Structured Diffusion Guidance (ICLR 2023)

We propose a method to fuse language structures into diffusion guidance for compositionality text-to-image generation.

Project Page | arxiv | OpenReview

This is the official codebase for Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis.

Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis
Weixi Feng ¹, Xuehai He ², Tsu-Jui Fu¹, Varun Jampani³, Arjun Akula³, Pradyumna Narayana³, Sugato Basu³, Xin Eric Wang², William Yang Wang ¹
¹UCSB, ²UCSC, ³Google

Update:

Apr. 4th: updated links, uploaded benchmarks and GLIP eval scripts, updated bibtex.

Setup

Clone this repository and then create a conda environment with:

conda env create -f environment.yaml
conda activate structure_diffusion

If you already have a stable diffusion environment, you can run the following commands:

pip install stanza nltk scenegraphparser tqdm matplotlib
pip install -e .

Inference

This repository supports stable diffusion 1.4 for now. Please refer to the official stable-diffusion repository to download the pre-trained model and put it under models/ldm/stable-diffusion-v1/. Our method is training-free and can be applied to the trained stable diffusion checkpoint directly.

To generate an image, run

python scripts/txt2img_demo.py --prompt "A red teddy bear in a christmas hat sitting next to a glass" --plms --parser_type constituency

By default, the guidance scale is set to 7.5 and output image size is 512x512. We only support PLMS sampling and batch size equals to 1 for now. Apart from the default arguments from Stable Diffusion, we add --parser_type and --conjunction.

usage: txt2img_demo.py [-h] [--prompt [PROMPT]] ...
                       [--parser_type {constituency,scene_graph}] [--conjunction] [--save_attn_maps]

optional arguments:
    ...
  --parser_type {constituency,scene_graph}
  --conjunction         If True, the input prompt is a conjunction of two concepts like "A and B"
  --save_attn_maps      If True, the attention maps will be saved as a .pth file with the name same as the image

Without specifying the conjunction argument, the model applies one key and multiple values for each cross-attention layer. For concept conjunction prompts, you can run:

python scripts/txt2img_demo.py --prompt "A red car and a white sheep" --plms --parser_type constituency --conjunction

Overall, compositional prompts remains a challenge for Stable Diffusion v1.4. It may still take several attempts to get a correct image with our method. The improvement is system-level instead of sample-level, and we are still looking for good evaluation metrics for compositional T2I synthesis. We observe less missing objects in Stable Diffusion v2, and we are implementing our method on top of it as well. Please feel free to reach out for a discussion.

Benchmarks

CC-500.txt: Concept Conjunction of two objects with different colors (line1-446). ABC-6K.txt: ~6K attribute binding prompts collected and created from COCO captions.

GLIP Eval

For our GLIP eval, please first clone and setup your environment according to the official GLIP repo and download the model checkpoint(s). Then refer to our GLIP_eval/eval.py and you may need to modify line 59&82. We assumed that each image file name contains the text prompt.

Comments

Our codebase builds heavily on Stable Diffusion. Thanks for open-sourcing!

Citing our Paper

If you find our code or paper useful for your research, please consider citing

@inproceedings{feng2023trainingfree,
title={Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis},
author={Weixi Feng and Xuehai He and Tsu-Jui Fu and Varun Jampani and Arjun Reddy Akula and Pradyumna Narayana and Sugato Basu and Xin Eric Wang and William Yang Wang},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=PUIqjT4rzq7}
}

weixi-feng / Structured-Diffusion-Guidance