Research map / Image generation / Editing and style transfer
StyleSSP: Sampling StartPoint Enhancement for Training-free Diffusion-based Method for Style Transfer
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Proposes StyleSSP, a training-free diffusion style transfer method that improves the DDIM sampling startpoint via low-frequency reduction of the content latent and negative guidance (using IP-Instruct embeddings) during inversion.
Problem
Training-free diffusion style transfer suffers from layout changes of the content image and content leakage from the style image.
Approach
Treat the DDIM sampling startpoint as the key lever: reduce low-frequency components of the inverted content latent to preserve layout, and apply negative guidance during DDIM inversion (with IP-Instruct style/content embeddings) to push the startpoint away from style-image content.
Key results
- StyleSSP reaches ArtFID 21.499, FID 13.448, LPIPS 0.4881, beating the best baseline StyleID (ArtFID 28.801, FID 18.131, LPIPS 0.5055) on the 800-image comparison.
- Ablation: frequency manipulation alone lowers ArtFID from 26.683 to 24.112; negative guidance alone barely changes it (26.542), and the full method reaches 21.499.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use StyleSSP instead of StyleID | Style transfer method · Style transfer dataset (800 stylized images) | ArtFID: 28.801 -> 21.499 | Helped |
| use StyleSSP instead of StyleID | Style transfer method · Style transfer dataset (800 stylized images) | FID: 18.131 -> 13.448 | Helped |
| use StyleSSP instead of StyleID | Style transfer method · Style transfer dataset (800 stylized images) | LPIPS: 0.5055 -> 0.4881 | Helped |
| add frequency manipulation (ablation) | StyleSSP baseline without frequency manipulation or negative guidance via inversion · Style transfer dataset (800 stylized images) | ArtFID: 26.683 -> 24.112 | Helped |
| add frequency manipulation (ablation) | StyleSSP baseline without frequency manipulation or negative guidance via inversion · Style transfer dataset (800 stylized images) | FID: 16.205 -> 15.103 | Helped |
| add frequency manipulation (ablation) | StyleSSP baseline without frequency manipulation or negative guidance via inversion · Style transfer dataset (800 stylized images) | LPIPS: 0.5509 -> 0.4973 | Helped |
| add negative guidance via inversion (ablation) | StyleSSP baseline without frequency manipulation or negative guidance via inversion · Style transfer dataset (800 stylized images) | ArtFID: 26.683 -> 26.542 | Helped |
| add negative guidance via inversion (ablation) | StyleSSP baseline without frequency manipulation or negative guidance via inversion · Style transfer dataset (800 stylized images) | FID: 16.205 -> 16.128 | Helped |
| add negative guidance via inversion (ablation) | StyleSSP baseline without frequency manipulation or negative guidance via inversion · Style transfer dataset (800 stylized images) | LPIPS: 0.5509 -> 0.5496 | Helped |
Surprising findings
- Negative guidance applied during sampling (or standard negative prompt guidance) gives little or no improvement over baseline; the gain comes from applying it during inversion.
Limitations
- Relies on text prompts from BLIP and on the IP-Instruct extractor, whose availability is unclear.
- Evaluation is limited to quantitative metrics on a single 800-image set; no human study reported.
- Content leakage control depends on tuning the negative guidance scale and frequency ratio alpha by user preference.
Experimental setup
Datasets: MS-COCO WikiArt
Benchmarks: MS-COCO content / WikiArt style pairs (800 generated stylized images)
Metrics: ArtFID FID LPIPS
Baselines: StyleID StyleAlign InstantStyle plus InstantStyle DiffuseIT InST DiffStyle
Builds on: DDIM inversion InstantStyle StyleID FlexiEdit ControlNet
Closest papers by meaning
- CLeaR: A Unified Framework for Resolving the Leakage–Degradation Dilemma in Style Transfer (NeurIPS 2026 · similarity 0.91)
- Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer (CVPR 2024 · similarity 0.90)
- SigStyle: Signature Style Transfer via Personalized Text-to-Image Models (AAAI 2025 · similarity 0.89)
- Z*: Zero-shot Style Transfer via Attention Reweighting (CVPR 2024 · similarity 0.89)
- OmniStyle: Filtering High Quality Style Transfer Data at Scale (CVPR 2025 · similarity 0.89)
- Less is More: Masking Elements in Image Condition Features Avoids Content Leakages in Style Transfer Diffusion Models (ICLR 2025 · similarity 0.89)
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