Research map / Image generation / Editing and style transfer
High-Fidelity Diffusion-Based Image Editing
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Proposes a hypernetwork "rectifier" that modulates pretrained diffusion U-Net conv weights from residual features for high-fidelity reconstruction, plus a score-matching-like editing training that restarts from the original trajectory at each step to curb error accumulation.
Problem
Diffusion-based image editing fails to become faithful even with more inversion/denoising steps, because errors accumulate along the conditional Markovian editing trajectory, causing irrelevant attribute changes and detail loss.
Approach
A hypernetwork rectifier (ResNet34 global encoder with separable-convolution subnets) takes the original image and per-step x0 estimate to predict weight offsets for middle/up U-Net blocks, and editing is trained by re-anchoring each step on the original denoising trajectory (score-matching-style) with a CLIP directional loss plus L1 regularizer, rather than Markovian training from previous edits.
Key results
- On CelebA-HQ reconstruction at 50 steps, the rectifier lowers iDDPM's L1 from 0.090 to 0.085 and L2 from 0.016 to 0.014, with LPIPS unchanged at 0.150.
- Editing identity similarity for the 'man' attribute is 0.45±0.23 versus 0.33±0.14 for DiffusionCLIP and 0.22±0.16 for Asyrp.
- For 'Pixar' the identity similarity is 0.25±0.10 versus 0.22 for DiffusionCLIP and 0.18 for Asyrp.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the rectifier to iDDPM | iDDPM · CelebA-HQ reconstruction, 50 steps | L1: 0.090 -> 0.085 | Helped |
| Add the rectifier to iDDPM | iDDPM · CelebA-HQ reconstruction, 50 steps | LPIPS: 0.150 -> 0.150 | No clear effect |
| Add the rectifier to iDDPM | iDDPM · CelebA-HQ reconstruction, 50 steps | SSIM: 0.95 -> 0.94 | Hurt |
| Add the rectifier to DDPM++ | DDPM++ · LSUN-Church reconstruction, 20 steps | L1: 0.255 -> 0.254 | Helped |
| Add the rectifier to DDPM++ | DDPM++ · LSUN-Church reconstruction, 20 steps | SSIM: 0.48 -> 0.48 | No clear effect |
| Use the proposed editing method instead of Asyrp or DiffusionCLIP | Image editing · Man editing | identity similarity: 0.22$\pm$0.16 -> 0.45$\pm$0.23; 0.33$\pm$0.14 -> 0.45$\pm$0.23 | Helped |
| Use the proposed editing method instead of Asyrp or DiffusionCLIP | Image editing · Pixar editing | identity similarity: 0.18$\pm$0.13 -> 0.25$\pm$0.10; 0.22$\pm$0.13 -> 0.25$\pm$0.10 | Helped |
| Train editing from the original trajectory at each step rather than Markovian edited results (ablation) | Diffusion editing training · Smiling edits | visual fidelity | Helped |
| Extend DiffusionCLIP editing through the full denoising interval (ablation) | DiffusionCLIP · Smiling edits | visual fidelity | Hurt |
| Use L1 loss instead of noise-fitting loss for rectifier reconstruction training (ablation) | Rectifier reconstruction training · Image reconstruction, 50 steps | visual fidelity | Hurt |
| Add output delta-weight regularization to L1 loss (ablation) | Rectifier reconstruction training · Image reconstruction, 50 steps | restoration quality | Mixed |
| Use noise-fitting loss rather than L1 loss or L1 loss with delta-weight regularization (ablation) | Rectifier reconstruction training · Image reconstruction, 50 steps | visual fidelity | Helped |
Surprising findings
- Reconstruction gains from the rectifier are marginal in the reported table; on LSUN-Church (DDPM++) metrics are nearly identical to the baseline.
- Noise-fitting (epsilon) loss alone outperformed L1 and GAN-style losses for reconstruction fine-tuning.
Limitations
- Editing quality is evaluated mainly with identity similarity on only two attributes, with no human study.
- Some attributes such as 'angry' are hard to train, plausibly due to low frequency in CLIP pretraining text.
- Loss/learning-rate settings are sensitive and need per-prompt adjustment for severe attribute changes.
Experimental setup
Datasets: FFHQ CelebA-HQ AFHQ-dog METFACES LSUN-church LSUN-bedroom LAION-400M
Benchmarks: CelebA-HQ LSUN-Church AFHQ-Dogs METFACES
Metrics: L1 L2 LPIPS SSIM posterior mean gap identity similarity
Baselines: Asyrp DiffusionCLIP iDDPM GLIDE SDEdit
Builds on: DDPM DDIM DiffusionCLIP Asyrp SDEdit PDAE
Closest papers by meaning
- Inversion-Free Image Editing with Language-Guided Diffusion Models (CVPR 2024 · similarity 0.92)
- h-Edit: Effective and Flexible Diffusion-Based Editing via Doob's h-Transform (CVPR 2025 · similarity 0.91)
- DCI: Dual-Conditional Inversion for Boosting Diffusion-Based Image Editing (NeurIPS 2025 · similarity 0.91)
- Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising (NeurIPS 2024 · similarity 0.91)
- Denoising Diffusion Bridge Models (ICLR 2024 · similarity 0.90)
- PnP Inversion: Boosting Diffusion-based Editing with 3 Lines of Code (ICLR 2024 · similarity 0.90)
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