FreeMorph: Tuning-Free Generalized Image Morphing with Diffusion Model

2025-07-02

AI Summary

Existing image deformation methods face two key bottlenecks: fine-tuning-based approaches suffer from low efficiency and poor generalization, while tuning-free methods struggle to simultaneously preserve fidelity and semantic consistency due to the nonlinearity of diffusion processes and inherent model biases. This paper introduces the first tuning-free universal diffusion-based deformation framework. Its core innovations are: (1) guidance-aware spherical interpolation in the latent space to maintain identity continuity; and (2) a generation-step-aware, multi-stage attention fusion mechanism enabling bidirectional semantic alignment and smooth transition between source and target. The method operates entirely on pretrained diffusion models, requiring modifications only to the self-attention modules. Experiments demonstrate state-of-the-art performance across diverse semantic and layout transfer tasks, with significantly improved generation quality over baselines, 10–50× faster inference, and superior efficiency, stability, and fidelity.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

We present FreeMorph, the first tuning-free method for image morphing that accommodates inputs with different semantics or layouts. Unlike existing methods that rely on finetuning pre-trained diffusion models and are limited by time constraints and semantic/layout discrepancies, FreeMorph delivers high-fidelity image morphing without requiring per-instance training. Despite their efficiency and potential, tuning-free methods face challenges in maintaining high-quality results due to the non-linear nature of the multi-step denoising process and biases inherited from the pre-trained diffusion model. In this paper, we introduce FreeMorph to address these challenges by integrating two key innovations. 1) We first propose a guidance-aware spherical interpolation design that incorporates explicit guidance from the input images by modifying the self-attention modules, thereby addressing identity loss and ensuring directional transitions throughout the generated sequence. 2) We further introduce a step-oriented variation trend that blends self-attention modules derived from each input image to achieve controlled and consistent transitions that respect both inputs. Our extensive evaluations demonstrate that FreeMorph outperforms existing methods, being 10x ~ 50x faster and establishing a new state-of-the-art for image morphing.

Problem

• Tuning-free image morphing for diverse semantics/layouts
• Overcoming non-linear denoising biases in diffusion models
• Achieving high-fidelity transitions without per-instance training

Innovation

• Guidance-aware spherical interpolation for identity preservation
• Step-oriented variation trend for consistent transitions
• Tuning-free diffusion model for high-fidelity morphing