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PostEdit: Posterior Sampling for Efficient Zero-Shot Image Editing
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Proposes PostEdit, an inversion-free, training-free zero-shot image editing method that replaces DDIM inversion with a single-step noising of the source latent and then refines the estimated clean latent via posterior-sampling-style Langevin optimization against a masked measurement of the source, using LCM-SD1.5.
Problem
Zero-shot text-guided image editing faces a trade-off among controllability, background preservation, and efficiency: inversion-based methods are slow and inversion-free methods lack background-consistency guarantees, with CFG's unconditional term causing accumulated errors.
Approach
Replace DDIM inversion with direct Gaussian noising of the source latent, then apply posterior-sampling-inspired Langevin optimization of the estimated clean latent toward a masked measurement of the source features plus a weighted injection of the source latent, while the LCM solver produces the target-prompt-conditioned estimate.
Key results
- On PIE-Bench editing, PostEdit reaches CLIP whole 26.76 and edited 24.14, the highest among compared methods, at about 1.5 s runtime.
- For reconstruction, PostEdit achieves PSNR 24.39 and SSIM 72.74 in about 1.5 s, versus NTI at about 120 s with PSNR 25.58.
- Best MSE among editing methods (3.24e-3), with PSNR 27.04 and LPIPS 6.38e-2; ablation shows removing posterior sampling drops PSNR to 21.31.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| remove posterior sampling optimization (ablation) | PostEdit · PIE-Bench image editing | PSNR: 27.04 -> 21.31 | Hurt |
| remove the masked measurement (ablation) | PostEdit · PIE-Bench image editing | CLIP Similarity Edited: 24.14 -> 20.94 | Hurt |
| add the initial latent z_in (ablation) | PostEdit · PIE-Bench image editing | PSNR: 24.01 -> 27.04 | Helped |
| set masked-feature probability f to 0.3 instead of the default 0.5 (ablation) | PostEdit · PIE-Bench image editing | PSNR: 27.04 -> 27.20 | Helped |
| set masked-feature probability f to 0.7 instead of the default 0.5 (ablation) | PostEdit · PIE-Bench image editing | PSNR: 27.04 -> 24.43 | Hurt |
| reduce optimization steps from 100 to 50 (ablation) | PostEdit · PIE-Bench image editing | CLIP Similarity Edited: 24.14 -> 23.59 | Hurt |
| increase optimization steps from 100 to 150 (ablation) | PostEdit · PIE-Bench image editing | Time: ~1.5s -> ~1.8s | Hurt |
| set weighting coefficient w to 0.3 instead of the default 0.1 (ablation) | PostEdit · PIE-Bench image editing | PSNR: 27.04 -> 27.00 | No clear effect |
| set weighting coefficient w to 0.5 instead of the default 0.1 (ablation) | PostEdit · PIE-Bench image editing | PSNR: 27.04 -> 27.75 | Helped |
| set weighting coefficient w to 0 instead of the default 0.1 (ablation) | PostEdit · PIE-Bench image editing | PSNR: 27.04 -> 24.01 | Hurt |
| use the alternate forward measurement instead of the measurement used by PostEdit (ablation) | PostEdit · PIE-Bench image reconstruction | PSNR: 24.39 -> 24.90 | Helped |
| use PostEdit instead of NTI | image reconstruction methods · PIE-Bench image reconstruction | Time: ~ 120s -> ~ 1.5s | Helped |
Surprising findings
- The paper reports that TurboEdit, built on SDXL-Turbo, can be slower than PostEdit built on LCM-SD1.5 in its own timing comparison, and that SDXL-Turbo is not faster than LCM-SD1.5 in the authors' measurements.
- Removing the mask in the measurement term keeps the highest background preservation but sharply lowers CLIP edited similarity (20.94), showing a trade-off between editing and reconstruction.
Limitations
- Struggles to represent highly specific scenes or fine-grained actions described in text, compared with structure-conditioned methods like ControlNet.
- Background consistency is limited and the authors state it requires improvement.
- Generation quality and speed depend strongly on the underlying baseline model (LCM-SD1.5).
Experimental setup
Datasets: PIE-Bench FFHQ
Benchmarks: PIE-Bench
Metrics: PSNR LPIPS MSE SSIM CLIP similarity (whole) CLIP similarity (edited) runtime
Baselines: NTI NPI iCD DDCM PnP DI TurboEdit SPD GR IP2P OmniGen SeedX
Builds on: DPS Latent Consistency Models DDIM Classifier-free guidance Null-text inversion
Closest papers by meaning
- PnP Inversion: Boosting Diffusion-based Editing with 3 Lines of Code (ICLR 2024 · similarity 0.88)
- High-Fidelity Diffusion-Based Image Editing (AAAI 2024 · similarity 0.88)
- Inversion-Free Image Editing with Language-Guided Diffusion Models (CVPR 2024 · similarity 0.88)
- Posterior Distillation Sampling (CVPR 2024 · similarity 0.88)
- Object-Aware Inversion and Reassembly for Image Editing (ICLR 2024 · similarity 0.87)
- Tuning-Free Inversion-Enhanced Control for Consistent Image Editing (AAAI 2024 · similarity 0.87)
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