MCGM: Mask Conditional Text-to-Image Generative Model

• URL: http://arxiv.org/abs/2410.00483v1
• Date: Tue, 1 Oct 2024 08:13:47 GMT
• Title: MCGM: Mask Conditional Text-to-Image Generative Model
• Authors: Rami Skaik, Leonardo Rossi, Tomaso Fontanini, Andrea Prati,
• Abstract summary: We propose a novel Conditional Mask Text-to-Image Generative Model (MCGM) Our model builds upon the success of the Break-a-scene [1] model in generating new scenes using a single image with multiple subjects. By introducing this additional level of control, MCGM offers a flexible and intuitive approach for generating specific poses for one or more subjects learned from a single image.
• Score: 1.909929271850469
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in generative models have revolutionized the field of artificial intelligence, enabling the creation of highly-realistic and detailed images. In this study, we propose a novel Mask Conditional Text-to-Image Generative Model (MCGM) that leverages the power of conditional diffusion models to generate pictures with specific poses. Our model builds upon the success of the Break-a-scene [1] model in generating new scenes using a single image with multiple subjects and incorporates a mask embedding injection that allows the conditioning of the generation process. By introducing this additional level of control, MCGM offers a flexible and intuitive approach for generating specific poses for one or more subjects learned from a single image, empowering users to influence the output based on their requirements. Through extensive experimentation and evaluation, we demonstrate the effectiveness of our proposed model in generating high-quality images that meet predefined mask conditions and improving the current Break-a-scene generative model.

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