Diffusion implementation for fast text-to-image model personalization
Top 85.2% on sourcepulse
This repository provides an implementation of Encoder-based Domain Tuning (E4T) for fast personalization of text-to-image models, specifically targeting the Hugging Face diffusers
library. It enables users to quickly adapt large pre-trained diffusion models to specific domains or styles with minimal training data and steps, benefiting researchers and artists looking to customize image generation.
How It Works
E4T employs a novel approach by pre-training an encoder that learns domain-specific embeddings. This encoder is then integrated into the diffusion model's architecture. During domain tuning, only this encoder and optionally the text encoder are fine-tuned, drastically reducing training time and data requirements compared to methods like Dreamboho. The method leverages Stable unCLIP for data augmentation to enhance results.
Quick Start & Requirements
pip install -r requirements.txt
after cloning the repository.diffusers
, accelerate
, and xformers
for memory-efficient attention.Highlighted Details
fp16
) and memory-efficient attention (xformers
).Maintenance & Community
Licensing & Compatibility
Limitations & Caveats
The project is still under development, with planned features like using face segmentation networks for human face domains and supporting ToMe for more efficient training. The exact licensing for the codebase itself requires clarification.
2 years ago
1 week