# DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing

arXiv: https://arxiv.org/abs/2312.07409 · HTML: https://mlsanity.com/paper/2312.07409/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 0 of its 59 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Built on by (most cited first, 30 of 67)

- [Parameter-Efficient Fine-Tuning for Foundation Models](https://mlsanity.com/paper/2501.13787/) (2025)
- [AID: Attention Interpolation of Text-to-Image Diffusion](https://mlsanity.com/paper/2403.17924/) (2024)
- [Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing](https://mlsanity.com/paper/2510.08532/) (2025)
- [FreeMorph: Tuning-Free Generalized Image Morphing with Diffusion Model](https://mlsanity.com/paper/2507.01953/) (2025)
- [DreamMover: Leveraging the Prior of Diffusion Models for Image Interpolation with Large Motion](https://mlsanity.com/paper/2409.09605/) (2024)
- [TVG: A Training-free Transition Video Generation Method with Diffusion Models](https://mlsanity.com/paper/2408.13413/) (2024)
- [PERSE: Personalized 3D Generative Avatars from A Single Portrait](https://mlsanity.com/paper/2412.21206/) (2024)
- [Alias-Free Latent Diffusion Models: Improving Fractional Shift Equivariance of Diffusion Latent Space](https://mlsanity.com/paper/2503.09419/) (2025)
- [SRIF: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimation](https://mlsanity.com/paper/2409.11682/) (2024)
- [When Video Coding Meets Multimodal Large Language Models: A Unified Paradigm for Video Coding](https://mlsanity.com/paper/2408.08093/) (2024)
- [M3-CVC: Controllable Video Compression with Multimodal Generative Models](https://mlsanity.com/paper/2411.15798/) (2024)
- [Probability Density Geodesics in Image Diffusion Latent Space](https://mlsanity.com/paper/2504.06675/) (2025)
- [FreeBlend: Advancing Concept Blending with Staged Feedback-Driven Interpolation Diffusion](https://mlsanity.com/paper/2502.05606/) (2025)
- [An Efficient and Harmonized Framework for Balanced Cross-Domain Feature Integration](https://mlsanity.com/paper/2403.18461/) (2024)
- [Stable-SCore: A Stable Registration-based Framework for 3D Shape Correspondence](https://mlsanity.com/paper/2503.21766/) (2025)
- [SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning](https://mlsanity.com/paper/2504.05504/) (2025)
- [Versatile Transition Generation with Image-to-Video Diffusion](https://mlsanity.com/paper/2508.01698/) (2025)
- [GS-ProCams: Gaussian Splatting-based Projector-Camera Systems](https://mlsanity.com/paper/2412.11762/) (2024)
- [Textured 3D Regenerative Morphing with 3D Diffusion Prior](https://mlsanity.com/paper/2502.14316/) (2025)
- [What's Inside Your Diffusion Model? A Score-Based Riemannian Metric to Explore the Data Manifold](https://mlsanity.com/paper/2505.11128/) (2025)
- [Continuous Control of Editing Models via Adaptive-Origin Guidance](https://mlsanity.com/paper/2602.03826/) (2026)
- [Disentangling Disentangled Representations: Towards Improved Latent Units via Diffusion Models](https://mlsanity.com/paper/2410.23820/) (2024)
- [Image Interpolation with Score-based Riemannian Metrics of Diffusion Models](https://mlsanity.com/paper/2504.20288/) (2025)
- [Distribution-Conditional Generation: From Class Distribution to Creative Generation](https://mlsanity.com/paper/2505.03667/) (2025)
- [MorphAny3D: Unleashing the Power of Structured Latent in 3D Morphing](https://mlsanity.com/paper/2601.00204/) (2026)
- [Interp3D: Correspondence-aware Interpolation for Generative Textured 3D Morphing](https://mlsanity.com/paper/2601.14103/) (2026)
- [TwoSquared: 4D Generation from 2D Image Pairs](https://mlsanity.com/paper/2504.12825/) (2025)
- [Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment](https://mlsanity.com/paper/2506.01511/) (2025)
- [Be Tangential to Manifold: Discovering Riemannian Metric for Diffusion Models](https://mlsanity.com/paper/2510.05509/) (2025)
- [AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models](https://mlsanity.com/paper/2510.05715/) (2025)

## Abstract (arXiv)

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.