This repository contains the PyTorch inference code for the paper "Enhancing Semantic Fidelity in Text-to-Image Synthesis: Attention Regulation in Diffusion Models". Paper link: arXiv
Clone this repository
git clone https://github.com/YaNgZhAnG-V5/attention_regulation.git
cd attention_regulation
Install the required dependencies with the supported versions
pip install -r requirements.txt
We provide a script (txt2img.py) for inference. You can use it to generate images from text using our Attention Regulation approach.
Example usage:
python txt2img.py --prompt "A painting of a bag and a apple" --target "bag apple"
The full list of options is as follows:
usage: txt2img.py [-h] --prompt PROMPT [--target TARGET] [--workdir WORKDIR] [--cuda-id CUDA_ID] [-n N] [-s STEPS] [--guidance-scale GUIDANCE_SCALE] [--seed SEED] [--edit-steps EDIT_STEPS] [--layers [LAYERS ...]]
[--pipeline-id PIPELINE_ID] [--scheduler SCHEDULER]
options:
-h, --help show this help message and exit
--prompt PROMPT Prompt
--target TARGET Target phrase for editing, separated by space
--workdir WORKDIR Working directory
--cuda-id CUDA_ID CUDA device id
-n N Number of images to generate per prompt
-s STEPS, --steps STEPS
Number of inference steps
--guidance-scale GUIDANCE_SCALE
Guidance scale
--seed SEED Random seed
--edit-steps EDIT_STEPS
Number of edit steps
--layers [LAYERS ...]
Layers to edit. Select from: ['down_blocks.0','down_blocks.1','down_blocks.2', 'mid_block', 'up_blocks.1', 'up_blocks.2', 'up_blocks.3']
--pipeline-id PIPELINE_ID
Pipeline ID from Diffusers. We support SD 1.4 SD 1.5 SD 2 and SD 2.5
--scheduler SCHEDULER
Scheduler to use
If you find our work useful for your work, please consider citing our paper:
@misc{zhang2024enhancing,
title={Enhancing Semantic Fidelity in Text-to-Image Synthesis: Attention Regulation in Diffusion Models},
author={Yang Zhang and Teoh Tze Tzun and Lim Wei Hern and Tiviatis Sim and Kenji Kawaguchi},
year={2024},
eprint={2403.06381},
archivePrefix={arXiv},
primaryClass={cs.CV}
}