SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations (2108.01073v2)

Abstract: Guided image synthesis enables everyday users to create and edit photo-realistic images with minimum effort. The key challenge is balancing faithfulness to the user input (e.g., hand-drawn colored strokes) and realism of the synthesized image. Existing GAN-based methods attempt to achieve such balance using either conditional GANs or GAN inversions, which are challenging and often require additional training data or loss functions for individual applications. To address these issues, we introduce a new image synthesis and editing method, Stochastic Differential Editing (SDEdit), based on a diffusion model generative prior, which synthesizes realistic images by iteratively denoising through a stochastic differential equation (SDE). Given an input image with user guide of any type, SDEdit first adds noise to the input, then subsequently denoises the resulting image through the SDE prior to increase its realism. SDEdit does not require task-specific training or inversions and can naturally achieve the balance between realism and faithfulness. SDEdit significantly outperforms state-of-the-art GAN-based methods by up to 98.09% on realism and 91.72% on overall satisfaction scores, according to a human perception study, on multiple tasks, including stroke-based image synthesis and editing as well as image compositing.

Guided Image Synthesis and Editing with Stochastic Differential Equations (SDEdit)

The paper "SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations" by Chenlin Meng et al. introduces a novel image synthesis and editing method leveraging stochastic differential equations (SDEs). The method aims to strike a balance between the realism of synthesized images and their faithfulness to user inputs, without the need for task-specific model training or complex inversion processes typically required by GAN-based methods.

Key Contributions and Methodology

1. SDE-based Image Synthesis and Editing:

The authors propose a new framework for image synthesis and editing, termed Stochastic Differential Editing (SDEdit). This method leverages the stochastic processes inherent in SDEs to guide the image synthesis process. By using a generative SDE model, SDEdit follows a trajectory from a noisy input towards a realistic image through iterative denoising.

2. Realism-Faithfulness Trade-off:

A critical insight of SDEdit is the ability to balance realism and faithfulness to user guidance by adjusting the amount of noise added initially. The parameter $t_0$, which denotes the starting point of the denoising process, determines this balance. Higher values of $t_0$ lead to more realistic but less faithful images, while lower values retain more details from the user input but may sacrifice realism. This trade-off is effectively illustrated through experiments on various datasets.

3. Unified Framework Without Task-Specific Training:

Unlike conditional GANs that require retraining for new tasks or GAN inversion methods that need manually designed loss functions, SDEdit operates with a pretrained generative model. It does not rely on additional training data or specific losses for different tasks, making it versatile and adaptable to various image synthesis and editing applications.

4. Application Scope:

The paper demonstrates SDEdit's efficacy across multiple tasks, including stroke-based image synthesis, stroke-based image editing, and image compositing. These applications showcase SDEdit's ability to produce high-quality, realistic images faithfully reflecting the provided guides.

Numerical Results

SDEdit outperforms state-of-the-art GAN-based methods by a significant margin. For instance, in stroke-based image synthesis, SDEdit achieves up to 98.09% improvement in realism and 91.72% in user satisfaction scores. These results are robust across different datasets such as LSUN, CelebA-HQ, and ImageNet.

Moreover, for image compositing tasks on the CelebA-HQ dataset, SDEdit demonstrates superior performance with a marked improvement in both realism and faithfulness. Specifically, SDEdit yields a satisfaction score improvement of up to 83.73% over traditional blending methods and GAN-based approaches.

Implications and Future Directions

Practical Implications:

The practicality of SDEdit lies in its minimalistic approach to user input and lack of requirement for extensive task-specific training. Everyday users can input coarse, hand-drawn strokes or make simple image edits, and SDEdit will produce highly realistic outputs. This democratizes image synthesis and editing, lowering the entry barriers for non-experts.

Theoretical Implications:

On the theoretical front, SDEdit's approach leveraging generative SDEs signals a shift from traditional GAN-based methods to more flexible models that exploit inherent stochastic processes. The ability to traverse the noise-input space and generate plausible images opens new avenues in the understanding and application of score-based generative models (SBGMs).

Future Developments:

Future research could explore the enhancement of the SDEdit framework by integrating additional conditions, such as semantic masks or language descriptions, further improving control over the synthesis process. Enhancing the speed of the denoising process and reducing computational costs will also be pivotal for real-time applications.

Additionally, extending SDEdit to video synthesis and editing could significantly impact media production, gaming, and virtual reality.

Conclusion

SDEdit presents a robust and flexible method for guided image synthesis and editing, leveraging the inherent strengths of stochastic differential equations. Its capacity to balance realism and faithfulness without the need for task-specific retraining or complex inversion processes makes it a valuable tool for both researchers and practitioners in the field of computer vision and machine learning. As generative models continue to evolve, SDEdit's approach highlights the potential for more intuitive and accessible content creation methodologies.