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A - A new imaginary
Every work, every action, and every word we express is the product of accumulated knowledge and past experiences. This seemingly simple principle reveals a fundamental truth: nothing we consider new is truly devoid of roots in the past. Every idea, every concept, every creative form that emerges is the result of a complex interaction between what we already know and what we are able to synthesize from this knowledge.
Analyzing this phenomenon in more rational terms, we can observe that innovation is not an isolated act but rather a process of evolution. Every new creation is intrinsically linked to existing patterns, models, and structures. These preexisting elements form a sort of matrix that conditions and guides our ability to create something new. Therefore, there is no real discontinuity between the past and the present; instead, there is a continuity that manifests through the transformation and adaptation of existing knowledge.
In this context, art, writing, and every other human practice can be understood as expressions of a progressive synthesis. Every creative act is, in effect, a process of reworking, where known elements are combined in new ways to generate seemingly original results. This process is driven by a constant interaction between past experience and the human ability to imagine new possibilities.
From a theoretical standpoint, one might argue that the idea of a completely original creation is, in a sense, an illusion. What we perceive as new is actually a novel configuration of preexisting elements. Thus, every innovation is the result of a complex network of influences, knowledge, and patterns that intersect, creating a continuum between the old and the new.
Generative artificial intelligence, especially in the realm of image creation, represents a technology that replicates, in an essentially technical manner, the human creative process. The algorithms that generate new images operate by processing vast amounts of preexisting data (images, styles, models), which are used to train the underlying systems responsible for creation. This process is not unlike the mechanism described earlier: nothing is created ex nihilo, but rather, it is the result of a synthesis of already existing information and patterns.
It is important to emphasize that these creations, although they may appear original, are closely tied to the data used to train the model. The algorithms do not possess creativity of their own (in the human sense of the term), but operate according to precise mathematical rules that determine how to manipulate and synthesize the inputs. This process, while sophisticated, is inherently limited. Artificial intelligence generates new variations and solutions, but always within the boundaries imposed by the initial data and the programmed instructions, which define the space of possibilities.
This approach, though more technical and less emotional compared to the human creative process, is no less valid. In fact, the strength of artificial intelligence lies in its capacity for processing and synthesis, which increases proportionally with the amount of available data. The more data provided, the greater the model's ability to generate complex and diverse solutions. This process can be compared to the human ability to assimilate knowledge from multiple experiences and contexts, even if in a less flexible and less emotionally nuanced way.
That said, the process differs substantially from the human experience of creation. A human being, during the creative act, does not merely rework information. Human creativity is immersed in a complex interplay of experiences, emotions, intuitions, and sensory stimuli that continuously interact. This is a phenomenon that goes beyond simple data synthesis. Every creative choice is the product of a dynamic process, influenced by a constant and varied accumulation of experiences. In contrast, artificial intelligence operates within a closed system, where the final result is predetermined by the input data and the model's rules. These rules cannot fully replicate the spontaneity and complexity of human intuition.
However, one cannot deny the allure this technology holds, especially for those who see in creativity a desire for total control over the work. The ability to use artificial intelligence to generate images that faithfully reflect given instructions, according to predefined parameters, offers a form of control rarely achieved in the human creative process, where unpredictability and instinct play a significant role. This raises an interesting question: if artificial intelligence can produce results very similar to those achievable by a human, albeit through different mechanisms, does it not, in part, realize the ambition of every creative mind to dominate and direct the creative act?
B - Inside the generative productions
In his seminal essay The Work of Art in the Age of Mechanical Reproduction, Walter Benjamin discusses how technological advancements, such as photography and cinema, have transformed the aura and authenticity of artworks. His insights are remarkably relevant today, as the concept of originality in the production of visual images related to a given reality is once again being challenged by generative technologies.
Benjamin observes that "what withers in the age of mechanical reproduction is the aura of the work of art" (Benjamin, 1936). With the advent of mechanical reproduction, the authenticity of the artwork is lost as it becomes separated from its original historical and cultural context. However, it also acquires a new kind of originality, inherent in the multiplicity of its reproductions. A similar phenomenon can be observed in generative images created by artificial intelligence, where reproduction does not merely copy but also transforms and reinterprets reality.
Similarly, another philosopher, Gilles Deleuze, through his work on simulacra and simulation, offers a complex perspective on the nature of reality and representation in the digital media age. Deleuze helps us understand how AI-generated images do not simply reproduce reality, but transfigure it, drawing from vast datasets to create new layers of meaning and reinterpretation. His concepts of "folds" and "flows" of information apply well to the dynamic and fluid nature of AI-generated images, which are not fixed representations but resemble a flexible material, constantly changing and open to reinterpretation.
Some programmers and prominent figures in Silicon Valley, such as Ian Goodfellow, the creator of GANs, and Fei-Fei Li, a pioneer in computer vision, have indirectly made significant conceptual contributions to the production of generative images through their work. In particular, they use deep neural networks to process large volumes of visual data. Machine learning algorithms, such as generative adversarial networks (GANs), create images that another network then attempts to distinguish from real ones. This adversarial process leads to the generation of increasingly realistic and detailed images.
Large datasets, such as ImageNet, provide a vast array of labeled images across countless categories. These datasets serve as the foundation for training deep neural networks, enabling models to learn from the visual variables embedded in the images. In a way, these databases act as a vast "cauldron" in which different characteristics of images are blended and recombined according to a specific logic determined by machine learning algorithms.
As we move forward, our focus remains on the intellectual and aesthetic implications of AI in art, examining how these technologies are redefining creativity and challenging traditional notions of authorship and originality.
C - Generative constructions
Building is an act that goes far beyond the mere assembly of parts. Derived from the Latin construĕre, meaning "to put together" or "to build," the term itself embodies a fundamental idea: the creation of a new, ordered, and functional entity. In a world where the concept of building applies not only to physical structures but also to ideas, relationships, and social frameworks, understanding the true essence of building allows us to view the process as a form of art. It is an art governed by precise and intentional plans, capable of bringing order to what was once chaos.
The notion of structure is intimately linked to that of building. In architecture, for instance, structure represents the system that supports the entire edifice, an organized arrangement of elements that ensures solidity, stability, and functionality. More broadly, the concept of structure can be applied to any domain in which there is an ordered set of components working together toward a common goal, from biology to linguistics, and from logic to mathematics.
Structure, therefore, is not merely the physical arrangement of elements, but the functional interaction between them. This interrelation defines the system’s emergent properties, those characteristics that cannot be reduced to the simple sum of the parts. It is through structure that building comes to life and acquires meaning.
D -The Metamorphosis of the digital aura
The concept of aura, long understood as the ineffable quality emanating from a singular artwork in its original physical context, is undergoing a radical transfiguration in the age of algorithmic generation.
Once rooted in the tangible presence of an artistic piece, the aura now migrates into the elusive territory of data, code, and computational processes. Rather than dissolving into obsolescence, it assumes novel shapes and modalities, shaped by continuous streams of emergent images that flow across digital networks.
In traditional aesthetics, aura was tethered to the irreproducibility of the artifact and the viewer's immediate interaction with it. Now, neural networks and generative algorithms open an almost boundless repertoire of possible iterations, shifting the locus of creativity from the singular event of manual craftsmanship to the iterative output of computational logic. Yet what appears at first to be an endless proliferation of copies can paradoxically engender a new kind of aura, one rooted not in the uniqueness of a material object but in the capacity of the algorithm to produce unprecedented forms that retain a vestige of mystery.
Such an aura, however, remains fragile and context-dependent. As images are disseminated in digital environments, the boundaries between original and copy grow increasingly porous.
The aura manifests in fleeting moments, when an image resonates with the viewer's sensibility, before it is subsumed into the mass of data that undergirds all subsequent variations. In this sense, the aura becomes an event: it emerges through perception, interpretation, and the algorithm’s ever-active reconfiguration of visual elements.
Ultimately, the digital aura recasts the traditional notion of artistic authenticity, unsettling entrenched ideas of authorship, originality, and the singular artwork. It is both liberated and threatened by the fluidity of generative production. This metamorphosis shows that the "presence" of art is no longer anchored to a single place or medium, but instead inhabits a dynamic, ever-expanding ecosystem of images, data, and human engagement.
E - Technical processes
Generative image models based on text descriptions leverage a combination of large language models and diffusion techniques to produce high-fidelity images that align semantically with the provided input.
Their architecture is built around an advanced text encoder, typically a pretrained language model trained exclusively on textual data rather than on direct image-text pairs. This approach has proven highly effective, as scaling the language model significantly enhances both image quality and semantic consistency with the input prompt. In fact, it often yields better results than increasing the complexity of the diffusion model itself.
The generation process begins by converting the input text into a numerical representation, a sequence of embeddings, which serves as conditioning input for the diffusion model.
The diffusion model operates by iteratively denoising an initially random noise image, progressively guiding the pixel distribution toward a configuration that accurately reflects the semantic content of the input text.
To further refine text-image alignment, a technique known as classifier-free guidance is often employed. This method amplifies the most relevant features of the textual input without compromising visual fidelity.
Once a preliminary low-resolution image is generated, it undergoes a series of refinements through a cascade of super-resolution diffusion models. These stages progressively enhance detail and quality until a high-resolution output is achieved.
This upscaling process is optimized using noise conditioning augmentation, a technique that introduces controlled noise into intermediate images to improve robustness and mitigate artifacts inherited from lower-resolution stages. Additionally, the underlying neural architecture is often fine-tuned through optimized network structures such as Efficient U-Net, which enhances computational efficiency, reduces memory consumption, and accelerates training without sacrificing output quality.
The performance of these models is rigorously evaluated using industry-standard benchmarks and perceptual assessment tests, measuring both visual quality and text-image alignment. To ensure a comprehensive evaluation, structured benchmarking frameworks analyze the model’s ability to generate semantically coherent images for complex prompts, assessing aspects such as spatial relationships, compositionality, and semantic accuracy.
Results consistently demonstrate that models integrating advanced linguistic representations with diffusion techniques surpass previous approaches in terms of both photorealism and alignment fidelity.
Despite their cutting-edge performance, these models present inherent challenges related to representation and fairness. Training on vast text and image datasets, often sourced from uncurated online content, can introduce social and cultural biases into the generated imagery.
Notably, models have shown a tendency to reinforce preexisting stereotypes and exhibit demographic imbalances, particularly in images depicting people. These limitations highlight the ongoing challenge of achieving truly diverse and unbiased representations in generative models.
class ImageGenerationSimulator:
def __init__(self, prompt): self.prompt = prompt; self.embeddings = None; self.context = None; self.noise = None; self.low_res_image = None; self.final_image = None
def text_embedding(self): print("Step 1: Generating Text Embeddings..."); time.sleep(1); self.embeddings = np.random.rand(512); print("→ Text Embeddings Created.")
def semantic_understanding(self): print("Step 2: Extracting Context from Prompt..."); time.sleep(1); self.context = f"Context extracted from: {self.prompt}"; print(f"→ Context: {self.context}")
def diffusion_initialization(self): print("Step 3: Initializing Diffusion Model..."); time.sleep(1); self.noise = np.random.randn(64, 64); print("→ Diffusion Noise Initialized.")
def iterative_denoising(self): print("Step 4: Running Iterative Denoising..."); time.sleep(2); self.low_res_image = np.clip(self.noise + 0.5, 0, 1); print("→ Denoising Completed.")
def super_resolution(self): print("Step 5: Super-Resolution Steps (64x64 → 1024x1024)..."); time.sleep(2); self.final_image = np.kron(self.low_res_image, np.ones((16, 16))); print("→ Super-Resolution Completed.")
def post_processing(self): print("Step 6: Artifact Removal & Noise Conditioning..."); time.sleep(1); self.final_image = np.clip(self.final_image, 0, 1); print("→ Image Cleaned.")
“import numpy as np
import time
import matplotlib.pyplot as plt
Class Image Generation:
def __init__(self, prompt):
self.prompt = prompt
self.embeddings = None
self.context = None
self.noise = None
self.low_res_image = None
self.final_image = None
def text_embedding(self):
print("Step 1: Generating Text Embeddings...")
time.sleep(1)
self.embeddings = np.random.rand(512) # Simulating text embeddings
print("→ Text Embeddings Created.")
def optimization(self): print("Step 7: Memory & Speed Optimization..."); time.sleep(1); print("→ Optimizations Applied.")
def coherence_check(self): print("Step 8: Performing Visual & Semantic Coherence Check..."); time.sleep(1); print("→ Coherence Verified.")
def benchmarking(self): print("Step 9: Running Benchmarking (FID, CLIP)..."); time.sleep(1); print("→ Benchmark Score: FID ~15.4, CLIP Similarity ~0.78")
def bias_analysis(self): print("Step 10: Conducting Bias & Fairness Analysis..."); time.sleep(1); print("→ Bias Analysis Completed. No significant biases detected.")
def generate(self): self.text_embedding(); self.semantic_understanding(); self.diffusion_initialization(); self.iterative_denoising(); self.super_resolution(); self.post_processing(); self.optimization(); self.coherence_check(); self.benchmarking(); self.bias_analysis(); print("Final Step: Rendering Image..."); plt.imshow(self.final_image, cmap='gray'); plt.title("Simulated Generated Image"); plt.axis('off'); plt.show(); print("→ Image Successfully Generated.")
prompt = "A futuristic city at sunset with flying cars."; simulator = ImageGenerationSimulator(prompt); simulator.generate()
def semantic_understanding(self):
print("Step 2: Extracting Context from Prompt...")
time.sleep(1)
self.context = f"Context extracted from: {self.prompt}"
print(f"→ Context: {self.context}")
def diffusion_initialization(self):
print("Step 3: Initializing Diffusion Model...")
time.sleep(1)
self.noise = np.random.randn(64, 64) # Simulated noise initialization
print("→ Diffusion Noise Initialized.")
def iterative_denoising(self):
print("Step 4: Running Iterative Denoising...")
time.sleep(2)
self.low_res_image = np.clip(self.noise + 0.5, 0, 1) # Simulating denoising process
print("→ Denoising Completed.")
Image Generation
prompt = "A futuristic city at sunset with flying cars."
simulator = ImageGenerationSimulator(prompt)
simulator.generate()”
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