2.5 Principles of AI Image Generation Technology

From Text to Pixels: Demystifying AI Image Generation Technology

In recent years, Artificial Intelligence has demonstrated astonishing leaps in visual creativity. By simply inputting a descriptive text phrase (known as a Text Prompt) into an AI, it can “conjure up” high-quality, realistic, and even artistically compelling images corresponding to the description within a short time. This technology, known as “Text-to-Image Generation,” is like giving wings to imagination, immensely stimulating possibilities in creative design, advertising, art creation, game development, virtual reality, and even in legal scenarios such as visualization assistance and evidence simulation (requires extreme caution).

To truly understand how these seemingly “magical” AI drawing tools (like Midjourney, Stable Diffusion, DALL-E series) work, grasp their capability boundaries, and foresee the potential legal and ethical risks they entail (e.g., copyright ownership disputes, proliferation of deepfakes, content bias), it is necessary for legal professionals to have a basic understanding of their underlying core technical principles.

This section will focus on introducing the two mainstream technological paradigms currently driving the wave of AI image generation: the former king—Generative Adversarial Networks (GANs), and the core engine now widely adopted by state-of-the-art text-to-image tools—Diffusion Models. We will delve into their working mechanisms, key variations, strengths, and potential limitations.

I. Generative Adversarial Networks (GANs): Learning “Realism” Through Adversarial Play

Generative Adversarial Networks (GANs), sensationally proposed by legendary AI researcher Ian Goodfellow and colleagues in 2014, are a highly innovative unsupervised learning model framework. The advent of GANs profoundly impacted the deep learning field, particularly in image generation, where they once set the benchmark for achieving the highest levels of realism.

The core idea of GANs cleverly borrows the zero-sum game concept from Game Theory. It sets up two neural networks that compete against and evolve with each other: the Generator and the Discriminator.

1. Core Components: Generator vs. Discriminator

• Generator (G):

  • Role: Imagine a skilled “forger” or “master counterfeiter.”
  • Goal: To learn the underlying distribution of real image data and attempt to generate new image samples that are as realistic as possible, capable of fooling the discriminator.
  • Workflow:
    • Input: Typically receives a random noise vector (latent vector, z) as input. This noise vector can be seen as the “seed,” “inspiration,” or “code” for the content of the generated image. Changing this seed vector theoretically generates different images.
    • Processing: Through a series of neural network layers (often based on Transposed Convolution for Up-sampling after DCGAN), it progressively “decodes” or “paints” the low-dimensional noise vector into high-dimensional image data.
    • Output: Generates a fake image sample.
  • Learning Motivation: The Generator learns from the Discriminator’s feedback. If the Discriminator easily identifies its generated image as fake, the Generator adjusts its parameters (weights) to improve its generation strategy, aiming to produce more realistic images next time. The goal is for the Discriminator to eventually be unable to distinguish its creations from real ones (i.e., the Discriminator outputs a probability close to 0.5).

• Discriminator (D):

  • Role: Imagine a sharp-eyed “art authenticator” or “detective.”
  • Goal: To learn how to accurately distinguish between real image samples (from the training dataset, like museum originals) and fake image samples generated by the Generator (like forgeries).
  • Workflow:
    • Input: Receives an image sample (which could be real or fake; the Discriminator doesn’t know beforehand).
    • Processing: Through a series of neural network layers (often based on Convolution for Down-sampling, similar to image classification networks), it extracts features and makes a judgment.
    • Output: Outputs a probability value (between 0 and 1) representing how likely it judges the input image to be real. E.g., close to 1 means it thinks it’s real, close to 0 means it thinks it’s fake.
  • Learning Motivation: The Discriminator learns by being exposed to both numerous real samples and fake samples from the Generator, knowing their true labels (real/fake). Its objective is to continuously sharpen its “eye,” aiming to assign high scores to real samples and low scores to fake samples as accurately as possible.

2. Dynamic Game: Co-evolution Through Adversity

The Generator (G) and Discriminator (D) do not operate independently but are locked in a dynamic, adversarial game process, often described as a Minimax Game:

• Discriminator’s (D) Goal: To maximize its ability to correctly distinguish real from fake samples. It wants to become as “smart” as possible, i.e., maximize V(D, G).
• Generator’s (G) Goal: To minimize the Discriminator’s ability to distinguish correctly, i.e., to “confuse” the Discriminator. It wants to become as “cunning” as possible, i.e., minimize V(D, G).

This process resembles:

1. Early Stage: The Generator (novice painter) scribbles randomly, producing crude images. The Discriminator (novice appraiser) easily tells them apart. The Discriminator learns quickly.
2. Middle Stage: The Generator, based on the Discriminator’s feedback (which fakes were caught), learns to improve its painting techniques, producing more decent images. The Discriminator also needs to learn subtler features to differentiate, improving its appraisal skills.
3. Late Stage (Ideal Equilibrium): The Generator becomes highly skilled, generating images statistically indistinguishable from real ones. The Discriminator, no matter how hard it tries, struggles to tell them apart (giving probabilities close to 0.5 for any input, essentially guessing randomly). At this ideal Nash Equilibrium, the Generator is considered to have successfully learned the underlying distribution of the real data, capable of generating high-quality, realistic images.

3. Important GAN Variants and Applications

While the basic GAN idea is ingenious, it often suffers from practical issues like training instability (e.g., vanishing/exploding gradients, asynchronous training of G and D), Mode Collapse (Generator learns to produce only a few modes/samples from the data distribution, lacking diversity), and difficulty in controlling the generated content. To address these issues and expand GAN applications, researchers proposed numerous improved GAN architectures:

• DCGAN (Deep Convolutional GANs): First successfully integrated the powerful feature extraction capabilities of Convolutional Neural Networks (CNNs) into both the Generator and Discriminator, significantly improving generated image quality and training stability. DCGAN introduced architectural guidelines (e.g., using strided convolutions instead of pooling, using Batch Normalization, removing fully connected layers, using ReLU/LeakyReLU activations) that formed the basis for many subsequent image GANs.

• Conditional GANs (cGANs): A very important extension. cGANs allow incorporating additional conditional information (Condition, y)—like class labels, text descriptions, edge maps, segmentation masks—into the generation process. The Generator receives not only random noise z but also the condition y, aiming to generate realistic images consistent with the specific condition G(z|y). The Discriminator receives both the image and the condition, judging if the image is real and matches the condition D(x|y).

  • Significance: cGANs enable control over the generated content, rather than just random generation. Many early “Text-to-Image” techniques were based on cGAN attempts, where the condition y was an embedding of the text description.

• StyleGAN Series (developed by NVIDIA Research): Achieved landmark breakthroughs in generating high-resolution, highly realistic, and controllable facial images. The “fake faces” generated by StyleGANs are often indistinguishable from real ones by the human eye. StyleGAN’s success stems from several innovations:

  • Disentangled Style Control: Introduced a Mapping Network to map the input noise z to an intermediate latent space W. Vectors in W are thought to control different style attributes (hair, age, skin tone, pose, lighting) in a more disentangled way.
  • Style Injection: Used Adaptive Instance Normalization (AdaIN) to inject style information from W into different layers of the Generator (corresponding to different feature map resolutions), enabling fine-grained control over image features at various scales.
  • Stochastic Detail Injection: Injecting random noise at different Generator layers adds stochastic details like hair texture or skin pores, enhancing realism and naturalness.
  • Progressive Growing: Started training by generating low-resolution images and gradually increased network layers and image resolution during training, helping stabilize high-resolution training.
  • Subsequent Improvements: From StyleGAN to StyleGAN2, StyleGAN3, and newer versions, issues like droplet artifacts and feature-coordinate entanglement (causing distortion upon rotation/translation) were addressed, further improving quality and control. StyleGAN-like technologies are behind many of the concerningly realistic AI-generated faces potentially used for creating fake identities or fraud.

• CycleGAN: Solved the challenge of Unpaired Image-to-Image Translation. E.g., translating horse photos to zebra style without having paired images of the “same horse” and its “corresponding zebra.” CycleGAN cleverly introduced Cycle Consistency Loss: translating a horse to a “zebra” and then back to a “horse” should yield the original horse. This constraint ensures the transformation preserves the basic structure and content while changing the style, preventing arbitrary outputs unrelated to the input. Widely used for style transfer, season translation, art mimicry, etc.

• BigGAN: Focused on generating high-fidelity, high-resolution images with rich class diversity. By using extremely large batch sizes, increased model parameters (deeper/wider networks), Orthogonal Regularization, etc., it significantly boosted generation quality on large datasets like ImageNet, achieving unprecedented visual quality and diversity at the time.

• WGAN (Wasserstein GAN): Addressed the instability and mode collapse issues of original GANs by proposing the use of Wasserstein distance (or Earth Mover’s Distance) to measure the difference between real and generated data distributions, replacing the original JS/KL divergence-based objective. The Wasserstein distance has better mathematical properties (provides meaningful gradients even when distributions don’t overlap), leading to more stable training. Its loss value also better correlates with generation quality (lower loss generally means better results), aiding debugging and model selection. WGAN and its improvements (like WGAN-GP with gradient penalty) were crucial milestones for enhancing GAN training stability.

4. Inherent Limitations of GANs

Despite their brilliant successes, GANs and their variants still face inherent challenges that are difficult to completely overcome:

• Training Instability: Even with improvements like WGAN, training GANs can still feel like “black magic,” highly sensitive to hyperparameters (learning rates, optimizers), network architecture, regularization methods, etc. Finding stable convergence settings often requires extensive experimentation and expertise. Balancing the Generator and Discriminator is tricky; one becoming too strong can lead to training failure.
• Mode Collapse: The Generator might “get lazy” and only learn to produce a few modes/types of samples from the data distribution that easily fool the Discriminator, ignoring the rich diversity present in the real data. E.g., a cat-generating GAN might only produce cats in a few specific poses or breeds.
• Evaluation Difficulty: Objectively and quantitatively evaluating the quality (realism) and diversity of GAN-generated images remains a persistent challenge. While common metrics like Inception Score (IS) and Fréchet Inception Distance (FID) exist, they have limitations. Final assessment often relies heavily on subjective human evaluation.
• Computationally Intensive: Training high-quality, high-resolution GANs typically requires large amounts of (labeled, for cGANs, or unlabeled) data and powerful computational resources (especially GPUs).
• Adversarial Fragility: The Discriminator might have “blind spots” or be easily fooled by specific, imperceptible patterns. This could lead the Generator to produce images that fool the Discriminator but exhibit systematic, unnatural flaws or artifacts upon closer inspection.

5. Potential Relevance of GANs in Legal Scenarios (Risks and Opportunities)

• Evidence Generation & Deepfake Risk: This is the most prominent negative impact of GANs (especially advanced models like StyleGAN) in the legal field. Their ability to generate highly realistic facial images, and potentially videos, significantly heightens concerns about deepfake evidence. Criminals could use GANs to generate:

  • Fake ID photos, passport photos for fraud, money laundering, or evading regulations.
  • Forged surveillance screenshots or dashcam snippets (generating coherent video is harder for GANs but not impossible) to create false alibis or frame others.
  • Fake chat log screenshots, email screenshots, etc. If such forged evidence enters judicial proceedings, it poses a grave threat to fact-finding and judicial fairness.

• Intellectual Property Disputes: Using GANs to generate art, designs, music, or even code raises complex IP issues:

  • Copyright Ownership of Generated Content: Do AI-generated works qualify for copyright? Who owns the copyright—the AI model, developer, user (prompt provider), or training data owner? Laws vary globally, and this is highly debated.
  • Copyright Issues with Training Data: If GAN training data includes numerous copyrighted images (e.g., scraped artworks), do the generated images infringe the originals? Did the model “learn” or “copy” protected styles or elements? This is the focus of several high-profile lawsuits.

• Visualization Aid (Theoretical Potential & Risks): Theoretically, Conditional GANs could be used to generate simulated crime scenes, accident reconstructions, suspect sketches, or visualizations of contract risk based on textual descriptions, witness testimonies, or reports. This might help lawyers, judges, or juries visualize complex situations or communicate key information more intuitively. However, this application requires extreme caution! Generated images are merely “imaginations” or “inferences” based on input, and their accuracy, objectivity, and potential for introducing misleading bias must be rigorously assessed. If used, their non-evidentiary nature must be explicit, and their potential psychological suggestion effects must be considered.

• Identity Forgery & Privacy Invasion: Models like StyleGAN can generate vast numbers of realistic but non-existent facial photos (“fake faces”). These can be used for:

  • Creating numerous fake social media accounts, troll farms for manipulating public opinion or conducting scams.
  • Generating synthetic images highly resembling a real person without permission, for advertising, defamation, or other purposes, potentially infringing portrait rights or privacy.

II. Diffusion Models: Gradually “Sculpting” Clear Images from Chaotic Noise

Diffusion Models have recently emerged as a powerful class of deep learning models in generative modeling, achieving breakthrough results in image, audio, video, and even 3D shape generation. They have become the core driving technology behind current state-of-the-art text-to-image tools (like Stable Diffusion, DALL-E 2/3, Midjourney, Imagen). Unlike GANs’ direct, one-shot generation or adversarial game approach, diffusion models employ a gentler, more controllable, iterative generation process based on gradual denoising.

1. Theoretical Origins and Development Path

The theoretical idea behind diffusion models traces back to Nonequilibrium Thermodynamics, describing the random motion (Brownian motion) and gradual dispersal of tiny particles (like pollen) in a liquid. Early work applying this idea to machine learning generative modeling appeared in 2015 (Sohl-Dickstein et al., “Deep Unsupervised Learning using Nonequilibrium Thermodynamics”) but didn’t gain widespread attention then.

It wasn’t until 2020 that Jonathan Ho and colleagues (at Google Brain, advised by Ashish Vaswani of Transformer fame) published the landmark paper “Denoising Diffusion Probabilistic Models” (DDPM). They demonstrated that through a carefully designed diffusion and denoising process, images rivaling or surpassing top GANs in quality could be generated, with more stable training. DDPM’s success ignited intense research interest in diffusion models.

Subsequently, OpenAI released GLIDE and DALL-E 2, which used CLIP for text conditioning; Google launched Imagen; and Stability AI (in collaboration with LMU Munich’s CompVis group and RunwayML) released and open-sourced the Stable Diffusion series. These pushed diffusion model technology into the application mainstream, popularizing high-quality text-to-image capabilities.

Mathematically, diffusion models can be understood as a type of Hierarchical Variational Autoencoder (VAE), or are closely theoretically related to Score-based Generative Models / Noise Conditional Score Networks (NCSN) (proposed by Yang Song et al. at Stanford). Later research showed DDPM is an implementation of Score-based Models over discrete time steps.

2. Core Idea: The Two-Step Dance of Diffusion and Denoising

The core operation of diffusion models involves two interconnected, opposing processes:

• Forward Process (Diffusion Process):

  • Goal: This is a fixed, human-defined process that doesn’t require learning. It starts with a real, clean image x_0 (from the training dataset).
  • Operation: Over a series of discrete time steps t=1, 2, ..., T (T is typically hundreds to thousands, e.g., T=1000 in DDPM), Gaussian Noise is gradually added to the image in small amounts. The amount of noise added at each step is controlled by a predefined Noise Schedule \beta_t, which usually increases with t (more noise added later).
  • Process: x_1 is obtained by adding slight noise to x_0, x_2 by adding noise to x_1, and so on… x_t is obtained by adding noise \epsilon \sim \mathcal{N}(0, \beta_t \mathbf{I}) to x_{t-1}.
  • Result: After T steps, the structural information of the original image x_0 is gradually overwhelmed by noise, and the final x_T becomes almost pure, unstructured random noise (its distribution approaches a standard Gaussian \mathcal{N}(0, \mathbf{I})). This process simulates the gradual diffusion and disappearance of information (ordered image structure) under random perturbations, increasing the system’s Entropy.
  • Mathematical Shortcut: A key property shown in the DDPM paper is that the distribution of the noisy image x_t at any time step t, obtained directly from the original image x_0, is also Gaussian. Its mean and variance can be calculated directly from x_0 and the cumulative noise level \bar{\alpha}_t = \prod_{i=1}^{t}(1-\beta_i): x_t = \sqrt{\bar{\alpha}_t}x_0 + \sqrt{1-\bar{\alpha}_t}\epsilon, where \epsilon \sim \mathcal{N}(0, \mathbf{I}). This closed-form solution allows sampling a time step t during training and directly calculating x_t and the corresponding noise \epsilon without simulating the entire forward process, greatly improving training efficiency.

• Reverse Process (Denoising Process):

  • Goal: This is the key process the model needs to learn through training. Its objective is to precisely Reverse the forward noising process described above.

  • Operation: Starting from a pure random noise image sampled from the same distribution as x_T (i.e., from \mathcal{N}(0, \mathbf{I})), the process involves iteratively denoising over time steps t=T, T-1, ..., 1. At each step t, the model needs to predict the noise present in x_t (or equivalently, predict the cleaner x_{t-1} or x_0). It then subtracts (or adjusts based on the prediction) this estimated noise from x_t to obtain a slightly cleaner image x_{t-1}.

  • Learning Task: The core learning task for the model is to accurately predict the original noise \epsilon that led to x_t, given the noisy image x_t and the noise level t. Thus, the model’s training objective (loss function) is typically to minimize the Mean Squared Error (MSE) between its predicted noise \epsilon_\theta(x_t, t) and the actual noise \epsilon added during training: \mathcal{L} = \mathbb{E}_{t \sim [1,T], x_0, \epsilon \sim \mathcal{N}(0,\mathbf{I})}[||\epsilon - \epsilon_\theta(\sqrt{\bar{\alpha}_t}x_0 + \sqrt{1-\bar{\alpha}_t}\epsilon, t)||^2]. Here, \epsilon_\theta represents the denoising neural network we need to train.

  • Denoising Network Architecture: The neural network used to predict the noise \epsilon_\theta takes the current noisy image x_t and the current time step t as input (time step info is usually encoded, e.g., via Transformer positional encodings or simple embeddings). The network architecture must handle image data and have identical input and output dimensions (input is noisy image, output is predicted noise). The U-Net architecture has become the most common and successful choice for diffusion models.

    • U-Net: Originally designed for medical image segmentation, it’s an Encoder-Decoder architecture featuring:
      • A down-sampling path (Encoder) that extracts contextual features and reduces spatial resolution using convolutions and pooling.
      • An up-sampling path (Decoder) that recovers spatial resolution and details using transposed convolutions or up-sampling.
      • Skip Connections that pass shallow, high-resolution features directly from the encoder to corresponding decoder layers, helping reconstruct details and alleviate vanishing gradients in deep networks.
    • U-Nets used in modern diffusion models are often enhanced, e.g., with extensive use of Self-Attention to capture long-range dependencies within the image, and (for conditional generation) Cross-Attention to fuse conditioning information (like text embeddings).

  • Image Generation (Sampling) Process: Once the model is trained, generating a new image involves:

    1. Start: Sample a pure noise image x_T from the standard Gaussian distribution \mathcal{N}(0, \mathbf{I}).
    2. Iterative Denoising: From t=T down to t=1:
       • Input the current noisy image x_t and time step t into the trained denoising network \epsilon_\theta to get the predicted noise \hat{\epsilon} = \epsilon_\theta(x_t, t).
       • Use this predicted noise \hat{\epsilon} along with predefined parameters like \beta_t, \bar{\alpha}_t in a sampling formula (e.g., DDPM or DDIM sampling step) to calculate the previous (slightly cleaner) image x_{t-1}. This essentially involves “subtracting” the predicted noise from x_t (possibly adding a small amount of new random noise for diversity or distribution correction).
    3. End: After the final denoising step at t=1, the resulting x_0 is the generated clear image.

3. Key Advantages of Diffusion Models: Why So Powerful?

Diffusion models have rapidly overtaken GANs to become the dominant technology in image generation, thanks to several key advantages:

• High-Quality Generation Results: They typically produce images with high resolution, rich detail, and excellent visual realism, often surpassing top GANs on benchmarks like FID score.
• More Stable Training: Compared to the delicate balancing act required for GANs’ adversarial training, the diffusion model objective (predicting noise) is more direct and stable, less prone to issues like mode collapse.
• Better Mode Coverage and Diversity: Diffusion models generally learn the full distribution of training data better, resulting in generated samples with greater diversity and less mode collapse.
• Easy Conditional Generation and Control: Conditioning information (like text prompts) can be effectively integrated into the denoising process using mechanisms like cross-attention, enabling high-quality conditional generation (e.g., text-to-image). Techniques like Classifier-Free Guidance allow flexible control over adherence to the condition. Technologies like ControlNet enable fine-grained control over structure, pose, etc.
• Solid Theoretical Foundation: They have deep theoretical connections to probabilistic modeling, score matching, and variational inference, providing a basis for further analysis and improvement.

4. Main Challenges of Diffusion Models

Of course, diffusion models are not perfect and still face challenges:

• Relatively Slow Sampling Speed: Traditional DDPM requires hundreds or thousands of denoising steps to generate an image, making generation relatively slow compared to GANs (which need only one forward pass). Although accelerated sampling techniques (like DDIM) and methods like Latent Diffusion Models (operating in lower-dimensional space) have significantly improved efficiency, sampling is often still slower than GANs.
• Computational Resource Requirements (Training): Training large, high-quality diffusion models (especially for high-resolution images or video) still demands massive computational resources (GPU memory, compute power) and huge datasets.
• Theoretical Understanding Still Deepening: Despite great success, a deep theoretical understanding of why they generate such high-quality samples, the precise role of attention mechanisms, and how to more precisely control the generation process is still an active area of research.

5. Latent Diffusion Models (LDM) and the Success of Stable Diffusion

The introduction of Latent Diffusion Models (LDM) was a crucial step enabling widespread application of diffusion models (especially on consumer hardware) and forms the core architecture of the Stable Diffusion series. The key idea is: “Diffusion in high-dimensional pixel space is too expensive. Can we first compress the image into an information-rich low-dimensional Latent Space, perform diffusion and denoising there, and finally decode back to pixel space?”

• Key Innovations:

  1. Autoencoder for Spatio-temporal Compression: LDM first uses a pre-trained autoencoder (often like VQ-VAE or VQGAN, with an Encoder and Decoder).
     • Encoder: Compresses the high-resolution input image x into a much lower-dimensional latent representation z. This z captures the main semantic and structural information, discarding pixel-level redundancy.
     • Decoder: Decompresses (reconstructs) the latent representation z back into a high-resolution image \hat{x}. The autoencoder is trained to reconstruct images well (\hat{x} \approx x).
  2. Diffusion and Denoising in Latent Space: Crucial step! The forward noising and reverse denoising processes are applied not to the original pixel image x, but to its low-dimensional latent representation z. This means the U-Net model for denoising also operates in the latent space, taking latent representations as input and outputting predicted noise in the latent space.
  3. Drastically Reduced Computational Complexity: Since the dimensionality of the latent space z (e.g., 64x64x4 for a 512x512 image in Stable Diffusion) is much smaller than the original pixel space (512x512x3), the computation and memory required for diffusion and denoising in latent space are greatly reduced. This enables training larger, more powerful diffusion models and allows inference (image generation) on hardware with relatively limited resources (like consumer GPUs).
  4. High-Quality Output Preserved: Because the pre-trained autoencoder effectively preserves key image information and reconstructs details well, performing diffusion in latent space still results in high-quality final image outputs.

• Stable Diffusion Implementation: Stable Diffusion is based on the LDM architecture. It uses a powerful CLIP text encoder to get embeddings for the text prompt. These embeddings are then injected into the U-Net (which operates in the latent space) via cross-attention mechanisms to condition the denoising process. Finally, the denoised latent representation is passed through the VAE decoder to generate the final pixel image. The success of this architecture, combined with its open-source policy, has massively driven the adoption and community innovation around text-to-image technology.

6. Implementing Conditional Guidance in Text-to-Image

To enable diffusion models to generate images based on Text Prompts, the core task is to effectively “inject” and “guide” the reverse denoising process using the semantic information from the text. This primarily relies on:

• Powerful Text Encoder: A model capable of deeply understanding text semantics and converting text into high-quality vector representations (Text Embeddings) is needed. The most commonly used is the CLIP text encoder, pre-trained on massive image-text pairs via contrastive learning, ensuring good alignment between its text embeddings and visual concepts. Encoders from other large language models (like T5) are sometimes used too.

• Cross-Attention Mechanism: This is the most critical and common technique for integrating text information into the U-Net denoising process. Cross-attention modules are added at multiple levels of the U-Net. In these modules:

  • Query (Q) comes from the image (or latent) features.
  • Key (K) and Value (V) come from the text prompt’s embeddings. By calculating similarity (attention scores) between Query and Key, the model determines which words or concepts in the text prompt are most relevant for generating the current image region. It then uses these attention weights to extract relevant information from the Value (text info) to guide the update of image features. This allows for fine-grained response to the text description.

• Classifier-Free Guidance (CFG): A simple yet highly effective technique to enhance the alignment between the generated image and the text prompt (sometimes at the cost of slightly reduced diversity). The idea is: during training, occasionally (e.g., 10-20% of the time) set the text condition to null (i.e., train for unconditional generation). During inference (generation), the model calculates both the noise prediction conditioned on the text c, \epsilon_\theta(x_t, c, t), and the unconditional noise prediction (with c=\empty), \epsilon_\theta(x_t, \empty, t). The final noise prediction used is a linear combination (effectively, an amplified difference) of these two: \hat{\epsilon}_\theta(x_t, c, t) = \epsilon_\theta(x_t, \empty, t) + s \cdot (\epsilon_\theta(x_t, c, t) - \epsilon_\theta(x_t, \empty, t)) Here, s is the Guidance Scale or CFG Scale, a hyperparameter (often set between 7-15). s=0 is equivalent to unconditional generation; s=1 uses only the conditional prediction; s>1 “pushes” the prediction direction further towards aligning with the text condition. Higher s values generally result in images more faithful to the prompt but potentially less diverse or more distorted. Users can adjust the CFG Scale to trade off Fidelity (prompt adherence) and Diversity.

7. Advanced Control Techniques: Beyond Simple Text-to-Image

Modern diffusion model development has moved far beyond simple “you say, I draw,” enabling much finer and more diverse control over the generation process:

• ControlNet: A revolutionary technique (proposed by Lvmin Zhang et al. at Stanford in early 2023) that allows users to add extra spatial conditioning while keeping the powerful generative capabilities of a pre-trained diffusion model (like Stable Diffusion) intact. ControlNet works by creating a trainable “copy” of the encoder part of the pre-trained U-Net. The outputs of this trainable copy are added to the corresponding layers of the original U-Net. This trainable copy specifically learns to incorporate additional control conditions, such as:

  • Canny Edges: Controls the outline of the generated image.
  • Depth Map: Controls the 3D spatial layout.
  • Human Pose Skeletons (OpenPose, MediaPipe Pose): Precisely controls the pose of generated figures.
  • Scribbles/Sketches: Generates images based on user doodles.
  • Segmentation Maps: Controls the content category generated in different regions. ControlNet vastly enhances the controllability of diffusion models, making them much more suitable for design tasks requiring precise layout and structure.

• Inpainting: Allows users to select a region of an image (using a Mask) and provide a text prompt, instructing the model to regenerate content only within the selected area, leaving the rest of the image unchanged. Very useful for image editing tasks like removing unwanted objects, replacing backgrounds, or fixing photo defects.

• Image-to-Image (Img2Img): Unlike text-to-image which starts from pure noise, Img2Img starts with an existing input image and a text prompt. The model first adds a certain amount of noise to the input image (controlled by the “Denoising Strength” parameter), then performs the reverse denoising process on this noisy image, guided by the text prompt. This generates an image that retains the basic structure and composition of the original image but incorporates the new style or content described in the text prompt.

• Low-Rank Adaptation (LoRA): A Parameter-Efficient Fine-Tuning (PEFT) technique widely used for personalizing diffusion models. The core idea is: when fine-tuning a large pre-trained model, instead of modifying the original massive weight matrices, we add two small, Low-Rank matrices alongside certain layers (typically attention layers). During fine-tuning, only the parameters of these small matrices are trained (far fewer parameters than the original model), while the original weights remain frozen. During inference, the product of these two small matrices is added to the original weights.

  • Advantages: LoRA allows users to teach a general pre-trained model (like Stable Diffusion) a specific art style, an artist’s style, an anime character, an object, or a concept using relatively little data and computational resources, without retraining the entire huge model. Trained LoRA files are usually small (few MBs to hundreds of MBs), easy to share and load. This has greatly fostered the community ecosystem around open-source models like Stable Diffusion, allowing users to easily download and use various themed LoRAs to customize their generations.

III. Analysis of Mainstream Text-to-Image Models: An Era of Competing Contenders

The AI text-to-image field is currently vibrant, with rapid technological iteration. Several mainstream models and platforms have distinct characteristics:

1. Stable Diffusion Series (Stability AI & Collaborators)

• Core Technology: Based on the Latent Diffusion Model (LDM) architecture.
• Key Feature: Open Source. Model weights, codebases (like the diffusers library), and related control techniques (like ControlNet) are largely open-sourced, fostering an extremely large and active developer and user community.
• Architectural Evolution:
  • SD 1.x (1.4, 1.5): Laid the foundation, used a BERT-based tokenizer and OpenAI CLIP ViT-L/14 text encoder. Relatively weaker at generating faces and following complex prompts.
  • SD 2.x (2.0, 2.1): Switched to the larger OpenCLIP-ViT/H text encoder aiming for better text understanding, but community reception was mixed. Due to changes in data filtering (removing many celebrity/artist names and NSFW content), ability to generate specific people and styles decreased.
  • SDXL (Stable Diffusion XL): A major upgrade. Features a much larger U-Net backbone (several times more parameters than 1.5/2.1 models) and innovatively uses two different text encoders (OpenCLIP ViT-G/14 and CLIP ViT-L/14) jointly for prompt encoding. This significantly improved detail, composition, aesthetic quality, and adherence to complex, long prompts. SDXL typically includes a Base Model and a Refiner Model for further detail enhancement.
  • Stable Diffusion 3 (SD3): (May be updated by release time) Reportedly uses a more advanced architecture (possibly incorporating ideas like Diffusion Transformers) and further increases model scale, text understanding, and image quality, especially in generating clear text and handling complex spatial relationships.
• Ecosystem: Extremely rich. Numerous open-source front-end interfaces (e.g., Automatic1111 WebUI, ComfyUI, InvokeAI, Fooocus), support for highly customizable workflows (adjusting samplers, steps, CFG Scale, VAE, etc.), and a vast collection of community-trained custom models (Checkpoints), LoRAs, Embeddings/Textual Inversions for generating diverse specific styles or subjects. This is its biggest advantage over closed-source models.
• Applications: Widely used in art creation, design assistance, game asset generation, virtual avatars, personalized product customization, etc.

2. DALL-E Series (OpenAI)

• Core Technology: DALL-E 2 and DALL-E 3 are based on diffusion models (specific architectural details not fully disclosed).
• Evolution:
  • DALL-E 1 (2021): Based on an autoregressive model (like GPT), treating images as sequences of discrete tokens. Impressive results but lower resolution.
  • DALL-E 2 (2022): Switched to a diffusion model architecture. Introduced CLIP to bridge text and image representations. It first generates a CLIP image embedding, uses a Prior model to map text embedding to image embedding, and finally a Decoder (diffusion-based) generates the image from the image embedding. Often used Cascaded Diffusion to enhance resolution.
  • DALL-E 3 (2023): Deep integration with ChatGPT (powered by GPT-4) is its main highlight. Users provide simple ideas, and ChatGPT automatically rewrites or expands them into more detailed, richer prompts (Prompt Expansion) before feeding them to the DALL-E 3 engine. This “built-in prompt engineer” mechanism greatly lowers the barrier to entry and significantly improves the model’s ability to understand and render complex, nuanced, even abstract concepts. DALL-E 3 excels at following instructions, generating text within images, and handling complex scenes and details.
• Characteristics:
  • Strong text understanding and instruction following (especially DALL-E 3).
  • High image quality and detail, relatively stable with details like faces and hands.
  • Ability to generate images containing clear, accurate text (a major breakthrough for DALL-E 3).
  • Generally good creative expression and concept combination abilities.
• Limitations:
  • Closed-source ecosystem: Users cannot access the underlying model, perform custom fine-tuning, or leverage community resources.
  • Relatively strict content policies and safety restrictions.
  • Limited workflow control options: Compared to the rich parameter tuning and workflow customization offered by the Stable Diffusion ecosystem, DALL-E typically provides a more streamlined interface with fewer controls.
• Applications: Integrated into ChatGPT Plus, Microsoft’s Copilot, and Bing Image Creator, offering easy-to-use AI image generation to a broad audience.

3. Midjourney

• Core Technology: Specific details undisclosed, but widely believed to be based on diffusion models combined with unique aesthetic preferences and data curation strategies.
• Characteristics: Renowned for generating images with exceptionally high artistic quality and a distinct, consistent aesthetic style. Midjourney images often excel in lighting, color, composition, atmosphere, and imagination, making it popular among artists and designers.
• Interaction Method: Primarily through a Discord bot. Users generate images using the /imagine command followed by a text prompt. Offers features like Variations, Upscaling, style tuning (--style), Image Prompting, and allows users to refine results through selection and iteration.
• Version Iteration: From V1 to V6 (and potential future versions), Midjourney has continuously and rapidly improved in image resolution, detail realism, text understanding (notably after V5), and style diversity.
• Strengths:
  • Top-tier aesthetic quality and artistic appeal.
  • Unique and consistent visual style (though some might see this as lacking diversity).
  • Relatively user-friendly interaction, especially for non-technical users.
• Limitations:
  • Closed-source, highly centralized: Users cannot control the underlying model or perform custom training.
  • Reliance on Discord platform: Interaction method is relatively singular.
  • Results sometimes overly “stylized”: Can be difficult to precisely control the generation of photorealistic or specific non-artistic styles.
  • Relatively strict content restrictions.
• Applications: Highly popular in art creation, concept design, illustration, game art, virtual scene building, etc.

4. Imagen & Imagen 2 (Google)

• Core Technology: Based on diffusion models, employing a Cascaded architecture.
• Characteristics:
  • Powerful Text Encoder: Imagen uses Google’s own powerful T5 large language model as its text encoder, considered key to its deep understanding of complex text prompts.
  • Cascaded Diffusion: Generates low-resolution images first, then uses a series of super-resolution diffusion models to progressively increase resolution, ensuring content consistency and detail quality.
  • High Photorealism: Excels at generating realistic, photorealistic images.
  • High Text-Image Alignment: Generated results usually reflect the details of the text prompt well.
  • Imagen 2 further improved performance and added features like image editing (Inpainting, Outpainting), logo generation, multi-language prompt support.
• Applications: Primarily integrated into Google’s Vertex AI platform and consumer products (like image generation in Google Gemini, images in Google SGE).

Summary: While mainstream text-to-image models converge on core technology (diffusion models), they differ significantly in architectural details, training data, text encoder choices, open-source strategy, community ecosystem, product positioning, and interaction methods, leading to a differentiated competitive landscape. Users need to weigh factors like desired image quality, artistic style, controllability, open-source freedom, ease of use, etc., against their needs and resources when choosing.

IV. Brief Introduction to Other Image Generation Technologies: A Diverse Landscape

Besides GANs and diffusion models, which dominate the current landscape, other important technological approaches exist in image generation. They might offer unique advantages in specific aspects or provide new directions for future development.

1. Autoregressive Models

• Core Idea: Treats image generation as a strict sequential generation problem. Images are viewed as 1D sequences of pixels (or patches/tokens). Like LLMs generating text, they predict and generate the value of the next pixel (or token) one by one in a fixed order (e.g., row by row), with each prediction depending on all previously generated pixels.
• Representative Models:
  • PixelRNN / PixelCNN: Early examples using RNN and CNN architectures to model pixel dependencies.
  • VQ-VAE + Autoregressive Prior: A more modern approach first uses a Vector Quantized Variational Autoencoder (VQ-VAE) to compress an image into a discrete, low-dimensional sequence of latent codes. Then, a powerful autoregressive model (like a Transformer) is trained to generate this sequence of codes. Finally, the VQ-VAE’s decoder decodes the generated code sequence back into a full image. DALL-E 1 followed this approach.
• Strengths:
  • Theoretically powerful expressiveness: Can model arbitrarily complex data probability distributions exactly.
  • High generation quality: Often produces detailed images with good internal consistency.
  • Relatively stable training: No adversarial training needed.
  • Easy likelihood computation: Can directly evaluate how well the model fits the data.
• Weaknesses:
  • Extremely slow generation: Generating pixel by pixel (or token by token) makes generating high-resolution images very time-consuming, much slower than GANs or accelerated diffusion models. Computational complexity is often proportional to or higher than the number of pixels.
  • Lack of global perspective: The strict sequential generation might struggle with capturing overall image structure.

2. Flow-based Models

• Core Idea: Based on the principle of Invertible Transformations. They construct a series of complex but mathematically guaranteed invertible neural network layers to learn an exact mapping between a simple, known probability distribution (like a standard Gaussian, i.e., pure random noise) and the complex data distribution (like real images).
• Representative Models: NICE, RealNVP, Glow, Flow++.
• Working Principle:
  • Generation: Sample a point from the simple distribution (e.g., Gaussian noise), then pass it through the series of invertible network layers (each with a precisely computable Jacobian determinant) to transform it into a sample belonging to the target data distribution (e.g., an image).
  • Training: Leveraging invertibility, they can exactly compute the probability density (likelihood) of any real data sample under the simple distribution. The training objective is to maximize the likelihood of the training data.
• Strengths:
  • Exact likelihood computation: Their biggest theoretical advantage, making model evaluation and comparison straightforward.
  • Well-behaved latent space: One-to-one mapping between latent representation and data allows for meaningful interpolation, attribute editing, etc.
  • Invertible and efficient generation: Generation (inference) typically requires only one forward pass, relatively fast.
  • Stable training: No adversarial training needed.
• Weaknesses:
  • Restricted network architectures: The need for invertibility imposes strict constraints on layer design (e.g., Coupling Layers), potentially limiting the model’s expressive power.
  • Computational cost: Computing Jacobian determinants can be expensive for high-dimensional data like images.
  • Generation quality: Although improving, often still slightly lags behind top GANs and diffusion models in terms of visual realism.
• Applications: More commonly used in density estimation, anomaly detection, speech synthesis (e.g., WaveGlow).

3. VQ-VAE (Vector Quantized Variational Autoencoder) & VQGAN

• Core Idea: VQ-VAE combines ideas from Variational Autoencoders (VAEs) and Vector Quantization. It inserts a discrete “Codebook” between the VAE’s encoder and decoder. The encoder compresses the input image into a continuous latent representation, which is then forced to map to the closest discrete code vector in the codebook. The decoder reconstructs the image from this discrete code.
• VQGAN: An important improvement over VQ-VAE, adding a GAN discriminator (and perceptual loss) after the decoder to enhance the detail and realism of the reconstructed image, enabling high-quality reconstruction even from highly compressed discrete latent spaces.
• Combination with Autoregressive Models: VQ-VAE/VQGAN itself is primarily for image compression and reconstruction. To generate new images, one typically trains a powerful Prior Model (like PixelCNN or Transformer) to learn the distribution of these discrete codes. For generation, the prior model generates a sequence of codes, which is then fed to the VQ-VAE/VQGAN decoder. The Taming Transformers paper demonstrated this powerful VQGAN + Transformer combination.
• Strengths:
  • Learned discrete latent representations are well-suited for subsequent modeling by powerful sequence models (like Transformers).
  • VQGAN can achieve very high compression rates while maintaining good reconstruction quality.
• Weaknesses:
  • Training involves two stages (train VQ-VAE/VQGAN, then train prior), more complex.
  • Reconstruction quality depends on the codebook size and training effectiveness.

4. Energy-Based Models (EBMs)

• Core Idea: EBMs don’t directly model the data probability density function p(x). Instead, they define an Energy Function E(x) such that low energy corresponds to high probability density (more likely samples) and high energy corresponds to low probability density. The relationship is often expressed as p(x) = \frac{e^{-E(x)}}{Z}, where Z = \int e^{-E(x)} dx is the normalization constant (partition function), usually intractable to compute.
• Training: Training aims to adjust the energy function so that real data samples have low energy, while other (non-real or generated) samples have high energy. This is often done using methods like Contrastive Divergence or Score Matching (theoretically related to diffusion models).
• Generation: Sampling from an EBM typically requires Markov Chain Monte Carlo (MCMC) methods (like Langevin Dynamics), starting from a random state and iteratively moving towards lower energy regions based on the energy function’s gradient, eventually yielding a high-probability sample.
• Strengths:
  • High modeling flexibility: Can define very complex energy functions to capture data distributions.
  • Easy composition: Multiple energy terms (corresponding to different attributes or constraints) can be easily combined.
• Weaknesses:
  • Difficult and slow sampling: MCMC sampling is often computationally expensive and slow to converge.
  • Training instability: Training can be difficult to converge or get stuck in local optima.
  • Intractable normalization constant Z: Makes direct likelihood computation very difficult.

Summary: Beyond GANs and diffusion models, technologies like autoregressive models, flow-based models, VQ-VAE/VQGAN, and EBMs offer different perspectives and tools for AI image generation. While diffusion models currently excel in mainstream tasks like text-to-image, these other approaches continue to evolve and often inspire or fuse with each other. For example, VQ-VAE can combine with autoregressive or diffusion models; diffusion models are theoretically linked to score matching (an EBM training method); GAN discriminator ideas are used to improve other models (like VQGAN). Future image generation technology will likely involve even more clever and efficient combinations of these different paradigms.

Conclusion: Understanding the Engine Enables Wise Use and Risk Avoidance

Generative Adversarial Networks (GANs) and Diffusion Models represent the two major technological waves in AI image generation. Through fundamentally different philosophies—adversarial play versus gradual denoising—they achieve astonishing “creation ex nihilo” capabilities. Understanding their basic working principles—the “cat and mouse game” of GANs’ generator and discriminator, and the iterative “sculpting” of clear images from chaotic noise in diffusion models—holds significant practical meaning for legal professionals:

• Deeper Evaluation of Text-to-Image Tools’ Capabilities and Limitations: Understand why AI can “draw” from text, but also why it sometimes generates unexpected, flawed, or stylistically odd results. Recognize the characteristics of different technical routes (e.g., GANs faster but potentially less diverse, diffusion models higher quality but potentially slower).
• Keener Identification of Potential Legal and Ethical Risks:
  • Copyright Ownership: Who owns the copyright to AI-generated images? Does it infringe copyright in the training data? This is an urgent question for the legal field.
  • Deepfakes and Evidence Credibility: Realistic AI-generated images (especially faces) can be used to create fake evidence, commit identity fraud, or spread disinformation, threatening judicial fairness and social trust.
  • Content Bias and Discrimination: AI-generated content might reflect or even amplify societal biases present in training data (e.g., stereotypes about certain groups).
  • Disinformation and Infringement: Generating fictional scenes, events, or defamatory images could lead to tort liability.
• More Prudent Exploration of Application Boundaries in Legal Practice:
  • Visualization Aid: Explore using text-to-image for non-evidentiary, comprehension-aiding simulations like scene diagrams, accident sketches, or visual summaries, but always explicitly stating limitations, non-authenticity, and strictly guarding against misleading effects.
  • Legal Education and Training: Creating visual materials for teaching cases.
  • IP Evidence Gathering Assistance: (Future potential) Analyzing images to determine if they are AI-generated or trace their possible origins (requires specialized techniques).
  • Scrutiny of AI-Generated Evidence: Lawyers and judges need enhanced ability to discern AI-generated images and seek expert help when necessary to assess evidence authenticity.

As AI image generation technology becomes more accessible and capable, its impact on the legal field will inevitably deepen. Legal professionals must keep pace, acquiring the necessary technical understanding to wisely leverage its advantages, prudently mitigate its risks, and participate in shaping relevant legal rules and regulations, ensuring technology’s development remains firmly on the track of the rule of law.