# High-Quality Text-to-Image Generation Using High-Detail Feature-Preserving Network
## 2 Related Work
### Text-to-Image Generation Networks  
[PERSON] et al. [35] used GANs to convert text into images for text-to-image generation, developing a simple and effective GAN architecture and training strategy that can synthesize captivating bird and flower images from human-written descriptions. However,  
Figure 1: Applications of the proposed HDFpNet model. (**a**) High-quality image generation from the warning signs on a construction site; (**b**) high-quality image generation from the descriptive content of machine operation steps; (**c**) high-quality image generation from information about birds in a tourist area. the resolution of these images is very low. To address this issue, [PERSON] et al. proposed using StackGAN [9] and StackGAN++ [11], two multistage methods in which the image generated at each stage is used as input for the next stage of image generation, thereby increasing the resolution of the image. However, the generated images still cannot reflect the shapes or colors of objects and generate unreasonable bird images. AttnGAN [10] introduced an attention mechanism into multistage image generation, allowing the model to perform word-level processing and enhance the image details while effectively generating text-to-image content for complex scenes. However, there are still many distorted images generated, such as birds with two heads, which are illogical. DM-GAN [14] introduced a dynamic memory generation network (DM-GN) to adjust the weight of each word in a sentence and enhance the blurry parts of the image generated in the previous stage. By assigning weights reflecting the importance of text individually, it improves the quality of the final image. However, it emphasizes the overall completeness and clarity of the image generated after text generation, while ignoring illogical local details. MirrorGAN [13] reconstructed text descriptions during image generation to improve the consistency between text and images. Since it emphasizes the consistency between the generated images and text descriptions, it ignores the effect of details, including the restoration of eyes and feet. KT-GAN [12] used a knowledge transfer method from image-to-image models to support text-to-image models, thereby improving the image quality. However, the generated images do not restore the features well. The recently proposed Transformer-based text-to-image methods can generate more complex images by calculating a large number of text parameters and combining them with an image encoder. CogView [38] and DALL-E [39] leveraged the advantages of Transformer-based methods to generate visually reasonable images in various types of scene descriptions. Although they can generate rough background outlines, the former produces images with relatively blurry backgrounds, while the latter has insufficient restoration. CSM-GAN [40] proposed a cross-modal semantic matching method that can better encode local key information to highlight detailed features in synthesized images and highlight important local structural information in text descriptions. However, it ignores the details in the picture, such as a bird's feet, which may be presented in an illogical manner, such as having only one foot or abnormally fine feet. [PERSON] [41] generated images from text descriptions through improved distribution learning, obtaining more accurate image distribution from key information to generate better-quality images. Although the overall performance regarding details is better, there are still some problems, including some artifacts and details, such as birds' feet not being well presented. The multistage methods [9; 10; 11; 12; 13; 14] primarily aim to enhance the image resolution and improve the extraction of image and text features. The extracted features are processed using a residual network to retain relevant features. However, none of these methods optimize the current image features.