# MagicNaming: Consistent Identity Generation by Finding a "Name Space" in T2I Diffusion Models

arXiv: https://arxiv.org/abs/2412.14902 · HTML: https://mlsanity.com/paper/2412.14902/

## Key facts

- Cited by 5 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 21 of its 51 references that are in the map.
- First posted on arXiv 19 Dec 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, 21 of 21)

- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [InstantID: Zero-shot Identity-Preserving Generation in Seconds](https://mlsanity.com/paper/2401.07519/) (2024)
- [InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation](https://mlsanity.com/paper/2309.06380/) (2023)
- [PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding](https://mlsanity.com/paper/2312.04461/) (2023)
- [FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention](https://mlsanity.com/paper/2305.10431/) (2023)
- [Aligning Text-to-Image Diffusion Models with Reward Backpropagation](https://mlsanity.com/paper/2310.03739/) (2023)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models](https://mlsanity.com/paper/2302.12228/) (2023)
- [Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning](https://mlsanity.com/paper/2307.11410/) (2023)
- [ImagenHub: Standardizing the evaluation of conditional image generation models](https://mlsanity.com/paper/2310.01596/) (2023)
- [PortraitBooth: A Versatile Portrait Model for Fast Identity-preserved Personalization](https://mlsanity.com/paper/2312.06354/) (2023)
- [DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image Generation](https://mlsanity.com/paper/2305.03374/) (2023)
- [Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models](https://mlsanity.com/paper/2310.13828/) (2023)
- [PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2309.05793/) (2023)
- [Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era](https://mlsanity.com/paper/2305.06131/) (2023)
- [FaceStudio: Put Your Face Everywhere in Seconds](https://mlsanity.com/paper/2312.02663/) (2023)
- [DreamTuner: Single Image is Enough for Subject-Driven Generation](https://mlsanity.com/paper/2312.13691/) (2023)
- [Direct Inversion: Optimization-Free Text-Driven Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.07825/) (2022)
- [Photorealistic and Identity-Preserving Image-Based Emotion Manipulation with Latent Diffusion Models](https://mlsanity.com/paper/2308.03183/) (2023)

## Built on by (most cited first, 5 of 5)

- [Personalized Image Generation with Deep Generative Models: A Decade Survey](https://mlsanity.com/paper/2502.13081/) (2025)
- [SceneDecorator: Towards Scene-Oriented Story Generation with Scene Planning and Scene Consistency](https://mlsanity.com/paper/2510.22994/) (2025)
- [BeyondFacial: Identity-Preserving Personalized Generation Beyond Facial Close-ups](https://mlsanity.com/paper/2511.11989/) (2025)
- [Training for Identity, Inference for Controllability: A Unified Approach to Tuning-Free Face Personalization](https://mlsanity.com/paper/2512.03964/) (2025)
- [NAMESAKES: Probing Identity Memorization in Text-to-Image Models](https://mlsanity.com/paper/2606.20155/) (2026)

## Abstract (arXiv)

Large-scale text-to-image diffusion models, (e.g., DALL-E, SDXL) are capable of generating famous persons by simply referring to their names. Is it possible to make such models generate generic identities as simple as the famous ones, e.g., just use a name? In this paper, we explore the existence of a "Name Space", where any point in the space corresponds to a specific identity. Fortunately, we find some clues in the feature space spanned by text embedding of celebrities' names. Specifically, we first extract the embeddings of celebrities' names in the Laion5B dataset with the text encoder of diffusion models. Such embeddings are used as supervision to learn an encoder that can predict the name (actually an embedding) of a given face image. We experimentally find that such name embeddings work well in promising the generated image with good identity consistency. Note that like the names of celebrities, our predicted name embeddings are disentangled from the semantics of text inputs, making the original generation capability of text-to-image models well-preserved. Moreover, by simply plugging such name embeddings, all variants (e.g., from Civitai) derived from the same base model (i.e., SDXL) readily become identity-aware text-to-image models. Project homepage: urlhttps://magicfusion.github.io/MagicNaming/.