Research map / Image generation / Editing and style transfer
PixelMan: Consistent Object Editing with Diffusion Models via Pixel Manipulation and Generation
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Proposes PixelMan, an inversion-free, training-free consistent object editing method that copies the source object in pixel space and iteratively harmonizes and inpaints with a three-branch sampler, leak-proof self-attention, and latent optimization, achieving better consistency in 16 steps.
Problem
Consistent object editing (repositioning, resizing, pasting) with diffusion models while preserving object and background consistency; existing methods rely on slow DDIM inversion (50 steps) or energy guidance that distorts latents.
Approach
Create a pixel-manipulated image with a duplicated source object at the target location, anchor the output latents to its VAE latents, and compute a delta edit direction from a three-branch inversion-free sampler (source, pixel-manipulated, target), with leak-proof self-attention to prevent inpainting from attending to the source and similar objects, and direct latent optimization instead of noise
Key results
- At 16 steps, PixelMan outperforms baselines (usually run at 50 steps) on 8 of 9 metrics on COCOEE, and at 50 steps it wins all 9 metrics.
- PixelMan at 16 steps uses 64 NFEs and 9 s latency versus 176 NFEs and 24 s for DiffEditor at 50 steps on COCOEE.
- Ablation: replacing DDIM inversion with the three-branch inversion-free sampler raises LPIPS-based object consistency from 0.041 to 0.015 at 16 steps.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use latents optimization instead of Energy Guidance (ablation) | PixelMan · COCOEE | NFEs: 70 -> 64 | Helped |
| replace DDIM inversion with three-branched inversion-free sampling (ablation) | PixelMan · COCOEE | TOPIQ: 0.565 -> 0.605 | Helped |
| add leak-proof self-attention (ablation) | PixelMan · COCOEE | CLIP-T2T: 0.891 -> 0.946 | Helped |
| add the pixel-manipulated branch (ablation) | PixelMan · COCOEE | object PSNR: 23.59 -> 35.62 | Helped |
| add K,V saving and injection from the source branch (ablation) | PixelMan · COCOEE | background LPIPS: 0.112 -> 0.074 | Helped |
| use PixelMan instead of DiffEditor | object repositioning · COCOEE | NFEs: 176 -> 64 | Helped |
| use PixelMan instead of DiffEditor | object repositioning · COCOEE | latency (seconds): 24 -> 9 | Helped |
Surprising findings
- Leak-proof self-attention mainly improves semantic consistency and object removal rather than the IQA or consistency metrics, and the no-saving K,V variant reaches fewer NFEs (48) with competitive IQA.
Limitations
- Relies on pixel-level copying and masks, so it does not handle occlusion cases, which were excluded from ReS evaluation.
- Evaluated only on object repositioning subsets of COCOEE and ReS with SD v1.5, and object-removal-style inpainting quality is judged visually.
Experimental setup
Datasets: COCOEE ReS
Benchmarks: COCOEE ReS
Metrics: TOPIQ MUSIQ LIQE LPIPS PSNR CLIP-T2T CLIP-I2I
Baselines: SelfGuidance DragonDiffusion DiffEditor SD2-Inpainting + AnyDoor
Builds on: DragonDiffusion DiffEditor Generative Semantic Nursing mutual self-attention DDIM inversion
Closest papers by meaning
- Inversion-Free Image Editing with Language-Guided Diffusion Models (CVPR 2024 · similarity 0.89)
- PixPerfect: Seamless Latent Diffusion Local Editing with Discriminative Pixel-Space Refinement (NeurIPS 2025 · similarity 0.89)
- PIXELS: Progressive Image Xemplar-based Editing with Latent Surgery (AAAI 2025 · similarity 0.89)
- Object-Aware Inversion and Reassembly for Image Editing (ICLR 2024 · similarity 0.88)
- Tuning-Free Inversion-Enhanced Control for Consistent Image Editing (AAAI 2024 · similarity 0.88)
- PAIR Diffusion: A Comprehensive Multimodal Object-Level Image Editor (CVPR 2024 · similarity 0.88)
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