# ViCo: Plug-and-play Visual Condition for Personalized Text-to-image Generation

arXiv: https://arxiv.org/abs/2306.00971 · HTML: https://mlsanity.com/paper/2306.00971/

## Key facts

- Cited by 23 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 57 of its 63 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## 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, 30 of 57)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [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)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [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)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)

## Built on by (most cited first, 23 of 23)

- [Less-to-More Generalization: Unlocking More Controllability by In-Context Generation](https://mlsanity.com/paper/2504.02160/) (2025)
- [A Survey on Personalized Content Synthesis with Diffusion Models](https://mlsanity.com/paper/2405.05538/) (2024)
- [Personalized Image Generation with Deep Generative Models: A Decade Survey](https://mlsanity.com/paper/2502.13081/) (2025)
- [LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation](https://mlsanity.com/paper/2412.05148/) (2024)
- [SingleInsert: Inserting New Concepts from a Single Image into Text-to-Image Models for Flexible Editing](https://mlsanity.com/paper/2310.08094/) (2023)
- [DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability](https://mlsanity.com/paper/2503.06505/) (2025)
- [CusConcept: Customized Visual Concept Decomposition with Diffusion Models](https://mlsanity.com/paper/2410.00398/) (2024)
- [Text-to-Image Synthesis: A Decade Survey](https://mlsanity.com/paper/2411.16164/) (2024)
- [Memory-Efficient Fine-Tuning for Quantized Diffusion Model](https://mlsanity.com/paper/2401.04339/) (2024)
- [Steering Guidance for Personalized Text-to-Image Diffusion Models](https://mlsanity.com/paper/2508.00319/) (2025)
- [DreamCache: Finetuning-Free Lightweight Personalized Image Generation via Feature Caching](https://mlsanity.com/paper/2411.17786/) (2024)
- [DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image Diffusion Models](https://mlsanity.com/paper/2411.19390/) (2024)
- [Customized Generation Reimagined: Fidelity and Editability Harmonized](https://mlsanity.com/paper/2412.04831/) (2024)
- [APT: Adaptive Personalized Training for Diffusion Models with Limited Data](https://mlsanity.com/paper/2507.02687/) (2025)
- [DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation](https://mlsanity.com/paper/2603.08090/) (2026)
- [EZIGen: Enhancing zero-shot personalized image generation with precise subject encoding and decoupled guidance](https://mlsanity.com/paper/2409.08091/) (2024)
- [Directional Textual Inversion for Personalized Text-to-Image Generation](https://mlsanity.com/paper/2512.13672/) (2025)
- [SpotDiff: Spotting and Disentangling Interference in Feature Space for Subject-Preserving Image Generation](https://mlsanity.com/paper/2510.07340/) (2025)
- [Forget Less by Learning Together through Concept Consolidation](https://mlsanity.com/paper/2601.01963/) (2026)
- [Decoupled Guidance: Disentangling Subject and Context Pathways in Text-to-Image Personalization](https://mlsanity.com/paper/2607.00766/) (2026)
- [BridgeIV: Bridging Customized Image and Video Generation through Test-Time Autoregressive Identity Propagation](https://mlsanity.com/paper/2505.06985/) (2025)
- [Intrinsic Concept Extraction Based on Compositional Interpretability](https://mlsanity.com/paper/2603.11795/) (2026)
- [AnimeAdapter: A Modular Adapter for Appearance-Consistent Anime Character Generation](https://mlsanity.com/paper/2605.20237/) (2026)

## Abstract (arXiv)

Personalized text-to-image generation using diffusion models has recently emerged and garnered significant interest. This task learns a novel concept (e.g., a unique toy), illustrated in a handful of images, into a generative model that captures fine visual details and generates photorealistic images based on textual embeddings. In this paper, we present ViCo, a novel lightweight plug-and-play method that seamlessly integrates visual condition into personalized text-to-image generation. ViCo stands out for its unique feature of not requiring any fine-tuning of the original diffusion model parameters, thereby facilitating more flexible and scalable model deployment. This key advantage distinguishes ViCo from most existing models that necessitate partial or full diffusion fine-tuning. ViCo incorporates an image attention module that conditions the diffusion process on patch-wise visual semantics, and an attention-based object mask that comes at no extra cost from the attention module. Despite only requiring light parameter training (~6% compared to the diffusion U-Net), ViCo delivers performance that is on par with, or even surpasses, all state-of-the-art models, both qualitatively and quantitatively. This underscores the efficacy of ViCo, making it a highly promising solution for personalized text-to-image generation without the need for diffusion model fine-tuning. Code: https://github.com/haoosz/ViCo