# MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation

arXiv: https://arxiv.org/abs/2404.05674 · HTML: https://mlsanity.com/paper/2404.05674/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 30 of its 38 references that are in the map.
- First posted on arXiv 8 Apr 2024.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)
- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 30)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers](https://mlsanity.com/paper/2211.01324/) (2022)
- [CogVLM: Visual Expert for Pretrained Language Models](https://mlsanity.com/paper/2311.03079/) (2023)
- [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://mlsanity.com/paper/2305.14720/) (2023)
- [InstantID: Zero-shot Identity-Preserving Generation in Seconds](https://mlsanity.com/paper/2401.07519/) (2024)
- [ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation](https://mlsanity.com/paper/2302.13848/) (2023)
- [AnyDoor: Zero-shot Object-level Image Customization](https://mlsanity.com/paper/2307.09481/) (2023)
- [InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning](https://mlsanity.com/paper/2304.03411/) (2023)
- [SVDiff: Compact Parameter Space for Diffusion Fine-Tuning](https://mlsanity.com/paper/2303.11305/) (2023)
- [Break-A-Scene: Extracting Multiple Concepts from a Single Image](https://mlsanity.com/paper/2305.16311/) (2023)
- [Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2304.02642/) (2023)
- [Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion](https://mlsanity.com/paper/2310.03502/) (2023)
- [PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2309.05793/) (2023)

## Built on by (most cited first, 30 of 35)

- [CamI2V: Camera-Controlled Image-to-Video Diffusion Model](https://mlsanity.com/paper/2410.15957/) (2024)
- [Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large Language Models](https://mlsanity.com/paper/2409.10695/) (2024)
- [MV-Adapter: Multi-view Consistent Image Generation Made Easy](https://mlsanity.com/paper/2412.03632/) (2024)
- [Text4Seg: Reimagining Image Segmentation as Text Generation](https://mlsanity.com/paper/2410.09855/) (2024)
- [RealCam-I2V: Real-World Image-to-Video Generation with Interactive Complex Camera Control](https://mlsanity.com/paper/2502.10059/) (2025)
- [CreatiDesign: A Unified Multi-Conditional Diffusion Transformer for Creative Graphic Design](https://mlsanity.com/paper/2505.19114/) (2025)
- [A Survey on Personalized Content Synthesis with Diffusion Models](https://mlsanity.com/paper/2405.05538/) (2024)
- [Generating Multi-Image Synthetic Data for Text-to-Image Customization](https://mlsanity.com/paper/2502.01720/) (2025)
- [Personalize Anything for Free with Diffusion Transformer](https://mlsanity.com/paper/2503.12590/) (2025)
- [Personalized Image Generation with Deep Generative Models: A Decade Survey](https://mlsanity.com/paper/2502.13081/) (2025)
- [Personalized Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2412.02142/) (2024)
- [InstantCharacter: Personalize Any Characters with a Scalable Diffusion Transformer Framework](https://mlsanity.com/paper/2504.12395/) (2025)
- [RealCustom++: Representing Images as Real Textual Word for Real-Time Customization](https://mlsanity.com/paper/2408.09744/) (2024)
- [PolyVivid: Vivid Multi-Subject Video Generation with Cross-Modal Interaction and Enhancement](https://mlsanity.com/paper/2506.07848/) (2025)
- [MagicTailor: Component-Controllable Personalization in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2410.13370/) (2024)
- [CustomContrast: A Multilevel Contrastive Perspective For Subject-Driven Text-to-Image Customization](https://mlsanity.com/paper/2409.05606/) (2024)
- [Text-to-Image Synthesis: A Decade Survey](https://mlsanity.com/paper/2411.16164/) (2024)
- [UNIC-Adapter: Unified Image-instruction Adapter with Multi-modal Transformer for Image Generation](https://mlsanity.com/paper/2412.18928/) (2024)
- [Text4Seg++: Advancing Image Segmentation via Generative Language Modeling](https://mlsanity.com/paper/2509.06321/) (2025)
- [LiON-LoRA: Rethinking LoRA Fusion to Unify Controllable Spatial and Temporal Generation for Video Diffusion](https://mlsanity.com/paper/2507.05678/) (2025)
- [Uncertainty-o: One Model-agnostic Framework for Unveiling Uncertainty in Large Multimodal Models](https://mlsanity.com/paper/2506.07575/) (2025)
- [Have Multimodal Large Language Models (MLLMs) Really Learned to Tell the Time on Analog Clocks?](https://mlsanity.com/paper/2505.10862/) (2025)
- [FlashEdit: Decoupling Speed, Structure, and Semantics for Precise Image Editing](https://mlsanity.com/paper/2509.22244/) (2025)
- [FlexIP: Dynamic Control of Preservation and Personality for Customized Image Generation](https://mlsanity.com/paper/2504.07405/) (2025)
- [Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects](https://mlsanity.com/paper/2505.20909/) (2025)
- [FreeCus: Free Lunch Subject-driven Customization in Diffusion Transformers](https://mlsanity.com/paper/2507.15249/) (2025)
- [SketchAgent: Generating Structured Diagrams from Hand-Drawn Sketches](https://mlsanity.com/paper/2508.01237/) (2025)
- [IMG: Calibrating Diffusion Models via Implicit Multimodal Guidance](https://mlsanity.com/paper/2509.26231/) (2025)
- [Tuning-free Visual Effect Transfer across Videos](https://mlsanity.com/paper/2601.07833/) (2026)
- [CollectionLoRA: Collecting 50 Effects in 1 LoRA via Multi-Teacher On-Policy Distillation](https://mlsanity.com/paper/2605.25378/) (2026)

## Abstract (arXiv)

In this paper, 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. Our work is open-source, thereby providing universal access to these advancements.