A Method for Training-free Person Image Picture Generation
This addresses the challenge for average users who cannot afford the hardware and cost of fine-tuning diffusion models to generate multiple images of fixed individuals, though it appears incremental as it builds on existing diffusion models.
The paper tackles the problem of generating diverse images of specific individuals using diffusion models without requiring fine-tuning, which is costly and hardware-intensive for average users, by proposing a Character Image Feature Encoder that allows users to input a character picture to match expectations and adjust details via prompts, achieving a method that can be integrated into Stable Diffusion without model modifications.
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.