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Diffusion Model-Based Image Editing: A Survey

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arxiv 2402.17525 v4 pith:NJ6VDZZQ submitted 2024-02-27 cs.CV

Yi Huang , Jiancheng Huang , Yifan Liu , Mingfu Yan , Jiaxi Lv , Jianzhuang Liu , Wei Xiong , He Zhang
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Liangliang Cao Shifeng Chen
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classification cs.CV
keywords editingimagediffusionanalysiscurrentlearningmethodsmodels
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Denoising diffusion models have emerged as a powerful tool for various image generation and editing tasks, facilitating the synthesis of visual content in an unconditional or input-conditional manner. The core idea behind them is learning to reverse the process of gradually adding noise to images, allowing them to generate high-quality samples from a complex distribution. In this survey, we provide an exhaustive overview of existing methods using diffusion models for image editing, covering both theoretical and practical aspects in the field. We delve into a thorough analysis and categorization of these works from multiple perspectives, including learning strategies, user-input conditions, and the array of specific editing tasks that can be accomplished. In addition, we pay special attention to image inpainting and outpainting, and explore both earlier traditional context-driven and current multimodal conditional methods, offering a comprehensive analysis of their methodologies. To further evaluate the performance of text-guided image editing algorithms, we propose a systematic benchmark, EditEval, featuring an innovative metric, LMM Score. Finally, we address current limitations and envision some potential directions for future research. The accompanying repository is released at https://github.com/SiatMMLab/Awesome-Diffusion-Model-Based-Image-Editing-Methods.

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Cited by 36 Pith papers

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

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    On real Reddit photo-editing requests, human judges prefer human edits over AI edits 66% of the time, and AI editors can satisfactorily handle about 33% of requests.

  2. Discrete Noise Inversion for Next-scale Autoregressive Text-based Image Editing

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    VARIN uses a Location-aware Argmax Inversion pseudo-inverse of Gumbel-max sampling to extract editable discrete noises, enabling training-free prompt-guided editing for visual autoregressive models.

  3. Make Your MoVe: Make Your 3D Contents by Adapting Multi-View Diffusion Models to External Editing

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    A tuning-free dual-pipeline that injects original normal latents into an edited multi-view diffusion stream, preserving geometry during 2D-to-3D appearance editing.

  4. OutDreamer: Video Outpainting with a Diffusion Transformer

    cs.CV 2025-06 conditional novelty 6.0 of 10

    OutDreamer couples a diffusion transformer with mask-driven self-attention and a latent alignment loss to outpaint videos in a zero-shot manner, exceeding prior zero-shot baselines on standard benchmarks.

  5. Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models

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    An exact denoising posterior score is derived and used to compute time-dependent DPS step sizes that transfer to colorization, inpainting, and super-resolution.

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

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    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.

  7. REED-VAE: RE-Encode Decode Training for Iterative Image Editing with Diffusion Models

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    REED-VAE fine-tunes only the decoder of a Stable Diffusion VAE with iterative encode-decode training, dynamic k incrementation, and a first-step loss, reducing artifact accumulation over 5 to 25 editing iterations.

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    VideoPure is a diffusion-based adversarial purification framework that combines temporal DDIM inversion, spatial-temporal optimization, and multi-step voting to defend video recognition models.

  9. IE-Bench: Advancing the Measurement of Text-Driven Image Editing for Human Perception Alignment

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    IE-Bench provides the first MOS-labeled dataset for text-driven image editing and IE-QA, a learned metric that outperforms existing metrics on that dataset.

  10. Dual-Schedule Inversion: Training- and Tuning-Free Inversion for Real Image Editing

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    Dual-Schedule Inversion uses two coupled time schedules so that diffusion inversion and sampling are mathematically reversible, enabling tuning-free real image reconstruction and editing.

  11. BrushEdit: All-In-One Image Inpainting and Editing

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    BrushEdit couples a multimodal language model and an object detector with a single arbitrary-mask inpainting model to turn free-form text instructions into interactive, multi-turn image edits.

  12. 3D-Consistent Image Inpainting with Diffusion Models

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  13. Pinco: Position-induced Consistent Adapter for Diffusion Transformer in Foreground-conditioned Inpainting

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    Pinco is a plug-and-play adapter that enables diffusion transformers to inpaint backgrounds around a provided foreground object, preserving its shape via self-attention injection and a positional anchor.

  14. DIVE: Taming DINO for Subject-Driven Video Editing

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    DIVE uses DINOv2 feature maps as automatic video correspondences to carry source motion, while LoRA adapters carry the target identity.

  15. Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning

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    UES adds a self-supervised video condition to text-to-video diffusion models, enabling them to edit videos from delta prompts without paired supervision.

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  17. InsightEdit: Towards Better Instruction Following for Image Editing

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    InsightEdit uses multimodal language model features in a two-stream adapter to improve complex instruction following and background consistency in image editing.

  18. Pathways on the Image Manifold: Image Editing via Video Generation

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    Frame2Frame performs text-based image editing by generating a short video transition from the source image and selecting the best resulting frame, achieving competitive or better benchmark scores than single-image dif...

  19. HeadRouter: A Training-free Image Editing Framework for MM-DiTs by Adaptively Routing Attention Heads

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    A training-free editing framework that routes attention heads by semantic sensitivity and refines text/image tokens, improving text-guided editing for MM-DiTs.

  20. Stable Flow: Vital Layers for Training-Free Image Editing

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    An automatic vital-layer selection for FLUX enables training-free, stable text-driven image editing via selective attention injection.

  21. Provable diffusion-based posterior sampling for linear inverse problems via DDIM

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    A SVD-based, coordinate-wise DDIM sampler is claimed to asymptotically sample from the posterior for noisy linear inverse problems, but the proof's posterior identification step does not follow from the stated updates.

  22. PairEdit: Learning Semantic Variations for Exemplar-based Image Editing

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    PairEdit trains two LoRA adapters on a pretrained diffusion model to capture the semantic direction between paired source-target images, enabling text-free, controllable image editing from as few as one pair.

  23. Hallucination at a Glance: Controlled Visual Edits and Fine-Grained Multimodal Learning

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    A new micro-edit dataset and fine-tuning recipe appear to help multimodal LLMs notice small visual changes, but the central 'feature consistency loss' claim is not present in the method.

  24. TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species Generation

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    A diffusion model trained progressively from Kingdom to Species generates more accurate fine-grained animal images, including rare species with as few as one training sample.

  25. X-Edit: Detecting and Localizing Edits in Images Altered by Text-Guided Diffusion Models

    cs.CV 2025-05 conditional novelty 5.0 of 10

    X-Edit uses Stable Diffusion inversion features with a U-Net and attention to predict edited-region masks, and contributes a paired 167,026-image dataset for the task.

  26. Exploring the latent space of diffusion models directly through singular value decomposition

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    The authors report that singular value decomposition of diffusion latent codes reveals stable, order-mobile attribute directions and propose Attribute Vector Integration, a per-pair MLP-based editor that transfers tex...

  27. RMD: A Simple Baseline for More General Human Motion Generation via Training-free Retrieval-Augmented Motion Diffuse

    cs.CV 2024-12 conditional novelty 5.0 of 10

    RMD improves text-to-motion generation by decomposing prompts with an LLM, retrieving and recombining body-part motions, and refining them with a pretrained diffusion model, all without additional training.

  28. MagicQuill: An Intelligent Interactive Image Editing System

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    MagicQuill combines brush-based edge and color control with an MLLM that guesses user intent, enabling fast interactive image edits without typing prompts.

  29. Jailbreak Attacks and Defenses against Multimodal Generative Models: A Survey

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  31. SecureT2I: No More Unauthorized Manipulation on AI Generated Images from Prompts

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    A diffusion editing model is fine-tuned with a blur target for forbidden images and the original output for permitted images, claiming selective suppression of unauthorized edits.

  32. Component Adaptive Clustering for Generalized Category Discovery

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  33. Unsupervised Region-Based Image Editing of Denoising Diffusion Models

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    A masking and Jacobian projection technique discovers unsupervised semantic directions in diffusion model latent space, enabling region-local editing without fine-tuning.

  34. DLSF: Dual-Layer Synergistic Fusion for High-Fidelity Image Syn-thesis

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  35. Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review

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  36. A Survey on Pre-Trained Diffusion Model Distillations

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