| } | |
| ``` |