Unite and Conquer (CVPR 2023)

This repository contains the implementation of the paper:

Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models
Nithin Gopalakrishnan Nair, Chaminda Bandara, Vishal M Patel

IEEE/CVF International Conference on Computer Vision (CVPR), 2023

From VIU Lab, Johns Hopkins University

[Paper] | [Project Page] | [Video]

Keywords: Multimodal Generation, Semantic Face Generation, Multimodal Face generation, Text to image generation, Diffusion based Face Generation, Text to Image Generation, Text to Face Generation

Applications

We propose Unite and Conquer, where users can use multiple modalities for face and generic image generation. (a) Face Generation. Given multi-modal controls, We can generate high-quality images consistent with the input conditions. (b) Text and Class guided Genearation. Users can input generic scenes and Imagenet classes to generate images with unlikely scenarios.

Contributions:

• We propose a diffusion-based solution for image generation under the presence of multimodal priors.
• We tackle the problem of need for paired data for multimodal synthesis by deriving upon the flexible property of diffusion models.
• Unlike existing methods, our method is easily scalable and can be incorporated with off-the-shelf models to add additional constraints

Environment setup

conda env create -f environment.yml

Pretrained models:

Please download the pretrained models using

python utils/download_models.py

Training On custom datasets

Data Preparation- Training

You can train on any custom datasets by arranging the data in the following format. Please note that you can choose to give either one or more modalities

    ├── data 
    |   ├── images
    |   └── hair_masks 
    |   └── face_masks

The training command for including semantic masks as different modalities along with text is

export PYTHONPATH=$PYTHONPATH:$(pwd)
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --large_size 256  --small_size 256 --learn_sigma True --noise_schedule linear --num_channels 192 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
CUDA_VISIBLE_DEVICES="0" NCCL_P2P_DISABLE=1  torchrun --nproc_per_node=1 --master_port=4326 scripts/train.py $MODEL_FLAGS

Testing On custom datasets

Data Preparation- Testing

You can test on any custom datasets by arranging the data in the following format. Please note that you can choose to give either one or more modalities

    ├── data 
    |   ├── face_map 
    |   └── hair_map  
    |   └── sketches
    |   └── text.txt

text.txt should be of the format

image_name : Text caption

An example would be:

0.jpg : This person wears eyeglasses

Testing code

Test on custom data using:

python test_multimodal_face.py --data_path /path/to/data --face_map --hair_map --text

Please set the flags you need for the generation.

Instructions for Interactive Demo

Demo

python gradio_set.py

Once you perform the above steps, the models will automatically get downloaded to your Directory. One that is finished, the models will be automatically downloaded you will get a local demo link which can be used to tey the generation models on your own. More details about internal components of the code will be uploaded shortyl

Citation

5. If you use our work, please use the following citation

@inproceedings{nair2023unite,
  title={Unite and Conquer: Plug \& Play Multi-Modal Synthesis Using Diffusion Models},
  author={Nair, Nithin Gopalakrishnan and Bandara, Wele Gedara Chaminda and Patel, Vishal M},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6070--6079},
  year={2023}
}

This code is reliant on:

https://github.com/openai/guided-diffusion
https://github.com/openai/glide-text2im
https://github.com/FacePerceiver/facer