Awesome-Diffusion-Model-Based-Image-Editing-Methods
Diffusion Model-Based Image Editing: A Survey (TPAMI 2025)
https://github.com/SiatMMLab/Awesome-Diffusion-Model-Based-Image-Editing-Methods
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Training-Based
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Training-Based: Reference and Attribute Guidance via Self-Supervision
- PAIR-Diffusion: A Comprehensive Multimodal Object-Level Image Editor
- DreamInpainter: Text-Guided Subject-Driven Image Inpainting with Diffusion Models
- Uni-paint: A Unified Framework for Multimodal Image Inpainting with Pretrained Diffusion Model
- Face Aging via Diffusion-based Editing
- Anydoor: Zero-shot object-level image customization
- Paste, Inpaint and Harmonize via Denoising: Subject-Driven Image Editing with Pre-Trained Diffusion Model
- Text-to-image editing by image information removal
- Reference-based Image Composition with Sketch via Structure-aware Diffusion Model
- Imagen editor and editbench: Advancing and evaluating text-guided image inpainting
- Smartbrush: Text and shape guided object inpainting with diffusion model
- ObjectStitch: Object Compositing With Diffusion Model
- Paint by example: Exemplar-based image editing with diffusion models
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Training-Based: Instructional Editing via Full Supervision
- InstructGIE: Towards Generalizable Image Editing
- MoEController: Instruction-based Arbitrary Image Manipulation with Mixture-of-Expert Controllers
- ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based Image Manipulation
- Inst-Inpaint: Instructing to Remove Objects with Diffusion Models
- HIVE: Harnessing Human Feedback for Instructional Visual Editing
- DialogPaint: A Dialog-based Image Editing Model
- Learning to Follow Object-Centric Image Editing Instructions Faithfully
- Instructpix2pix: Learning to follow image editing instructions
- SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models
- InstructAny2Pix: Flexible Visual Editing via Multimodal Instruction Following
- Focus on Your Instruction: Fine-grained and Multi-instruction Image Editing by Attention Modulation
- Emu edit: Precise image editing via recognition and generation tasks
- Guiding instruction-based image editing via multimodal large language models
- Instructdiffusion: A generalist modeling interface for vision tasks
- EditWorld: Simulating World Dynamics for Instruction-Following Image Editing
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Training-Based: Domain-Specific Editing
- Fine-grained Image Editing by Pixel-wise Guidance Using Diffusion Models
- Diffusion Models Already Have A Semantic Latent Space
- Unit-ddpm: Unpaired image translation with denoising diffusion probabilistic models
- Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations
- TexFit: Text-Driven Fashion Image Editing with Diffusion Models
- CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image Manipulation
- Stylediffusion: Controllable disentangled style transfer via diffusion models
- Hierarchical diffusion autoencoders and disentangled image manipulation
- Towards Real-time Text-driven Image Manipulation with Unconditional Diffusion Models
- Diffstyler: Controllable dual diffusion for text-driven image stylization
- Diffusion autoencoders: Toward a meaningful and decodable representation
- Diffusionclip: Text-guided diffusion models for robust image manipulation
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Training-Based: Pseudo-Target Retrieval with Weak Supervision
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Training-Based: Domain-Specific Editing with Weak Supervision
- Unit-ddpm: Unpaired image translation with denoising diffusion probabilistic models
- Fine-grained Image Editing by Pixel-wise Guidance Using Diffusion Models
- Diffusion Models Already Have A Semantic Latent Space
- Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations
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Training-Based: Reference and Attribute Guided Editing
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Testing-Time Finetuning
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Testing-Time Finetuning: Denosing Model Finetuning
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Custom-edit: Text-guided image editing with customized diffusion models
- Unitune: Text-driven image editing by fine tuning an image generation model on a single image
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
- Kv inversion: Kv embeddings learning for text-conditioned real image action editing
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Testing-Time Finetuning: Latent Variable Optimization
- Directed Diffusion: Direct Control of Object Placement through Attention Guidance
- StableDrag: Stable Dragging for Point-based Image Editing
- FreeDrag: Feature Dragging for Reliable Point-based Image Editing
- Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing
- MagicRemover: Tuning-free Text-guided Image inpainting with Diffusion Models
- Dragondiffusion: Enabling drag-style manipulation on diffusion models
- DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing
- Delta denoising score
- Diffusion-based Image Translation using disentangled style and content representation
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Testing-Time Finetuning: Embeddings Finetuning
- Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing
- Prompt Tuning Inversion for Text-Driven Image Editing Using Diffusion Models
- Uncovering the Disentanglement Capability in Text-to-Image Diffusion Models
- Null-text inversion for editing real images using guided diffusion models
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Testing-Time Finetuning: Guidance with Hypernetworks
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Testing-Time Finetuning: Hybrid Finetuning
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Training and Finetuning Free
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Training and Finetuning Free: Inversion/Sampling Modification
- An Edit Friendly DDPM Noise Space: Inversion and Manipulations
- Null-text guidance in diffusion models is secretly a cartoon-style creator
- Inversion-Free Image Editing with Natural Language
- Fixed-point Inversion for Text-to-image diffusion models
- Tuning-Free Inversion-Enhanced Control for Consistent Image Editing
- The Blessing of Randomness: SDE Beats ODE in General Diffusion-based Image Editing
- LEDITS++: Limitless Image Editing using Text-to-Image Models
- A latent space of stochastic diffusion models for zero-shot image editing and guidance
- Effective real image editing with accelerated iterative diffusion inversion
- Fec: Three finetuning-free methods to enhance consistency for real image editing
- Iterative multi-granular image editing using diffusion models
- ProxEdit: Improving Tuning-Free Real Image Editing With Proximal Guidance
- Diffusion self-guidance for controllable image generation
- Diffusion Brush: A Latent Diffusion Model-based Editing Tool for AI-generated Images
- Negative-prompt Inversion: Fast Image Inversion for Editing with Text-guided Diffusion Models
- Training-Free Content Injection Using H-Space in Diffusion Models
- Edict: Exact diffusion inversion via coupled transformations
- Direct inversion: Optimization-free text-driven real image editing with diffusion models
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Training and Finetuning Free: Attention Modification
- Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing
- Conditional Score Guidance for Text-Driven Image-to-Image Translation
- HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models
- Tf-icon: Diffusion-based training-free cross-domain image composition
- MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing
- Localizing Object-level Shape Variations with Text-to-Image Diffusion Models
- Shape-Guided Diffusion With Inside-Outside Attention
- Plug-and-play diffusion features for text-driven image-to-image translation
- Prompt-to-prompt image editing with cross attention control
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Training and Finetuning Free: Mask Guidance
- Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models
- ZONE: Zero-Shot Instruction-Guided Local Editing
- Watch your steps: Local image and scene editing by text instructions
- Differential Diffusion: Giving Each Pixel Its Strength
- PFB-Diff: Progressive Feature Blending Diffusion for Text-driven Image Editing
- FISEdit: Accelerating Text-to-image Editing via Cache-enabled Sparse Diffusion Inference
- Inpaint anything: Segment anything meets image inpainting
- Region-aware diffusion for zero-shot text-driven image editing
- Text-guided mask-free local image retouching
- Blended diffusion for text-driven editing of natural images
- DiffEdit: Diffusion-based semantic image editing with mask guidance
- Blended latent diffusion
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Training and Finetuning Free: Input Text Refinement
- User-friendly Image Editing with Minimal Text Input: Leveraging Captioning and Injection Techniques
- ReGeneration Learning of Diffusion Models with Rich Prompts for Zero-Shot Image Translation
- InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User Instructions
- Preditor: Text guided image editing with diffusion prior
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Training and Finetuning Free: Multi-Noise Redirection
- Object-aware Inversion and Reassembly for Image Editing
- Ledits: Real image editing with ddpm inversion and semantic guidance
- Sega: Instructing diffusion using semantic dimensions
- The stable artist: Steering semantics in diffusion latent space
- Unsplash - 4V. This benchmark covers seven most popular specific editing tasks across semantic, stylistic and structural editing defined in our paper: *object addition*, *object replacement*, *object removal*, *background change*, *overall style change*, *texture change*, and *action change*. Click [here](EditEval_v1/Dataset) to download this dataset!
- Star History Chart
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Programming Languages
Sub Categories
Testing-Time Finetuning: Denosing Model Finetuning
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Training and Finetuning Free: Inversion/Sampling Modification
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Training-Based: Instructional Editing via Full Supervision
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Training and Finetuning Free: Mask Guidance
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Training-Based: Domain-Specific Editing
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Training-Based: Reference and Attribute Guidance via Self-Supervision
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Training and Finetuning Free: Attention Modification
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Testing-Time Finetuning: Latent Variable Optimization
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Training and Finetuning Free: Multi-Noise Redirection
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Testing-Time Finetuning: Hybrid Finetuning
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Testing-Time Finetuning: Embeddings Finetuning
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Training-Based: Domain-Specific Editing with Weak Supervision
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Training and Finetuning Free: Input Text Refinement
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Training-Based: Pseudo-Target Retrieval with Weak Supervision
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Training-Based: Reference and Attribute Guided Editing
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Testing-Time Finetuning: Guidance with Hypernetworks
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