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DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing (2312.07409v1)

Published 12 Dec 2023 in cs.CV

Abstract: Diffusion models have achieved remarkable image generation quality surpassing previous generative models. However, a notable limitation of diffusion models, in comparison to GANs, is their difficulty in smoothly interpolating between two image samples, due to their highly unstructured latent space. Such a smooth interpolation is intriguing as it naturally serves as a solution for the image morphing task with many applications. In this work, we present DiffMorpher, the first approach enabling smooth and natural image interpolation using diffusion models. Our key idea is to capture the semantics of the two images by fitting two LoRAs to them respectively, and interpolate between both the LoRA parameters and the latent noises to ensure a smooth semantic transition, where correspondence automatically emerges without the need for annotation. In addition, we propose an attention interpolation and injection technique and a new sampling schedule to further enhance the smoothness between consecutive images. Extensive experiments demonstrate that DiffMorpher achieves starkly better image morphing effects than previous methods across a variety of object categories, bridging a critical functional gap that distinguished diffusion models from GANs.

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Authors (5)
  1. Kaiwen Zhang (23 papers)
  2. Yifan Zhou (158 papers)
  3. Xudong Xu (20 papers)
  4. Xingang Pan (45 papers)
  5. Bo Dai (245 papers)
Citations (13)
View on Semantic Scholar

Summary

An Analysis of DiffMorpher: Enhancing Diffusion Models for Image Morphing

The paper under review introduces DiffMorpher, an innovative technique designed to address the challenge of achieving smooth and natural image interpolation using diffusion models. This task, usually associated with Generative Adversarial Networks (GANs), has posed difficulties for diffusion models due to their less structured latent space. The novel approach put forth in this paper demonstrates significant advancements over prior efforts by leveraging the capabilities of pre-trained diffusion models without the need for annotation, thus providing a compelling alternative to GAN-related methods.

Core Contributions and Techniques

One of the central contributions of this work lies in the introduction of the DiffMorpher method, which adeptly utilizes Low-Rank Adaptations (LoRAs) to capture image semantics. By fitting two LoRA parameter sets to the input images and interpolating between these parameters, the authors facilitate a smooth transition between image semantics. This interweaving of LoRA parameters marks a substantial progression in the capacity of diffusion models to manage meaningful semantic transformations, owing to the analogous parameter structures that permit linear interpolation, offering a robust mechanism to blend semantic content from two distinct images.

To further enhance the transitional smoothness, the paper introduces several noteworthy techniques:

  1. Attention Interpolation and Injection: By employing self-attention interpolation and substitution, the paper effectively tackles abrupt changes in low-level textures during the morphing process. This method enhances the consistency and smoothness of transitions by enabling the system to query correlated textures and structures from both input images in early denoising steps.
  2. Adaptive Instance Normalization (AdaIN) Adjustment: This adjustment ensures color and brightness coherence throughout the interpolation, thereby contributing to more visually uniform image transitions.
  3. Reschedule Sampling: The inclusion of a new sampling schedule helps maintain a homogenous transition rate in image semantics, addressing potential issues of uneven content change rates in the generated image sequences.

These complementary techniques showcase a comprehensive approach to overcoming the limitations previously faced by diffusion models in the image morphing domain.

Empirical Validation

The authors performed extensive evaluations to validate the effectiveness of their approach. Quantitative results from experiments conducted using the MorphBench dataset—a newly proposed benchmark specifically for image morphing—highlight the superior performance of DiffMorpher. The metrics used include Frechet Inception Distance (FID), Perceptual Path Length (PPL), and Perceptual Distance Variance (PDV), which collectively assess the fidelity, smoothness, and speed homogeneity of transition sequences. DiffMorpher achieves lower scores across these metrics compared to classical graphical and other deep learning-based methods, translating to more consistent and high-quality intermediary results.

Implications and Future Prospects

The implications of DiffMorpher extend beyond the immediate improvements in image morphing. By solving a longstanding issue in diffusion models, this work broadens the potential applications of these generative models in artistic fields, animations, and even data augmentation areas, where smooth transitions and consistency are paramount. Furthermore, the combination of LoRA-based semantic handling with diffusion models lays the groundwork for future advancements and adaptations in generative modeling tasks.

Looking ahead, future research may explore refining the LoRA fitting process to enhance efficiency or further diversify the model’s applicability to morphing tasks involving more complex or less evidently correlated image pairs. Moreover, the potential integration of DiffMorpher’s innovations into interactive image editing frameworks might streamline user experiences and expand the scope of creative digital applications.

Conclusion

DiffMorpher represents a sound advancement in unleashing the potential of diffusion models for the task of image morphing. By addressing the fundamental challenges of semantic complexity and transition smoothness, this approach not only outperforms existing methodologies but also suggests new directions for research and application in the evolving landscape of AI-driven visual content generation.

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