Differential Diffusion: Giving Each Pixel Its Strength (2306.00950v2)

Abstract: Diffusion models have revolutionized image generation and editing, producing state-of-the-art results in conditioned and unconditioned image synthesis. While current techniques enable user control over the degree of change in an image edit, the controllability is limited to global changes over an entire edited region. This paper introduces a novel framework that enables customization of the amount of change per pixel or per image region. Our framework can be integrated into any existing diffusion model, enhancing it with this capability. Such granular control on the quantity of change opens up a diverse array of new editing capabilities, such as control of the extent to which individual objects are modified, or the ability to introduce gradual spatial changes. Furthermore, we showcase the framework's effectiveness in soft-inpainting -- the completion of portions of an image while subtly adjusting the surrounding areas to ensure seamless integration. Additionally, we introduce a new tool for exploring the effects of different change quantities. Our framework operates solely during inference, requiring no model training or fine-tuning. We demonstrate our method with the current open state-of-the-art models, and validate it via both quantitative and qualitative comparisons, and a user study. Our code is available at: https://github.com/exx8/differential-diffusion

Differential Diffusion: Giving Each Pixel Its Strength

The paper presents a novel framework for diffusion models that enables per-pixel control over the amount of change during the image-to-image translation process. This granular control is a significant extension of traditional diffusion models, which typically apply uniform strength across entire edited regions. The framework does not require any model training or fine-tuning, functioning solely at inference time, which makes it distinctively efficient and versatile.

Contributions

The major contributions of this research include:

1. Introduction of Change Maps: The paper introduces the concept of change maps, which generalize the traditional binary mask concept in image editing. Change maps allow for varying degrees of change at different regions within the same image, controlled by a matrix of values rather than a single scalar.
2. Efficient Inference Process: The research outlines an optimized inference process for applying these change maps, ensuring that the model remains efficient in terms of memory usage and computation. This involves selectively modifying various regions at different timesteps during the diffusion's inference process.
3. Applications: Several applications of this method are demonstrated, including:
   • Multi-Strength Editing: Allows fine-grained control over impact areas in an image, facilitating detailed and nuanced edits.
   • Soft-Inpainting: Extends traditional inpainting methods with gradual transitions for smoother blending.
   • Strength Fan Tool: Provides a visualization method that helps users explore various strengths simultaneously, aiding in the intuitive tuning of parameters.
4. Quantitative Evaluation Metrics: The paper establishes the first metrics to quantify adherence to change maps, namely Correlation Adherence Metric (CAM) and Distance Adherence Metric (DAM). These metrics evaluate the spatial accuracy of applied change maps against the intended change maps.

Experimental Evaluation

Comparative Analysis

The research provides a comparative analysis against existing methods such as InstructPix2Pix and DiffEdit for text-guided editing, as well as diffusion models like Stable Diffusion 2 and Blended Latent Diffusion for mask-based editing. The proposed method demonstrates superior adherence to change maps and improved visual quality, validated through both quantitative measures and user studies.

Performance Metrics

The CAM and DAM metrics are used to assess the performance of the proposed method against competitors:

• CAM evaluates high-level similarity based on Pearson correlation coefficients.
• DAM considers low-level feature similarities using Frobenius norms.

The proposed framework outperforms alternatives on both metrics, indicating higher fidelity in applying spatial changes according to the change maps.

User Study

A user paper validates the intuitive and perceptible impact of the framework. Participants consistently identified the correct change maps and preferred the visual outcomes generated by the proposed method, both in terms of map adherence and visual quality, over the other methods tested.

Implications and Future Work

Theoretical Implications

The introduction of change maps and the corresponding inference algorithm significantly extend the capabilities of diffusion models. This development opens up new theoretical avenues for image synthesis and editing, particularly in the context of fine-grained control over spatial changes. It also challenges existing paradigms of uniform edit application, paving the way for more nuanced and controllable editing methodologies.

Practical Applications

From a practical perspective, this framework has far-reaching implications for industries relying heavily on image processing, such as digital art, augmented reality, and virtual reality. The ability to control the extent of change at a per-pixel level enables artists and designers to achieve a higher degree of customization and precision in their work.

Future Directions

Several future research directions can be pursued based on the findings of this paper:

1. Optimization: Further optimization of the inference algorithm could be explored, potentially through parallel computations or more efficient data structures.
2. Automated Change Map Generation: Research into algorithms for automatically generating change maps based on image content and desired outcome could make the framework even more user-friendly.
3. Enhanced Visualization Tools: Building on the strength fan, other intuitive tools for visualizing and adjusting parameters could be developed to further improve user interaction.
4. Broader Model Compatibility: Extending the framework's compatibility to a wider range of diffusion models and other generative architectures could broaden its applicability and robustness.

In conclusion, this research presents a significant enhancement in the field of image editing with diffusion models. By enabling per-pixel control over change strength and demonstrating various applications, it sets the stage for future advancements in precise and customizable image synthesis.