# Generating Multi-Image Synthetic Data for Text-to-Image Customization

arXiv: https://arxiv.org/abs/2502.01720 · HTML: https://mlsanity.com/paper/2502.01720/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 79 of its 87 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Builds on (most cited first, 30 of 79)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [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)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)

## Built on by (most cited first, 25 of 25)

- [Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities](https://mlsanity.com/paper/2505.02567/) (2025)
- [Ovis-U1 Technical Report](https://mlsanity.com/paper/2506.23044/) (2025)
- [CreatiDesign: A Unified Multi-Conditional Diffusion Transformer for Creative Graphic Design](https://mlsanity.com/paper/2505.19114/) (2025)
- [Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction](https://mlsanity.com/paper/2505.02471/) (2025)
- [Learning an Image Editing Model without Image Editing Pairs](https://mlsanity.com/paper/2510.14978/) (2025)
- [Scaling Group Inference for Diverse and High-Quality Generation](https://mlsanity.com/paper/2508.15773/) (2025)
- [IC-Custom: Diverse Image Customization via In-Context Learning](https://mlsanity.com/paper/2507.01926/) (2025)
- [UP2You: Fast Reconstruction of Yourself from Unconstrained Photo Collections](https://mlsanity.com/paper/2509.24817/) (2025)
- [Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis](https://mlsanity.com/paper/2505.23325/) (2025)
- [MICON-Bench: Benchmarking and Enhancing Multi-Image Context Image Generation in Unified Multimodal Models](https://mlsanity.com/paper/2602.19497/) (2026)
- [From Competition to Synergy: Unlocking Reinforcement Learning for Subject-Driven Image Generation](https://mlsanity.com/paper/2510.18263/) (2025)
- [In-Context Sync-LoRA for Portrait Video Editing](https://mlsanity.com/paper/2512.03013/) (2025)
- [Tuning-free Visual Effect Transfer across Videos](https://mlsanity.com/paper/2601.07833/) (2026)
- [Identity-Preserving Text-to-Image Generation via Dual-Level Feature Decoupling and Expert-Guided Fusion](https://mlsanity.com/paper/2505.22360/) (2025)
- [RefAny3D: 3D Asset-Referenced Diffusion Models for Image Generation](https://mlsanity.com/paper/2601.22094/) (2026)
- [JRM: Joint Reconstruction Model for Multiple Objects without Alignment](https://mlsanity.com/paper/2603.25985/) (2026)
- [Adversarial Concept Distillation for One-Step Diffusion Personalization](https://mlsanity.com/paper/2510.20512/) (2025)
- [ConceptPrism: Concept Disentanglement in Personalized Diffusion Models via Residual Token Optimization](https://mlsanity.com/paper/2602.19575/) (2026)
- [NearID: Identity Representation Learning via Near-identity Distractors](https://mlsanity.com/paper/2604.01973/) (2026)
- [Redirecting the Flow: Image Customization through Attention Distribution Shift](https://mlsanity.com/paper/2606.16866/) (2026)
- [Thinking in Boxes: 3D Editing in Real Images Made Easy](https://mlsanity.com/paper/2606.20556/) (2026)
- [Keep The Essentials: Efficient Reference Conditioned Generation via Token Dropping](https://mlsanity.com/paper/2606.23682/) (2026)
- [DivRL: Disentangled Self-Similarity Rewards for Diverse Subject-Driven Generation](https://mlsanity.com/paper/2606.23950/) (2026)
- [Latent-Identity Tuning in Text-to-Image Personalization Models](https://mlsanity.com/paper/2607.11885/) (2026)
- [ReGain: Restoring Subject Fidelity in Personalization on Synthetic Images](https://mlsanity.com/paper/2609.38680/) (2026)

## Abstract (arXiv)

Customization of text-to-image models enables users to insert new concepts or objects and generate them in unseen settings. Existing methods either rely on comparatively expensive test-time optimization or train encoders on single-image datasets without multi-image supervision, which can limit image quality. We propose a simple approach to address these challenges. We first leverage existing text-to-image models and 3D datasets to create a high-quality Synthetic Customization Dataset (SynCD) consisting of multiple images of the same object in different lighting, backgrounds, and poses. Using this dataset, we train an encoder-based model that incorporates fine-grained visual details from reference images via a shared attention mechanism. Finally, we propose an inference technique that normalizes text and image guidance vectors to mitigate overexposure issues in sampled images. Through extensive experiments, we show that our encoder-based model, trained on SynCD, and with the proposed inference algorithm, improves upon existing encoder-based methods on standard customization benchmarks.