# Imagine yourself: Tuning-Free Personalized Image Generation

arXiv: https://arxiv.org/abs/2409.13346 · HTML: https://mlsanity.com/paper/2409.13346/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 22 of its 23 references that are in the map.
- First posted on arXiv 20 Sep 2024.

## Placed in

- 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, 22 of 22)

- [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)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (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)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [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)
- [Adversarial Diffusion Distillation](https://mlsanity.com/paper/2311.17042/) (2023)
- [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://mlsanity.com/paper/2105.13626/) (2021)
- [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)
- [PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding](https://mlsanity.com/paper/2312.04461/) (2023)
- [UL2: Unifying Language Learning Paradigms](https://mlsanity.com/paper/2205.05131/) (2022)
- [Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack](https://mlsanity.com/paper/2309.15807/) (2023)
- [HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models](https://mlsanity.com/paper/2307.06949/) (2023)
- [PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2309.05793/) (2023)
- [MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation](https://mlsanity.com/paper/2404.11565/) (2024)

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

- [Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://mlsanity.com/paper/2411.17440/) (2024)
- [InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity](https://mlsanity.com/paper/2503.16418/) (2025)
- [Movie Weaver: Tuning-Free Multi-Concept Video Personalization with Anchored Prompts](https://mlsanity.com/paper/2502.07802/) (2025)
- [Concat-ID: Towards Universal Identity-Preserving Video Synthesis](https://mlsanity.com/paper/2503.14151/) (2025)
- [EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM](https://mlsanity.com/paper/2412.09618/) (2024)
- [Personalized Image Generation with Deep Generative Models: A Decade Survey](https://mlsanity.com/paper/2502.13081/) (2025)
- [YoChameleon: Personalized Vision and Language Generation](https://mlsanity.com/paper/2504.20998/) (2025)
- [Personalized Image Generation with Large Multimodal Models](https://mlsanity.com/paper/2410.14170/) (2024)
- [A Unified Agentic Framework for Evaluating Conditional Image Generation](https://mlsanity.com/paper/2504.07046/) (2025)
- [ID-Patch: Robust ID Association for Group Photo Personalization](https://mlsanity.com/paper/2411.13632/) (2024)
- [HyperLoRA: Parameter-Efficient Adaptive Generation for Portrait Synthesis](https://mlsanity.com/paper/2503.16944/) (2025)
- [Text-to-Image Synthesis: A Decade Survey](https://mlsanity.com/paper/2411.16164/) (2024)
- [Personalization Toolkit: Training Free Personalization of Large Vision Language Models](https://mlsanity.com/paper/2502.02452/) (2025)
- [Unified Personalized Understanding, Generating and Editing](https://mlsanity.com/paper/2601.06965/) (2026)
- [POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generation](https://mlsanity.com/paper/2504.13392/) (2025)
- [Style-Friendly SNR Sampler for Style-Driven Generation](https://mlsanity.com/paper/2411.14793/) (2024)
- [FaceCrafter: Identity-Conditional Diffusion with Disentangled Control over Facial Pose, Expression, and Emotion](https://mlsanity.com/paper/2505.15313/) (2025)
- [SerialGen: Personalized Image Generation by First Standardization Then Personalization](https://mlsanity.com/paper/2412.01485/) (2024)
- [UIFace: Unleashing Inherent Model Capabilities to Enhance Intra-Class Diversity in Synthetic Face Recognition](https://mlsanity.com/paper/2502.19803/) (2025)
- [AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models](https://mlsanity.com/paper/2510.05715/) (2025)
- [Personal Visual Memory from Explicit and Implicit Evidence](https://mlsanity.com/paper/2605.28806/) (2026)
- [CoCoIns: Consistent Subject Generation via Contrastive Instantiated Concepts](https://mlsanity.com/paper/2503.24387/) (2025)
- [Zero-Shot Personalized Camera Motion Control for Image-to-Video Synthesis](https://mlsanity.com/paper/2504.09472/) (2025)
- [FontAdapter: Instant Font Adaptation in Visual Text Generation](https://mlsanity.com/paper/2506.05843/) (2025)
- [Say Cheese! Detail-Preserving Portrait Collection Generation via Natural Language Edits](https://mlsanity.com/paper/2601.20511/) (2026)
- [Non-Markov Multi-Round Conversational Image Generation with History-Conditioned MLLMs](https://mlsanity.com/paper/2601.20911/) (2026)
- [Keep the General, Inject the Specific: Structured Dialogue Fine-Tuning for Knowledge Injection without Catastrophic Forgetting](https://mlsanity.com/paper/2505.00029/) (2025)
- [Generating Fit Check Videos with a Handheld Camera](https://mlsanity.com/paper/2505.23886/) (2025)
- [Salient Concept-Aware Generative Data Augmentation](https://mlsanity.com/paper/2510.15194/) (2025)
- [CaricHarmony: Contrastive Diffusion Paths for Identity-Preserving Caricature Synthesis](https://mlsanity.com/paper/2606.13964/) (2026)

## Abstract (arXiv)

Diffusion models have demonstrated remarkable efficacy across various image-to-image tasks. In this research, we introduce Imagine yourself, a state-of-the-art model designed for personalized image generation. Unlike conventional tuning-based personalization techniques, Imagine yourself operates as a tuning-free model, enabling all users to leverage a shared framework without individualized adjustments. Moreover, previous work met challenges balancing identity preservation, following complex prompts and preserving good visual quality, resulting in models having strong copy-paste effect of the reference images. Thus, they can hardly generate images following prompts that require significant changes to the reference image, eg, changing facial expression, head and body poses, and the diversity of the generated images is low. To address these limitations, our proposed method introduces 1) a new synthetic paired data generation mechanism to encourage image diversity, 2) a fully parallel attention architecture with three text encoders and a fully trainable vision encoder to improve the text faithfulness, and 3) a novel coarse-to-fine multi-stage finetuning methodology that gradually pushes the boundary of visual quality. Our study demonstrates that Imagine yourself surpasses the state-of-the-art personalization model, exhibiting superior capabilities in identity preservation, visual quality, and text alignment. This model establishes a robust foundation for various personalization applications. Human evaluation results validate the model's SOTA superiority across all aspects (identity preservation, text faithfulness, and visual appeal) compared to the previous personalization models.