MagicMix: Semantic Mixing with Diffusion Models

• URL: http://arxiv.org/abs/2210.16056v1
• Date: Fri, 28 Oct 2022 11:07:48 GMT
• Title: MagicMix: Semantic Mixing with Diffusion Models
• Authors: Jun Hao Liew, Hanshu Yan, Daquan Zhou, Jiashi Feng
• Abstract summary: We explore a new task called semantic mixing, aiming at blending two different semantics to create a new concept. We present MagicMix, a solution based on pre-trained text-conditioned diffusion models. Our method does not require any spatial mask or re-training, yet is able to synthesize novel objects with high fidelity.
• Score: 85.43291162563652
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Have you ever imagined what a corgi-alike coffee machine or a tiger-alike rabbit would look like? In this work, we attempt to answer these questions by exploring a new task called semantic mixing, aiming at blending two different semantics to create a new concept (e.g., corgi + coffee machine -- > corgi-alike coffee machine). Unlike style transfer, where an image is stylized according to the reference style without changing the image content, semantic blending mixes two different concepts in a semantic manner to synthesize a novel concept while preserving the spatial layout and geometry. To this end, we present MagicMix, a simple yet effective solution based on pre-trained text-conditioned diffusion models. Motivated by the progressive generation property of diffusion models where layout/shape emerges at early denoising steps while semantically meaningful details appear at later steps during the denoising process, our method first obtains a coarse layout (either by corrupting an image or denoising from a pure Gaussian noise given a text prompt), followed by injection of conditional prompt for semantic mixing. Our method does not require any spatial mask or re-training, yet is able to synthesize novel objects with high fidelity. To improve the mixing quality, we further devise two simple strategies to provide better control and flexibility over the synthesized content. With our method, we present our results over diverse downstream applications, including semantic style transfer, novel object synthesis, breed mixing, and concept removal, demonstrating the flexibility of our method. More results can be found on the project page https://magicmix.github.io

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