Low-Resolution Editing is All You Need for High-Resolution Editing
Not on the map. Why?
Abstract (arXiv)
High-resolution content creation is rapidly emerging as a central challenge in both the vision and graphics communities. Images serve as the most fundamental modality for visual expression, and content generation that aligns with the user intent requires effective, controllable high-resolution image manipulation mechanisms. However, existing approaches remain limited to low-resolution settings, typically supporting only up to 1K resolution. In this work, we introduce the task of high-resolution image editing and propose a test-time optimization framework to address it. Our method performs patch-wise optimization on high-resolution source images, followed by a fine-grained detail transfer module and a novel synchronization strategy to maintain consistency across patches. Extensive experiments show that our method produces high-quality edits, facilitating high-resolution content creation.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 55 of its 63 references that are in the map. First posted on arXiv 25 Nov 2025.
Builds on
55 of 63 references.
References · 55
- 2017Attention Is All You Needcited by 60,361
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 2020Denoising Diffusion Implicit Modelscited by 9,781
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
38 more
- 2022Scalable Diffusion Models with Transformerscited by 6,054
- 2022Flow Matching for Generative Modelingcited by 5,702
- 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,356
- 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
- 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
- 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
- 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,611
- 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,431
- 2023Efficient Streaming Language Models with Attention Sinkscited by 2,186
- 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,476
- 2022Null-text Inversion for Editing Real Images using Guided Diffusion Modelscited by 1,179
- 2021Blended Diffusion for Text-driven Editing of Natural Imagescited by 927
- 2022Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translationcited by 906
- 2023MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editingcited by 727
- 2022DiffEdit: Diffusion-based semantic image editing with mask guidancecited by 647
- 2021DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulationcited by 642
- 2023Zero-shot Image-to-Image Translationcited by 505
- 2023Exploiting Diffusion Prior for Real-World Image Super-Resolutioncited by 489
- 2025Step1X-Edit: A Practical Framework for General Image Editingcited by 429
- 2023SeeSR: Towards Semantics-Aware Real-World Image Super-Resolutioncited by 315
- 2023Diffusion Self-Guidance for Controllable Image Generationcited by 305
- 2024Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wildcited by 286
- 2024One-Step Effective Diffusion Network for Real-World Image Super-Resolutioncited by 246
- 2023SinSR: Diffusion-Based Image Super-Resolution in a Single Stepcited by 238
- 2025In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformercited by 204
- 2023ScaleCrafter: Tuning-free Higher-Resolution Visual Generation with Diffusion Modelscited by 128
- 2025MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsingcited by 116
- 2023DemoFusion: Democratising High-Resolution Image Generation With No cited by 107
- 2024Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approachcited by 97
- 2024TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolutioncited by 87
- 2024DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe Dataset Curationcited by 65
- 2025Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Modelscited by 65
- 2024FouriScale: A Frequency Perspective on Training-Free High-Resolution Image Synthesiscited by 59
- 2016A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessmentcited by 57
- 2025DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolutioncited by 53
- 2025KV-Edit: Training-Free Image Editing for Precise Background Preservationcited by 49
- 2024FreeScale: Unleashing the Resolution of Diffusion Models via Tuning-Free Scale Fusioncited by 30
- 2024Effective Diffusion Transformer Architecture for Image Super-Resolutioncited by 21
50 most cited of 55 All 55 (JSON)
Built on by
Citing papers per year: 2026: 2.
2 papers.