TIGER: A Unified Generative Model Framework for Multimodal Dialogue Response Generation

📢 Latest Updates

• 2024 May-18 : Our paper is available at here.
• 2024 Feb-20 : This work has been accepted by COLING 2024.
• 2023 Oct-13 : Updated the demo interface.
• 2023 Aug-02 : Released the demo video. [YouTube]

TIGER Framework 💡

Figure 1: The overview of TIGER. Given the dialogue context, response modal predictor $\mathcal{M}$ determines the timing to respond with images. If the predicted response modal is text, textual dialogue response generator $\mathcal{G}$ generates the text response. Conversely, $\mathcal{G}$ produces an image description, and Text-to-Image translator $\mathcal{F}$ leverages this description to generate an image as the visual response.

Contributions 🏆

• We propose mulTImodal GEnerator for dialogue Response (TIGER), a unified generative model framework designed for multimodal dialogue response generation. Notably, this framework is capable of handling conversations involving any combination of modalities.
• We implement a system for multimodal dialogue response generation, incorporating both text and images, based on TIGER.
• Extensive experiments show that TIGER achieves new state-of-the-art results on both automatic and human evaluations, which validate the effectiveness of our system in providing a superior multimodal conversational experience.

Demo 🌐

☝ We implemented a multimodal dialogue system based on TIGER, as depicted in figure above.

Our system offers various modifiable components:

• For the textual dialogue response generator, users can can choose decoding strategies and adjust related parameters.
• For the Text-to-Image translator, users can freely modify prompt templates and negative prompt to suit different requirements. Default prompt templates and negative prompts are provided, enhancing the realism of generated images.

❗Note: It's worth mentioning that our research focuses on open-domain multimodal dialogue response generation. However, the system may not possess perfect instruction-following capabilities. Users can treat it as a companion or listener, but using it as a QA system or AI painting generator is not recommended.

Examples 🪧

Supplementary Instructions 🔍

Restricted by the limited number of pages, we only give a clear and easy-to-understand introduction of our method in the paper. More implementation details, experimental results and discussions can be found in supplement.

Getting Start ⏳

Hardware ⚙️

⭐ A GPU with 24GB memory (18GB at runtime) is enough for the demo.

Installation 🔧

1. Prepare the code and the environment

cd TIGER/
conda env create -f environment.yml
conda activate tiger

2. Prepare the model weights

✨ Please download our model weights from here (Google Drive). For Text-to-Image Translator's weights, we have already uploaded it to Hugging Face, so you don't need to download it locally now. More details can be sourced from friedrichor/stable-diffusion-2-1-realistic.

The final weights would be in a single folder in a structure similar to the following:

TIGER
├── demo
│   └── ...
├── model_weights
│   ├── tiger_response_modal_predictor.pth
│   ├── tiger_textual_dialogue_response_generator.pth
│   └── tiger_text2image_translator
│       ├── feature_extractor
│       │   └── preprocessor_config.json
│       ├── scheduler
│       │   └── scheduler_config.json
│       ├── text_encoder
│       │   ├── config.json
│       │   └── pytorch_model.bin
│       ├── tokenizer
│       ├── merges.txt
│       ├── special_tokens_map.json
│       ├── tokenizer_config.json
│       │   └── vocab.json
│       ├── unet
│       │   ├── config.json
│       │   └── diffusion_pytorch_model.bin
│       ├── vae
│       │   ├── config.json
│       │   └── diffusion_pytorch_model.bin
│       └── model_index.json
├── tiger
│   └── ...
├── utils
│   └── ...
├── demo.py
...

Launching Demo Locally 💻

python demo.py --config demo/demo_config.yaml

Citation

If you find our work useful in your research, please consider citing us:

@inproceedings{kong-etal-2024-tiger-unified,
    title = "{TIGER}: A Unified Generative Model Framework for Multimodal Dialogue Response Generation",
    author = "Kong, Fanheng  and
      Wang, Peidong  and
      Feng, Shi  and
      Wang, Daling  and
      Zhang, Yifei",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italy",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1403",
    pages = "16135--16141",
}