MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation

Introduction

we present MoMA: an open-vocabulary, training-free personalized image model that boasts flexible zero-shot capabilities. As foundational text-to-image models rapidly evolve, the demand for robust image-to-image translation grows. Addressing this need, MoMA specializes in subject-driven personalized image generation. Utilizing an open-source, Multimodal Large Language Model (MLLM), we train MoMA to serve a dual role as both a feature extractor and a generator. This approach effectively synergizes reference image and text prompt information to produce valuable image features, facilitating an image diffusion model. To better leverage the generated features, we further introduce a novel self-attention shortcut method that efficiently transfers image features to an image diffusion model, improving the resemblance of the target object in generated images. Remarkably, as a tuning-free plug-and-play module, our model requires only a single reference image and outperforms existing methods in generating images with high detail fidelity, enhanced identity-preservation, and prompt faithfulness. We commit to making our work open-source, thereby providing universal access to these advancements.

Release

[2024/04/20] 🔥 We release the model code on GitHub.
[2024/04/22] 🔥 We add HuggingFace repository and release the checkpoints.

Installation

Install LlaVA: Please install from its official repository
Download our MoMA repository

cd ..
git clone https://github.com/bytedance/MoMA.git
cd MoMA
pip install -r requirements.txt

(we also provide a requirements_freeze.txt, generated by pip freeze)

Memory Requirements

We support 8-bit and 4-bit inferences which reduce memory consumptions:

If you have 22 GB or more GPU memory: args.load_8bit, args.load_4bit = False, False
If you have 18 GB or more GPU memory: args.load_8bit, args.load_4bit = True, False
If you have 14 GB or more GPU memory: args.load_8bit, args.load_4bit = False, True

Download Models

You don't have to download any checkpoints, our code will automatically download them from HuggingFace repositories, which includes:

VAE: stabilityai--sd-vae-ft-mse
StableDiffusion: Realistic_Vision_V4.0_noVAE
MoMA: 
    Multi-modal LLM: MoMA_llava_7b (13 GB)
    Attentions and mappings: attn_adapters_projectors.th (151 Mb)

How to Use

Jupyter-notebook

run_MoMA_notebook.ipynb

Python code

run_evaluate_MoMA.py

run:

CUDA_VISIBLE_DEVICES=0 python run_evaluate_MoMA.py

(generated images will be saved in the output folder)

Example Results

New context: New texture:

Hyper parameter:

In "changing context", you can increase the strength to get more accurate details. Mostly,strength=1.0 is the best. It's recommended that strength is no greater than 1.2.
In "changing texture", you can change the strength to balance between detail accuracy and prompt fidelity. To get better prompt fidelity, just decrease strength. Mostly, strength=0.4 is the best. It's recommended that strength is no greater than 0.6.

Citation

If you find our work useful for your research and applications, please consider citing us by:

@article{song2024moma,
  title={MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation},
  author={Song, Kunpeng and Zhu, Yizhe and Liu, Bingchen and Yan, Qing and Elgammal, Ahmed and Yang, Xiao},
  journal={arXiv preprint arXiv:2404.05674},
  year={2024}
}

wyaeld / MoMA