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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
This is my paper
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 11 Pith papers

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

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

    cs.CV 2025-09 conditional novelty 6.0 of 10

    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.

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

    cs.CV 2025-08 conditional novelty 6.0 of 10

    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.

  3. 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.

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

    cs.LG 2026-07 reject novelty 5.0 of 10

    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.

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

    cs.CV 2025-06 conditional novelty 5.0 of 10

    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.

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

    cs.CV 2025-06 reject novelty 5.0 of 10

    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.

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

    cs.CV 2025-06 conditional novelty 5.0 of 10

    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.

  8. Stationary Power-Law Solutions of Kinetic-Alfv\'{e}nic Turbulence

    physics.plasm-ph 2025-08 unverdicted novelty 4.0 of 10

    The submission cannot be assessed because the supplied full text is a different paper than the abstract and metadata describe.

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

    cs.CR 2025-07 reject novelty 4.0 of 10

    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.

  10. Component Adaptive Clustering for Generalized Category Discovery

    cs.CV 2025-07 conditional novelty 4.0 of 10

    AdaGCD applies adaptive slot attention to decompose DINO image features into semantic components and pools them with global features, reporting SOTA accuracy on six GCD benchmarks.

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

    cs.GR 2025-07 reject novelty 3.0 of 10

    A softmax and spatial attention fusion of SDXL base and refiner latents yields an ImageNet FID drop of about 1 point, but the result is not statistically supported.

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