Diffusion-Based Image-to-Image Translation by Noise Correction via Prompt Interpolation
<|reference_start|>Diffusion-Based Image-to-Image Translation by Noise Correction via Prompt Interpolation: We propose a simple but effective training-free approach tailored to diffusion-based image-to-image translation. Our approach revises the original noise prediction network of a pretrained diffusion model by introducing a noise correction term. We formulate the noise correction term as the difference between two noise predictions; one is computed from the denoising network with a progressive interpolation of the source and target prompt embeddings, while the other is the noise prediction with the source prompt embedding. The final noise prediction network is given by a linear combination of the standard denoising term and the noise correction term, where the former is designed to reconstruct must-be-preserved regions while the latter aims to effectively edit regions of interest relevant to the target prompt. Our approach can be easily incorporated into existing image-to-image translation methods based on diffusion models. Extensive experiments verify that the proposed technique achieves outstanding performance with low latency and consistently improves existing frameworks when combined with them.<|reference_end|>
@article{lee2024diffusion-based,
    title={Diffusion-Based Image-to-Image Translation by Noise Correction via
  Prompt Interpolation},
    author={Junsung Lee, Minsoo Kang, Bohyung Han},
    journal={arXiv preprint arXiv:2409.08077},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08077},
    primaryClass={cs.CV}
}