Enterprise AI Analysis

An Overview of Generative AI Technology

2.1 Technical Principles and Classification

Generative AI can break through traditional painting forms. It learns the underlying distribution patterns of massive image data through algorithms [11], and then generates new images that con-form to semantics after extensive system training and by receiving input instructions (such as prompt words, texts, sketches, etc.). Its technical paths mainly include: Generative Adversarial Networks (GAN), Variational Autoencoders (VAE), Autoregressive models, and Diffusion models. Currently, the mainstream technology is centered around Diffusion models (such as Stable Diffusion, Mid-journey, etc.).

Generative Artificial Intelligence relies on several core algorith-mic architectures, each offering distinct mechanisms for image syn-thesis. The four most prominent models-Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Autoregres-sive Models, and Diffusion Models-differ substantially in terms of learning objectives, sampling strategy, and output quality. Genera-tive Adversarial Networks (GANs), first introduced by Goodfellow [12], consist of two neural networks: a generator (G) that creates fake images from noise, and a discriminator (D) that distinguishes between real and generated images. The training process is a min-max adversarial game, where the generator seeks to minimize the discriminator's ability to tell real from fake, while the discriminator maximizes its classification accuracy. Over time, the generator learns to produce images that increasingly resemble the training data distribution. Notably, StyleGAN2 [13], an advanced variant developed by NVIDIA, achieves highly realistic portraits by intro-ducing style-based feature modulation and progressive growing, significantly improving image resolution and control over attributes. Variational Autoencoders (VAEs) follow a different probabilistic framework. They encode input data into a latent space governed by a prior distribution (typically Gaussian) and then decode it back to reconstruct the original input. This encoding-decoding process is regularized using a Kullback-Leibler divergence loss, ensuring that the latent space remains continuous and interpretable. VAEs are effective for style interpolation and smooth transformations between artistic styles, though their generated images tend to lack fine-grained detail compared to GANs [14]. The ẞ-VAE, for in-stance, is known for improving disentanglement of latent factors, which enables users to independently control attributes like brush thickness or color scheme. Autoregressive Models, such as PixelRNN, PixelCNN or more recently ImageGPT by OpenAI, model the joint probability distri-bution of images as a product of conditionals [15]. These mod-els generate images pixel-by-pixel in a sequential fashion. Built upon Transformer architectures, ImageGPT demonstrates strong language-to-image capability by converting complex text prompts into semantically aligned visual compositions. However, autore-gressive generation is typically computationally expensive and lacks parallelism, making it slower than other models despite its high fidelity. Diffusion Models have recently gained prominence due to their ability to generate high-resolution, diverse images with minimal artifacts. Inspired by the thermodynamic process of forward diffusion (adding Gaussian noise) and reverse denoising (removing noise to reconstruct data), models like DDPM and Sta-ble Diffusion achieve state-of-the-art performance across creative applications. Their iterative refinement mechanism enables better preservation of structural details and artistic coherence. Accord-ing to benchmark tests reported in, Stable Diffusion achieves an FID (Fréchet Inception Distance) score of 12.1 on COCO images [16], outperforming GAN-based systems while offering greater user control via prompt engineering and latent-space editing. Ta-ble 1 provides a structured overview of four major categories of generative Al models.

2.2 Development History and Current Situation

The concept of artificial intelligence was proposed as early as the 1950s, during which time research mainly focused on generative theoretical algorithms such as probability models [17], laying the foundation for generative AI. Due to the limitations of comput-ing power and algorithms in the early days, the image generation effect was rough and could only generate simple graphics. With the improvement of computer performance and the continuous innovation of algorithms, especially after the concept of deep learn-ing was proposed in 2006, generative AI made significant break-throughs. The proposal of Generative Adversarial Networks (GAN) in 2014 greatly improved the quality and realism of image gener-ation, promoting the application of generative AI in the field of painting. With the maturity of generative AI painting technology, well-known painting generation tools such as DALL-E, Midjourney, and Stable Diffusion have emerged (see Table 2). These tools can quickly generate high-quality and diverse painting works based on simple text descriptions or prompts provided by users, covering various traditional painting styles such as oil paintings, watercolor paintings, and Chinese paintings. They have been widely applied in fields such as art creation, design, and entertainment. According to data from Statista, the global generative AI market size is expected to grow rapidly from 2021 to 2031. It is estimated that the market size will increase by a total of 375.2 billion US dollars from 2025 to 2031. After ten consecutive years of growth, the market size is expected to reach 442.07 billion US dollars in 2031, reaching a new peak. This data reflects the rapid development of commercial applications of generative AI technology, including in the field of painting.

The Impact of Generative AI on Traditional Painting Creation Methods

The differences between generative AI and traditional painting art mainly lie in the transformation of the creative process, the expansion of tools and media used for creation, and the changes in the role of artists and technical requirements.

3.1 The transformation of the creative process

Traditional painting follows a complex and highly personalized creative workflow. The artist must observe and interpret reality through their own aesthetic lens, forming initial concepts via life experience and imagination. This is followed by sketching the composition, iterative refinement of structure and detail, color lay-ering, and final adjustments. The entire process-from concept to completion-demands extensive training, deep knowledge of ma-terials, and significant time investment. For example, producing a realistic oil painting may take from several days to weeks, including preparatory studies, pigment mixing, and layer drying time. This form of creation, though expressive and nuanced, is inherently limited in scalability, speed, and reproducibility. By contrast, generative AI introduces an entirely different pro-duction paradigm. At its core, generative AI models (such as Dif-fusion Models or GAN-based systems) operate by learning latent distributions of vast artistic datasets and synthesizing new im-ages from textual or visual prompts (shown in Figure 1). The typi-cal process begins with the user providing a text input-e.g., "an impressionist-style sunset over the ocean"-which is then parsed through a language encoder (e.g., CLIP or Transformer) that con-verts semantics into a latent vector representation. In Diffusion Models (like Stable Diffusion), this latent vector is used to condi-tion the denoising process that iteratively reconstructs an image from pure noise. Most models perform 25 to 50 denoising steps, generating a high-quality image within 5-15 seconds depending on hardware capabilities [18]. Systems like DALL-E 2 and Midjourney offer advanced control over style, resolution, and abstraction [20]. DALL-E 2 integrates a prior model to better capture semantic alignment and achieves high precision in visual-text mapping. Midjourney emphasizes artistic stylization and aesthetic coherence, often producing highly expres-sive results. According to user benchmarks, DALL-E 2 generates a 1024x1024 image in under 10 seconds on a consumer GPU, with an FID score of 12.2 on the COCO benchmark. These platforms allow creators to quickly iterate through dozens of stylistic variants by modifying parameters such as prompt length, CFG (classifier-free guidance) scale, and sampling seed. This highly automated and flexible workflow empowers both trained artists and novices to explore new ideas rapidly. It enables batch generation, stylistic interpolation, and hybrid compositional experimentation with minimal technical effort. In comparison to traditional methods, generative AI reduces the average creation time by over 90%, while offering near-infinite stylistic possibilities. Furthermore, advanced post-processing techniques such as inpaint-ing, style transfer, and latent editing allow creators to refine outputs with surgical precision. Therefore, the introduction of generative Al not only accelerates the creative cycle but also redefines the cognitive role of the artist-from a craftsman to an orchestrator of machine-aided creativity. Expansion of creative tools and media Traditional painting relies on tangible tools and materials-brushes, pigments, canvases, and papers-each of which imposes its own set of operational constraints. Oil paints, for example, offer subtle transitions and deep layering due to their slow drying time, but resist modification once set. Watercolors, while offering high fluidity and luminous effects, demand precise moisture control and exhibit low error tolerance. Generative Al replaces these physi-cal constraints with highly customizable digital interfaces, where artists interact with models via text prompts, parameter sliders, or latent code manipulation. Through natural language commands, creators can precisely control elements such as:

• Stroke weight, e.g., “thick, textured brushstrokes” or “fine line art style";
• Texture granularity, e.g., "rough canvas texture" or "smooth silk surface";
• Color opacity and blending, e.g., "50% transparent water-color wash" or "high-saturation impasto style".

In systems like Midjourney and Stable Diffusion with ControlNet, these properties can be further refined using prompt tokens (e.g., -stylize 1000, -chaotic, -rough) or embedding visual references into the generation pipeline. Users may also employ layer-specific masking, inpainting, or multi-prompt interpolation to simulate multi-media techniques not feasible in traditional settings-such as glowing iridescent inks, holographic textures, or dynamic brush in-teractions. Moreover, generative tools are resolution-agnostic and support arbitrary canvas sizes without performance degradation. This allows for ultra-high-resolution mural concepts or thumbnail-size micro-paintings. The digital nature of the medium also simpli-fies editing, versioning, duplication, and sharing-eliminating con-cerns about material degradation, archival fragility, or irreversible changes.

3.2 The changes in the roles and skill requirements of artists

In traditional painting creation, the artist is the core of the cre-ation and should possess certain painting skills, such as accurate modeling ability, delicate color perception and application ability, and proficient brush control ability, as well as profound artistic accomplishment, unique creative inspiration and acute observa-tion. In the generative AI painting environment, the artist's role changes from a hand-drawn creator to a creative leader. The artist deeply integrates technology, accurately converts creative ideas and expressions into text descriptions that the generative AI can un-derstand, providing inspiration for the AI and guiding it to generate works that meet expectations. If an artist wants to create a work that "integrates the artistic conception of traditional Chinese ink wash painting with modern sci-fi elements", they need to clearly describe the elements of the picture, the color atmosphere, style characteristics, etc. At the same time, the artist should master the relevant software of generative AI and operation skills, learn to interpret the works generated by AI, judge whether they meet the creative intentions, and use image processing knowledge to edit and optimize the generated works to make them more perfect. This means that the artist not only needs to have traditional artistic aes-thetics, but also needs to master new digital technology knowledge and skills to adapt to the transformation of the creative role.

Style Reproduction and Cross-Genre Creation

Generative Al enables both the faithful imitation of existing paint-ing styles and the innovative fusion of diverse artistic traditions. For style reproduction, models such as Neural Style Transfer (NST) and StyleGAN-based frameworks are widely used (shown in Figure 2). NST leverages convolutional neural networks (e.g., VGG-19) to extract style and content representations, reconstructing images through a combination of content loss and style loss derived from feature statistics. This allows AI to replicate brushstroke textures, color palettes, and composition patterns from artists like Van Gogh or Monet. More advanced systems, such as StyleGAN-NADA and Stable Diffusion with CLIP guidance, manipulate latent codes or prompt embeddings to generate stylistically aligned outputs with controllable fidelity. For example, when prompted to generate a "Van Gogh-style landscape," the AI synthesizes images with swirling strokes and vibrant tones that emulate the artist's signature aes-thetic. Beyond imitation, generative AI can deeply integrate and re-compose stylistic elements across genres, creating novel hybrid aesthetics. By learning from vast datasets of traditional and mod-ern artworks, models can combine visual languages that historically never intersected-for instance, blending the realistic rendering of Western oil painting with the expressive fluidity of Eastern ink wash. In such compositions, the central subject may be rendered with meticulous realism, while the background adopts ethereal ink-splash textures, producing visually striking and culturally rich outcomes (Figure 3). These capabilities open new frontiers in artis-tic creation, allowing for unprecedented diversity, personalization, and stylistic innovation.

Conclusion

Generative AI, supported by technologies such as GANs, VAEs, and Diffusion Models, is profoundly reshaping the landscape of tradi-tional painting. It not only transforms the creative workflow and expands the toolkit available to artists, but also redefines the role and skillsets required in artistic practice. In terms of style, genera-tive AI enables both accurate reproduction of established aesthetics and bold cross-genre innovation, fostering unprecedented diver-sity and personalization in visual expression. Beyond its technical and artistic contributions, the integration of Al into the art world also raises critical societal questions. Issues such as copyright at-tribution, authorship legitimacy, and artistic ethics have sparked widespread debate. The blurred boundaries between human creativ-ity and machine generation challenge existing legal and cultural frameworks, calling for interdisciplinary responses from artists, policymakers, and technologists. Looking ahead, generative Al is expected to exert increasing influence on art education-shifting the focus from manual technique to creative direction and digital literacy-and reshape the structure of the art industry by enabling scalable content production and mass customization. As the rela-tionship between human creators and intelligent systems continues to evolve, embracing AI as a collaborative partner rather than a replacement will be key. By doing so, artists can harness its capa-bilities to extend their imagination and produce works that are not only technically compelling, but also conceptually meaningful.