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AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea

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arxiv 2411.15738 v3 pith:GPZDT5FJ submitted 2024-11-24 cs.CV

Qifan Yu , Wei Chow , Zhongqi Yue , Kaihang Pan , Yang Wu , Xiaoyang Wan , Juncheng Li , Siliang Tang
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Hanwang Zhang Yueting Zhuang
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classification cs.CV
keywords editinganyeditimagemodelscomprehensivedatadatasetdiversity
verification ladder T0 review T1 audit T2 compute T3 formal
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Instruction-based image editing aims to modify specific image elements with natural language instructions. However, current models in this domain often struggle to accurately execute complex user instructions, as they are trained on low-quality data with limited editing types. We present AnyEdit, a comprehensive multi-modal instruction editing dataset, comprising 2.5 million high-quality editing pairs spanning over 20 editing types and five domains. We ensure the diversity and quality of the AnyEdit collection through three aspects: initial data diversity, adaptive editing process, and automated selection of editing results. Using the dataset, we further train a novel AnyEdit Stable Diffusion with task-aware routing and learnable task embedding for unified image editing. Comprehensive experiments on three benchmark datasets show that AnyEdit consistently boosts the performance of diffusion-based editing models. This presents prospects for developing instruction-driven image editing models that support human creativity.

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Cited by 13 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FocusDiff: Advancing Fine-Grained Text-Image Alignment for Autoregressive Visual Generation through RL

    cs.CV 2025-06 conditional novelty 7.0 of 10

    FocusDiff improves autoregressive text-to-image generation by training on paired similar prompts with a modified GRPO objective, achieving state-of-the-art alignment on PairComp and gains on GenEval and T2I-CompBench.

  2. Under One Sun: Multi-Object Generative Perception of Materials and Illumination

    cs.CV 2026-03 conditional novelty 6.0 of 10

    Factorizing video editing into semantic-token anchoring and motion-restoration pre-training produces strong zero-shot and SOTA open-source instruction-guided video edits without heavy external structural priors.

  3. Edit in 2D, Verify in 3D: Reinforcement Learning for Multi-view Consistent Scene Editing

    cs.CV 2026-03 conditional novelty 6.0 of 10

    RL3DEdit fine-tunes FLUX-Kontext with GRPO using VGGT confidence and pose rewards to produce multi-view consistent 3D scene edits in a single pass.

  4. ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A 91K GPT-4o-generated image and editing dataset, and a fine-tuned open model Janus-4o, report improved text-to-image scores and new editing ability.

  5. ComplexBench-Edit: Benchmarking Complex Instruction-Driven Image Editing via Compositional Dependencies

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Introduces a benchmark for chain-dependent image editing instructions plus a region-aware consistency metric, and shows a chain-of-thought prompt improves a Gemini-based editor.

  6. Image Editing As Programs with Diffusion Models

    cs.CV 2025-06 conditional novelty 6.0 of 10

    IEAP decomposes complex editing instructions into atomic operations executed sequentially on a diffusion transformer, and reports state-of-the-art results on MagicBrush and AnyEdit.

  7. DiffDecompose: Layer-Wise Decomposition of Alpha-Composited Images via Diffusion Transformers

    cs.CV 2025-05 conditional novelty 6.0 of 10

    DiffDecompose recovers foreground and background layers from alpha-composited images using in-context diffusion with position encoding cloning, trained and evaluated on a new six-task synthetic dataset.

  8. KRIS-Bench: Benchmarking Next-Level Intelligent Image Editing Models

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A new benchmark, KRIS-Bench, evaluates image editing models on knowledge-grounded reasoning across factual, conceptual, and procedural tasks, and finds large performance gaps in current models.

  9. Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing

    cs.CV 2026-07 conditional novelty 5.0 of 10

    A compact 4B image generation/editing system with a fast one-step VAE, native-resolution packing, RL alignment, and 4-step distillation reports competitive benchmarks against 6B–80B open models.

  10. SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models

    cs.CV 2025-10 conditional novelty 5.0 of 10

    A unified multimodal model can improve its own text-to-image generation by using its understanding module as a rewarder in a global-plus-local reward-weighted training loop.

  11. ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid Motions

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A released 6.4 million pair dataset and 613 sample benchmark for instruction-guided image editing of non-rigid motions, plus a Flux.1-dev based baseline that outperforms open-source methods on the new benchmark.

  12. SeedEdit 3.0: Fast and High-Quality Generative Image Editing

    cs.CV 2025-06 conditional novelty 4.0 of 10

    SeedEdit 3.0 reports a 56.1% usability rate on internal real-image editing tests, beating SeedEdit 1.6, GPT-4o, and Gemini 2.0, with 8x faster inference after distillation and quantization.

  13. MIND-Edit: MLLM Insight-Driven Editing via Language-Vision Projection

    cs.CV 2025-05 reject novelty 4.0 of 10

    MIND-Edit combines instruction rewriting with MLLM-derived visual embeddings to guide diffusion-based image editing, but the reported numbers only partly support the claim of state-of-the-art performance.

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