Image Editing
2개 벤치마크 · 논문 613편 · 이 태스크의 논문 보기 →
Benchmarks
ImgEdit-Data
GEdit-Bench-EN
Most implemented
InstructPix2Pix: Learning to Follow Image Editing Instructions
SenseNova-U1.5: Towards Native Unified Visual Intelligence
FlowMimic: Mask-free Visual Editing and Generation with Pixel-pair Warped Flow Field for Online Video Editing Data Generation and Modality Mimicry
Mi-Ripple: Restoring Images Degraded by Iterative AI Editing
Papers
When Text-to-Image Helps Editing: The Effects of Conditioning During Denoising
Unified models are trained for both instruction-based image editing and text-to-image (T2I) generation, but standard editing pipelines keep source-image conditioning throughout denoising. We ask whether editing can benef…
Image EditingDo MLLM Judges Judge the Edit? Auditing Bias in Image Editing Evaluation with Verified Quality Preservation
Multimodal large language models (MLLMs) are increasingly used as automated judges for instruction-based image editing and as reward signals for model training. However, systematically auditing whether these judges are i…
Image EditingFlowTool: Controlling Tool Parameter in Image Retouching via Flow Matching
Tool-based image editing (image retouching) is commonly formulated with autoregressive multimodal large language models (MLLMs) that sequentially generate reasoning, tool selections, and parameter values. In this work, w…
Image EditingOn-Policy Self-Distillation for Multi-Turn Image Editing
Instruction-based image editing has achieved strong performance in single-turn settings, yet practical editing is often iterative, with each instruction applied to the output of the previous turn. We find that existing e…
Image EditingRefinement Is Inherently Editable: Training-Free Prompt-to-Prompt Image Editing with Generative Refinement Network
Text-guided image editing must introduce the requested changes while preserving unrelated source content. Diffusion-based editors rely on spatial controls whose inaccuracies can leave edits incomplete or alter unrelated …
Image EditingMi-Ripple: Restoring Images Degraded by Iterative AI Editing
Iterative reference-conditioned image editing can introduce grid-like and granular textures, commonly described as digital ripple. We present Mi-Ripple, a diagnosis-guided restoration workflow that suppresses this digita…
Image Editing