# Product Image Generation Method Based on Morphological Optimization and Image Style Transfer
## 2 Literature Review
### Image Style Transfer
However, GANs exhibit critical limitations: (i) simultaneous optimization of dual networks induces training instability and convergence challenges; (ii) dependence onrandom noise sampling inherently constrains output novelty to variations within the training data distribution, limiting generative innovation. The image authenticity and detail generation capabilities of diffusion models surpass those of generative adversarial network (GAN) models. Consequently, the focus of generated image detection research is progressively shifting towards diffusion models. Compared to GANs, diffusion models demonstrate substantial advancements in generation quality and controllability. Specifically, the underlying principle of diffusion models involves a forward noising process followed by a reverse denoising process. Through this mechanism, neural networks learn to synthesize images by iteratively denoising initially random noise inputs. During reverse reconstruction, the network acquires representations of the global structure and fine-grained details of images, preserving output diversity. Furthermore, diffusion models offer enhanced controllability over generated content. This is particularly evident in their integration with cross-modal techniques, such as CLIP, enabling significant breakthroughs in text-to-image synthesis and overcoming limitations inherent in prior generative approaches. However, as diffusion models have only recently gained prominence in image generation, associated detection methodologies remain underdeveloped. Key limitations include the following: Firstly, the iterative nature of the diffusion process demands considerable computational resources, resulting in comparatively slower generation speeds, though subsequent model iterations have shown improvements. Secondly, current diffusion models exhibit content generation constraints. For instance, the Stable Diffusion (SD) model, while a leading example in AI-generated art, is primarily effective for specific portrait domains or artistic styles. Generating content in broader or novel domains typically necessitates model retraining or fine-tuning. When it comes to generating realistic product renderings from contour images, the full potential of style transfer technology has yet to be fully harnessed. Presently, the majority of researchers primarily concentrate on mere alterations in image color and texture through the application of style transfer technology, while overlooking the significance of form modification. Nevertheless, in particular scenarios, designers aspire not only for the model to facilitate style transformations between contour images and actual product images, but also for it to incorporate suitable image deformations, thereby enabling the creation of product design renderings at various levels using design contour images. To tackle the aforementioned issues, this paper introduces an intelligent image design methodology grounded in esthetic evaluation, contour optimization, and style transfer, all from the vantage point of product form design. The primary contributions of this paper can be summarized as follows:  
1. We construct a model aimed at optimizing product forms. By utilizing Bezier curves to delineate the contours of the products, we employ an esthetic comprehensive evaluation model as the fitness function. Subsequently, genetic algorithms are leveraged to refine the product forms, ultimately yielding esthetically superior solutions. 2. We employ genetic algorithms for morphological optimization. Initially, an initial population is established by encoding key morphological parameters of the target product. Through iterative application of stochastic genetic operators--selection, crossover, and mutation--successive generations are generated. An esthetic index serves as the objective fitness function, quantifying individual fitness values within each population. This cyclic process of fitness evaluation, operator application, and population renewal continues until convergence criteria are satisfied.