Diffusion-Based Image-to-Image Translation by Noise Correction via Prompt Interpolation

• URL: http://arxiv.org/abs/2409.08077v1
• Date: Thu, 12 Sep 2024 14:30:45 GMT
• Title: Diffusion-Based Image-to-Image Translation by Noise Correction via Prompt Interpolation
• Authors: Junsung Lee, Minsoo Kang, Bohyung Han,
• Abstract summary: We propose a training-free approach tailored to diffusion-based image-to-image translation. Our approach can be easily incorporated into existing image-to-image translation methods.
• Score: 43.48099716183503
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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