A Method for Training-free Person Image Picture Generation
Tianyu Chen
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.