FreeMorph: Tuning-Free Generalized Image Morphing with Diffusion Model

Yukang Cao^*, Chenyang Si^*, Jinghao Wang, Ziwei Liu^†

Please refer to our webpage for more visualizations.

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 fine-tuning 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.

# python 3.8 cuda 12.1 pytorch 2.1.0
conda create -n freemorph python=3.8 -y && conda activate freemorph
conda install -c "nvidia/label/cuda-12.1.0" cuda-toolkit

pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu121

# other dependencies
pip install -r requirements.txt

The folder that contains the image pairs should have the structures like:

image_pairs/
    ├── pair1_0.jpg
    ├── pair1_1.jpg
    ├── ...
    ├── pairN_0.jpg
    ├── pairN_1.jpg

python caption.py --image_path /PATH/TO/PAIRED_IMAGES --json_path /PATH/TO/DESIRED/CAPTION_PATH

python freemorph.py --json_path /PATH/TO/DESIRED/CAPTION_PATH

The 4-class evaluation data, Morph4Data, is now released. You can download the dataset from Google Drive or OneDrive

If you want to cite our work, please use the following bib entry:

@article{cao2025freemorph,
  title={FreeMorph: Tuning-Free Generalized Image Morphing with Diffusion Model},
  author={Cao, Yukang and Si, Chenyang and Wang, Jinghao and Liu, Ziwei},
  journal={arXiv preprint arXiv:2507.01953},
  year={2025}
}