# PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language Instructions

arXiv: https://arxiv.org/abs/2409.15278 · HTML: https://mlsanity.com/paper/2409.15278/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 129 of its 162 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Builds on (most cited first, 30 of 129)

- [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)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [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)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [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)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (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)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)

## Built on by (most cited first, 30 of 31)

- [Video models are zero-shot learners and reasoners](https://mlsanity.com/paper/2509.20328/) (2025)
- [UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics](https://mlsanity.com/paper/2412.07774/) (2024)
- [Lumina-Image 2.0: A Unified and Efficient Image Generative Framework](https://mlsanity.com/paper/2503.21758/) (2025)
- [TIIF-Bench: How Does Your T2I Model Follow Your Instructions?](https://mlsanity.com/paper/2506.02161/) (2025)
- [No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves](https://mlsanity.com/paper/2505.02831/) (2025)
- [UniCTokens: Boosting Personalized Understanding and Generation via Unified Concept Tokens](https://mlsanity.com/paper/2505.14671/) (2025)
- [VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning](https://mlsanity.com/paper/2504.07960/) (2025)
- [Training-free Regional Prompting for Diffusion Transformers](https://mlsanity.com/paper/2411.02395/) (2024)
- [Uni-MoE-2.0-Omni: Scaling Language-Centric Omnimodal Large Model with Advanced MoE, Training and Data](https://mlsanity.com/paper/2511.12609/) (2025)
- [RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models](https://mlsanity.com/paper/2503.10406/) (2025)
- [RestoreVAR: Visual Autoregressive Generation for All-in-One Image Restoration](https://mlsanity.com/paper/2505.18047/) (2025)
- [Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision](https://mlsanity.com/paper/2504.04903/) (2025)
- [EditMGT: Unleashing Potentials of Masked Generative Transformers in Image Editing](https://mlsanity.com/paper/2512.11715/) (2025)
- [MIGE: Mutually Enhanced Multimodal Instruction-Based Image Generation and Editing](https://mlsanity.com/paper/2502.21291/) (2025)
- [X2I: Seamless Integration of Multimodal Understanding into Diffusion Transformer via Attention Distillation](https://mlsanity.com/paper/2503.06134/) (2025)
- [Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning](https://mlsanity.com/paper/2507.01908/) (2025)
- [MICo-150K: A Comprehensive Dataset Advancing Multi-Image Composition](https://mlsanity.com/paper/2512.07348/) (2025)
- [PartEdit: Fine-Grained Image Editing using Pre-Trained Diffusion Models](https://mlsanity.com/paper/2502.04050/) (2025)
- [Skywork UniPic 3.0: Unified Multi-Image Composition via Sequence Modeling](https://mlsanity.com/paper/2601.15664/) (2026)
- [Jodi: Unification of Visual Generation and Understanding via Joint Modeling](https://mlsanity.com/paper/2505.19084/) (2025)
- [Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization](https://mlsanity.com/paper/2412.18525/) (2024)
- [Exploring Scalable Unified Modeling for General Low-Level Vision](https://mlsanity.com/paper/2507.14801/) (2025)
- [LORE: Latent Optimization for Precise Semantic Control in Rectified Flow-based Image Editing](https://mlsanity.com/paper/2508.03144/) (2025)
- [CoLoGen: Progressive Learning of Concept-Localization Duality for Unified Image Generation](https://mlsanity.com/paper/2602.22150/) (2026)
- [Generative Visual Chain-of-Thought for Image Editing](https://mlsanity.com/paper/2603.01893/) (2026)
- [Masked Generative Transformer Is What You Need for Image Editing](https://mlsanity.com/paper/2605.10859/) (2026)
- [FlowInOne:Unifying Multimodal Generation as Image-in, Image-out Flow Matching](https://mlsanity.com/paper/2604.06757/) (2026)
- [Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study](https://mlsanity.com/paper/2605.09622/) (2026)
- [Rethinking Vector Field Learning for Generative Segmentation](https://mlsanity.com/paper/2603.19218/) (2026)
- [Your Pre-trained Diffusion Model Secretly Knows Restoration](https://mlsanity.com/paper/2604.04924/) (2026)

## Abstract (arXiv)

This paper presents a versatile image-to-image visual assistant, PixWizard, designed for image generation, manipulation, and translation based on free-from language instructions. To this end, we tackle a variety of vision tasks into a unified image-text-to-image generation framework and curate an Omni Pixel-to-Pixel Instruction-Tuning Dataset. By constructing detailed instruction templates in natural language, we comprehensively include a large set of diverse vision tasks such as text-to-image generation, image restoration, image grounding, dense image prediction, image editing, controllable generation, inpainting/outpainting, and more. Furthermore, we adopt Diffusion Transformers (DiT) as our foundation model and extend its capabilities with a flexible any resolution mechanism, enabling the model to dynamically process images based on the aspect ratio of the input, closely aligning with human perceptual processes. The model also incorporates structure-aware and semantic-aware guidance to facilitate effective fusion of information from the input image. Our experiments demonstrate that PixWizard not only shows impressive generative and understanding abilities for images with diverse resolutions but also exhibits promising generalization capabilities with unseen tasks and human instructions. The code and related resources are available at https://github.com/AFeng-x/PixWizard