Source-linked AI summary
SINE: SINgle Image Editing with Text-to-Image Diffusion Models
Zhixing Zhang, Ligong Han, Arnab Ghosh, Dimitris Metaxas, Jian Ren
TL;DR
SINE addresses single-image editing, where fine-tuning diffusion models can overfit and fail to preserve the source while following new language guidance. It combines model-based classifier-free guidance with patch-based fine-tuning, and demonstrates editing with preserved details, arbitrary resolutions, and varied manipulations, while noting failures for confusing guidance and drastic changes.
Problem
Single-image editing is difficult because existing fine-tuning approaches can severely overfit and fail to preserve source content while adding language-guided features.
Method
SINE distills a single-image fine-tuned model into a pre-trained diffusion model through model-based classifier-free guidance and uses patch-based fine-tuning for arbitrary-resolution generation.
Results
SINE preserves image structure, background, and identity features while supporting style transfer, content addition, object manipulation, and arbitrary-resolution editing.
Takeaways & Limitations
The method enables a wide range of language-guided edits from one image, including generating a sculpture of the depicted girl at a different resolution while preserving identity features.
Takeaways & Limitations
SINE can produce unsatisfying results with confusing guidance or drastic changes, and improving fidelity by reducing fine-tuned-model overfitting remains future work.
Abstract
from arXiv · showhide
Recent works on diffusion models have demonstrated a strong capability for conditioning image generation, e.g., text-guided image synthesis. Such success inspires many efforts trying to use large-scale pre-trained diffusion models for tackling a challenging problem--real image editing. Works conducted in this area learn a unique textual token corresponding to several images containing the same object. However, under many circumstances, only one image is available, such as the painting of the Girl with a Pearl Earring. Using existing works on fine-tuning the pre-trained diffusion models with a single image causes severe overfitting issues. The information leakage from the pre-trained diffusion models makes editing can not keep the same content as the given image while creating new features depicted by the language guidance. This work aims to address the problem of single-image editing. We propose a novel model-based guidance built upon the classifier-free guidance so that the knowledge from the model trained on a single image can be distilled into the pre-trained diffusion model, enabling content creation even with one given image. Additionally, we propose a patch-based fine-tuning that can effectively help the model generate images of arbitrary resolution. We provide extensive experiments to validate the design choices of our approach and show promising editing capabilities, including changing style, content addition, and object manipulation. The code is available for research purposes at https://github.com/zhang-zx/SINE.git .
1. Introduction
SINE addresses single-image editing by combining model-based guidance with patch-based fine-tuning. The method preserves source content while enabling language-guided edits across styles, objects, contexts, and resolutions.
- SINE builds on pre-trained text-to-image models that already support diverse, natural-language-conditioned image generation and editing.
- Single-image editing remains challenging because fine-tuning large-scale diffusion models can overfit the only available image.
- SINE introduces model-based classifier-free guidance that uses a single-image fine-tuned model to guide content and structure during diffusion sampling.The fine-tuned model plants a content seed early, while the pre-trained model performs later language-guided editing.
- Patch-based fine-tuning decouples pixel position from content, enabling generation at arbitrary resolution.
- The method preserves image details while supporting style transfer, context addition, object manipulation, and resolution changes from one source image.For example, a 512 × 512 painting can produce a 640 × 512 sculpture image while preserving identity features.
2. Related Work
Related work uses pre-trained diffusion models either as generative priors for training-free editing or through fine-tuning for semantic personalization. SINE targets simultaneous image fidelity, text alignment, and arbitrary-resolution editing on one image.
- Training-free methods guide diffusion denoising with reference images, semantic maps, or blended representations.
- Fine-tuning methods such as DiffusionCLIP, Textual-Inversion, and DreamBooth support style transfer or personalized object synthesis using gradients, embeddings, or full-model updates.Textual-Inversion and DreamBooth typically use 3 ∼5 personalized images.
- SINE seeks simultaneous image fidelity and text alignment while addressing geometry artifacts that arise during arbitrary-resolution editing.Its patch-based fine-tuning method is designed to remove the position-content correlation that contributes to such artifacts.
3. Methods
SINE addresses single-image editing by combining model-based guidance with patch-based fine-tuning. The method uses the fine-tuned model to preserve source content while leveraging the pre-trained diffusion model for language-guided editing and arbitrary-resolution generation.
- Motivation: SINE fine-tunes a latent diffusion model on one image and its text description, but single-image fine-tuning can cause overfitting and language drift.The fine-tuned model may reconstruct the input while failing to synthesize diverse images under other language guidance.
- Model-Based Classifier-Free Guidance: Model-based classifier-free guidance combines scores from the fine-tuned and pre-trained diffusion models during selected sampling steps.The fine-tuned model supplies content and structure guidance through a linear combination with the pre-trained model’s score.
- Model-Based Classifier-Free Guidance: The sampling schedule uses model-based guidance for t > K and only the pre-trained model for t ≤ K, allowing later denoising to exploit its generalization ability.This design plants source-image content during early denoising while leaving subsequent steps to the pre-trained model and target language guidance.
- Patch-Based Fine-Tuning: Patch-based fine-tuning decouples pixel position from content by associating fixed-resolution latent codes with sub-areas of the single training image.The method randomly crops image patches and supplies positional embeddings for their corresponding areas.
- Patch-Based Fine-Tuning: At sampling time, the model receives whole-image positional information and a noisy latent whose dimensions match the desired output resolution.This enables arbitrary-resolution editing while preserving the original image’s structure.
4. Experiments
Experiments evaluate SINE across diverse single-image editing tasks, resolutions, baselines, and guidance settings. Results indicate broad editing capability, preserved source details, improved arbitrary-resolution behavior, and a fidelity–creativity trade-off controlled by guidance.
- Qualitative Evaluation: SINE edits diverse single images with prompts for style transfer, content addition, posture change, and breed change.Experiments use images from varied domains and generate four results per image and prompt, selecting the best result.
- Qualitative Evaluation: The method performs higher-resolution editing without duplicate artifacts, including when the output aspect ratio changes substantially.Patch-based fine-tuning enables the diffusion model to leverage the decoder’s super-resolution ability during testing.
- Comparisons: Compared with DreamBooth and Textual-Inversion, SINE preserves source-image fidelity and structural integrity while applying requested edits, including at higher resolution.The comparison reports dramatic structural changes from other methods in some cases, including failures on the Mona Lisa edit.
- Ablation Analysis: The interpolation value v controls fidelity and creativity, with v = 0.7 selected as a trade-off between them.Values near 1 reduce fidelity and edited features, whereas values near 0 increase artifacts because the fine-tuned model dominates after t > K.
- More Editing Tasks: SINE supports face manipulation, content removal, style generation, and style transfer using single-image guidance.Dual-model guidance combines content from one fine-tuned model with style from another for stylized generation.
- Ablation Analysis: Model-based classifier-free guidance succeeds where direct sampling from the fine-tuned model fails to apply the target property while maintaining high-fidelity synthesis.Direct sampling can reconstruct the source but has limited ability to follow alternative language guidance; model-based guidance addresses this during sampling.
- Ablation Analysis: Varying guidance step K controls the balance between source-image details and applied edits, while increasing K lowers image fidelity as the pretrained model contributes more.LPIPS measures fidelity to the original image, and CLIP measures alignment with the target text.
5. Conclusion
SINE combines single-image fine-tuning with model-based guidance to preserve fidelity while enabling complex, arbitrary-resolution edits. The method remains limited by confusing guidance, drastic transformations, and residual overfitting.
- 5. Conclusion: SINE fine-tunes a pre-trained diffusion model on one image, then uses the overfitted model to guide sampling with the pre-trained model.This preserves source fidelity while retaining the pre-trained model’s generalization ability.
- 5. Conclusion: The approach supports complex image editing beyond style transfer through improved geometric understanding of the source image.The paper describes this as enabling more complex manipulations of image content.
- 5. Conclusion: SINE can fail under confusing editing guidance, such as requesting a chair-shaped dog.The limitation is stated as a failure case for the diffusion model’s editing guidance.
- 5. Conclusion: Drastic transformations, such as changing a dog into a tiger in the same posture, can produce noticeable artifacts.The paper presents this as a limitation of large changes to the source content.
- 5. Conclusion: Future work should improve editing fidelity by alleviating overfitting in the fine-tuned model.The paper identifies this as a future direction.
Appendix
The appendix extends the main paper with additional comparisons, editing and manipulation results, ablations, and limitation discussions.
- Appendix: The appendix adds comparisons with existing single-real-image editing methods, further SINE editing results, ablation analyses, and limitation discussions.It specifically lists comparisons, novel manipulation tasks, design analyses, and possible future work.
A. More Comparisons
The appendix compares SINE with Prompt-to-Prompt and training-free methods, reporting stronger visual preservation and high-fidelity editing than those alternatives.
- A. More Comparisons: The comparisons include Prompt-to-Prompt, SDEdit, and ILVR alongside existing single-image editing methods.SDEdit denoises an image or semantic map with the desired output, while ILVR replaces low-frequency sample content with reference content.
- A. More Comparisons: SINE significantly outperforms the compared methods in visual comparisons for high-fidelity images while maximally preserving source-image details.The reported comparison is qualitative and focuses on fidelity and detail retention.
B. More Editing Results
Additional figures demonstrate SINE edits generated from 512 × 512 fine-tuning at higher resolutions using patch-based fine-tuning and model-based guidance.
- B. More Editing Results: Figures 13–18 show edits fine-tuned at 512 × 512 and sampled with model-based classifier-free guidance at higher resolutions such as 768 × 1024.The source image used for fine-tuning appears in the top-left corner of each result.
C. More Ablations
The ablations examine how guidance step K and weight v affect SINE’s editing behavior and report parameter settings selected for different images and goals.
- Guidance step K and weight v: Varying guidance step K and weight v reveals consistent behavior with Section 4.3 and allows image- and prompt-specific parameter tuning.The experiments use the same random seed and fixed resolution while varying K and v.
- Guidance step K and weight v: The default experimental setting is K = 400 and v = 0.7.
- Guidance step K and weight v: For stronger fidelity or editing, the authors adjust the parameters; one example uses v = 0.5 and K = 400.
D. Limitations
SINE has difficulty with confusing guidance, drastic edits, and precise localized changes. The paper also reports artifacts and altered details in some edited images.
- The method can change sweater colors in many cases, even when editing other image content.
- Background letters may become twisted after editing, indicating that source details are not always preserved exactly.
- Strictly editing only a specific image region remains an open direction despite the method's protection of source details.