A Method for Training-free Person Image Picture Generation

• URL: http://arxiv.org/abs/2305.09817v1
• Date: Tue, 16 May 2023 21:46:28 GMT
• Title: A Method for Training-free Person Image Picture Generation
• Authors: Tianyu Chen
• Abstract summary: A Character Image Feature model is proposed in this paper. It enables the user to use the process by simply providing a picture of the character to make the image of the character in the generated image match the expectation. The proposed model can be conveniently incorporated into the Stable Diffusion generation process without modifying the model's or used in combination with Stable Diffusion as a joint model.
• Score: 4.043367784553845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The current state-of-the-art Diffusion model has demonstrated excellent results in generating images. However, the images are monotonous and are mostly the result of the distribution of images of people in the training set, making it challenging to generate multiple images for a fixed number of individuals. This problem can often only be solved by fine-tuning the training of the model. This means that each individual/animated character image must be trained if it is to be drawn, and the hardware and cost of this training is often beyond the reach of the average user, who accounts for the largest number of people. To solve this problem, the Character Image Feature Encoder model proposed in this paper enables the user to use the process by simply providing a picture of the character to make the image of the character in the generated image match the expectation. In addition, various details can be adjusted during the process using prompts. Unlike traditional Image-to-Image models, the Character Image Feature Encoder extracts only the relevant image features, rather than information about the model's composition or movements. In addition, the Character Image Feature Encoder can be adapted to different models after training. The proposed model can be conveniently incorporated into the Stable Diffusion generation process without modifying the model's ontology or used in combination with Stable Diffusion as a joint model.

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