Research map / Image generation / Editing and style transfer
An Edit Friendly DDPM Noise Space: Inversion and Manipulations
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Proposes an edit-friendly DDPM noise space, extracted by building auxiliary noisy images independently from the clean image, enabling exact inversion and structure-preserving text-guided and manipulation edits of real images.
Problem
DDPM inversion yields noise maps that are not edit-friendly: fixing native or CycleDiffusion-extracted noise while changing the text prompt or shifting the latent destroys image structure, and DDIM inversion is inaccurate and non-diverse for editing real images.
Approach
Construct auxiliary images directly from the clean image x0 with statistically independent noise (rather than via the Markov forward chain), so the extracted DDPM noise maps have higher variance and negative correlation across timesteps, which imprints image structure more strongly and makes them edit-friendly; the inversion requires no optimization.
Key results
- On the modified ImageNet-R-TI2I dataset, the method achieves a favorable LPIPS-CLIP trade-off relative to DDIM inversion, PnP, EDICT, null-text inversion, and CycleDiffusion, while editing in roughly tens of seconds versus over 2.5 minutes for PnP and null-text inversion.
- Integrated with Zero-Shot I2I translation, LPIPS improves from 0.35 to 0.27 at the same CLIP accuracy of 0.88 on the zebra/horse/cat/dog examples.
- Reconstruction is exact up to machine precision, and shifting the edit-friendly noise maps preserves image structure with only minor degradation, unlike the native DDPM noise space.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use our DDPM inversion with Zero-Shot | Zero-Shot · modified Zero-Shot I2IT | LPIPS: 0.35 -> 0.27 | Helped |
| Use our DDPM inversion with Zero-Shot | Zero-Shot · modified Zero-Shot I2IT | CLIP accuracy: 0.88 -> 0.88 | No clear effect |
| Integrate our DDPM inversion | Prompt-to-Prompt · modified ImageNet-R-TI2I | CLIP and LPIPS | Helped |
Surprising findings
- The native DDPM noise space, and the CycleDiffusion-extracted noise that follows its distribution, are not edit-friendly: fixing them while changing the prompt or shifting them destroys structure.
- The edit-friendly noise maps deliberately deviate from the standard normal distribution, with higher variance and negative correlation across consecutive timesteps.
Limitations
- Text-guided edits depend on hyperparameters (guidance strength and T_skip) that trade off fidelity against prompt adherence.
- Evaluated mainly on a small set of 48 images with 212 pairs and 60 pairs for the Zero-Shot comparison.
Experimental setup
Datasets: ImageNet-R-TI2I Zero-Shot I2IT modified dataset
Benchmarks: ImageNet-R-TI2I (modified) Zero-Shot I2IT (modified)
Metrics: LPIPS CLIP score editing time
Baselines: DDIM inversion PnP CycleDiffusion EDICT null-text inversion Prompt-to-Prompt Zero-Shot I2I SDEdit
Builds on: DDPM DDIM CycleDiffusion Prompt-to-Prompt Plug-and-Play Zero-Shot I2I translation
Closest papers by meaning
- Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for Image Editing (NeurIPS 2024 · similarity 0.91)
- Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation (ICML 2025 · similarity 0.91)
- Denoising Diffusion Bridge Models (ICLR 2024 · similarity 0.91)
- Noise Map Guidance: Inversion with Spatial Context for Real Image Editing (ICLR 2024 · similarity 0.90)
- Inversion-Free Image Editing with Language-Guided Diffusion Models (CVPR 2024 · similarity 0.90)
- UDPM: Upsampling Diffusion Probabilistic Models (NeurIPS 2024 · similarity 0.90)
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