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Lightning-Fast Image Inversion and Editing for Text-to-Image Diffusion Models
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Proposes Guided Newton-Raphson Inversion (GNRI), a training-free deterministic diffusion inversion method that solves a norm-reduced root-finding problem with a Gaussian likelihood guidance term, achieving fast and accurate reconstruction and editing.
Problem
Deterministic inversion of text-to-image diffusion and flow-matching models: finding a noise latent that reproduces a given image under its prompt, accurately and quickly, especially for few-step models where DDIM inversion's approximation error is large.
Approach
Frames inversion as root-finding on an implicit equation, reduces the residual to a scalar L1-norm objective so the Newton-Raphson update needs only a per-component diagonal approximation, and adds a guidance term (negative log-likelihood of the forward Gaussian step) that steers iterations toward in-distribution noise latents.
Key results
- On SDXL-Turbo, GNRI inverts an image in 0.521 s versus 32.3 s for EditFriendly, and reaches 23.9 PSNR in 6 NFEs versus 22 for TurboEdit (8 NFEs) and 18.1 for ReNoise (36 NFEs).
- On PIE-Bench with SDXL-Turbo, GNRI achieves PSNR 30.22, LPIPS 40.15 (x10^3), and CLIP whole/edited 26.15/23.26, the best among compared methods.
- In a human study over 100 images, GNRI was preferred 71.6% of the time versus 15.9% for ReNoise and 12.5% for ExactDPM.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the guidance prior to non-guided Newton-Raphson inversion (ablation) | NRI · SDXL-Turbo, 5,000 COCO images | reconstruction PSNR | Helped |
| Omit the guidance regularizer (λ = 0) (ablation) | GNRI · Image reconstruction | PSNR | Hurt |
| Set the guidance weight to λ = 0.1 (ablation) | GNRI · Image reconstruction | PSNR | Helped |
| Increase the guidance weight beyond λ = 0.1 (ablation) | GNRI · Image reconstruction | PSNR | Hurt |
| Use GNRI inversion instead of ReNoise | Seed interpolation · Seed interpolation | ACC: 52.51 -> 54.98 | Helped |
| Use GNRI inversion instead of ReNoise | Seed centroid generation · Seed centroid | FID: 5.22 -> 4.59 | Helped |
| Use GNRI inversion instead of ReNoise | Rare concept generation · ImageNet1k in LAION2B, Tail | accuracy: 85.3 -> 89.1 | Helped |
| Use GNRI inversion instead of TurboEdit | Image editing · PIE-Bench, SDXL-turbo | PSNR: 29.52 -> 30.22 | Helped |
| Use GNRI inversion instead of DDIM | Image editing · PIE-Bench, SDXL-turbo | inverse time: 0.454 -> 0.521 | Hurt |
| Use the guidance regularizer instead of Anderson Acceleration | Newton-Raphson inversion · Out-of-distribution COCO samples | PSNR: 18.3 -> 25.8 | Helped |
| Use GNRI instead of EditFriendly | Image inversion · SDXL Turbo, COCO validation | PSNR: 26.1 -> 23.9 | Hurt |
Surprising findings
- Vanilla (unguided) Newton-Raphson inversion converges quickly but to out-of-distribution latents with poor reconstruction, so the guidance term is essential.
- Edit-Friendly DDPM inversion achieves higher PSNR (26.1) than GNRI (23.9) on SDXL-Turbo, though at over 30 seconds inversion time.
Limitations
- Fails to converge in about 4.6% of COCO cases, which the authors attribute to incorrect or mismatched captions.
- Convergence theory for the non-zero-minimum case is not established; the contraction property is shown only empirically.
- Deterministic inversion yields lower PSNR than stochastic DDPM-based inversion (EditFriendly).
Experimental setup
Datasets: MS-COCO 2017 validation PIE-Bench ImageNet1k LAION2B
Benchmarks: MS-COCO 2017 validation PIE-Bench
Metrics: PSNR LPIPS MSE SSIM CLIP similarity inversion time FID classifier accuracy human preference
Baselines: DDIM inversion SDEdit Null-text inversion EDICT AIDI ReNoise ExactDPM Wu2024Turbo EditFriendly Gal2024Turbo TurboEdit DirectInv
Builds on: DDIM inversion Prompt-to-Prompt ReNoise ExactDPM Null-text inversion
Closest papers by meaning
- PnP Inversion: Boosting Diffusion-based Editing with 3 Lines of Code (ICLR 2024 · similarity 0.91)
- Inversion-Free Image Editing with Language-Guided Diffusion Models (CVPR 2024 · similarity 0.90)
- Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations (ICLR 2025 · similarity 0.90)
- Prompt-tuning Latent Diffusion Models for Inverse Problems (ICML 2024 · similarity 0.89)
- Exploring Fixed Point in Image Editing: Theoretical Support and Convergence Optimization (NeurIPS 2024 · similarity 0.89)
- DCI: Dual-Conditional Inversion for Boosting Diffusion-Based Image Editing (NeurIPS 2025 · similarity 0.89)
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