Cards-A3 - Revision history

NicolasMaleve: /* Pixel Space */

2025-09-16T13:40:44Z

Pixel Space

← Older revision		Revision as of 15:40, 16 September 2025
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	Objects, however, also contain an associative power, that literally can create memories and make a story come alive. This catalogue is therefore not just a collection of the objects that makes generative AI images, but an exploration of an imaginary of AI image creation through the collection and exhibition of objects – and in particular, an imaginary of ‘autonomy’ from mainstream capital platforms.		Objects, however, also contain an associative power, that literally can create memories and make a story come alive. This catalogue is therefore not just a collection of the objects that makes generative AI images, but an exploration of an imaginary of AI image creation through the collection and exhibition of objects – and in particular, an imaginary of ‘autonomy’ from mainstream capital platforms.

	~~'''Remark: The above text is integrated in the page as is.'''~~

	=== Maps ===		=== Maps ===
	There is little knowledge of what AI really looks like. The maps presented here are an attempt to abstract the different objects that one may come across when entering the world of autonomous and decentralised AI image creation. It can serve as a useful guide to experience what the objects of this world are called, how they connect each other, to communities or underlying infrastructures – perhaps also as an outset for one's own exploration.		There is little knowledge of what AI really looks like. The maps presented here are an attempt to abstract the different objects that one may come across when entering the world of autonomous and decentralised AI image creation. It can serve as a useful guide to experience what the objects of this world are called, how they connect each other, to communities or underlying infrastructures – perhaps also as an outset for one's own exploration.
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	Both images and LoRAs are organised and shared on dedicated platforms (e.g., Danbooru or CivitAI). The generation of images and use of GPU/hardware can also be distributed to a community of users in a peer-to-peer network (Stable Horde). This points to how models, software, datasets and other objects always also exist suspended between different planes of dependencies - organisational, material, or other.		Both images and LoRAs are organised and shared on dedicated platforms (e.g., Danbooru or CivitAI). The generation of images and use of GPU/hardware can also be distributed to a community of users in a peer-to-peer network (Stable Horde). This points to how models, software, datasets and other objects always also exist suspended between different planes of dependencies - organisational, material, or other.

	~~'''Remark: The above text is integrated in the page as is.'''~~

	=== Pixel Space ===		=== Pixel Space ===

NicolasMaleve: /* Maps */

2025-09-16T13:36:58Z

Maps

← Older revision		Revision as of 15:36, 16 September 2025
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	Both images and LoRAs are organised and shared on dedicated platforms (e.g., Danbooru or CivitAI). The generation of images and use of GPU/hardware can also be distributed to a community of users in a peer-to-peer network (Stable Horde). This points to how models, software, datasets and other objects always also exist suspended between different planes of dependencies - organisational, material, or other.		Both images and LoRAs are organised and shared on dedicated platforms (e.g., Danbooru or CivitAI). The generation of images and use of GPU/hardware can also be distributed to a community of users in a peer-to-peer network (Stable Horde). This points to how models, software, datasets and other objects always also exist suspended between different planes of dependencies - organisational, material, or other.

	~~[Images~~: '~~Our~~' ~~map(s) - perhaps surround by other maps]~~		'''Remark: The above text is integrated in the page as is.'''

	=== Pixel Space ===		=== Pixel Space ===

NicolasMaleve: /* Objects of Interest and Necessity */

2025-09-16T13:34:26Z

Objects of Interest and Necessity

← Older revision		Revision as of 15:34, 16 September 2025
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	== Cards A3 - objects ==		== Cards A3 - objects ==
	~~'''Objects/Maps – Pixel Space/Latent space – Prompt/LoRA – GPU/Currency – Stable Horde/Hugging Face'''~~

	=== Objects of Interest and Necessity ===		=== Objects of Interest and Necessity ===
	Most people’s experiences with generative AI image creation come from platforms like OpenAI’s DALL-E or other services. Nevertheless, there are also communities who for different reasons seek some kind of independence and autonomy from the mainstream platforms. The outset for this catalogue is ‘Stable Diffusion’, a so-called Free and Open Source Software system for AI image creation.		Most people’s experiences with generative AI image creation come from platforms like OpenAI’s DALL-E or other services. Nevertheless, there are also communities who for different reasons seek some kind of independence and autonomy from the mainstream platforms. The outset for this catalogue is ‘Stable Diffusion’, a so-called Free and Open Source Software system for AI image creation.
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	Objects, however, also contain an associative power, that literally can create memories and make a story come alive. This catalogue is therefore not just a collection of the objects that makes generative AI images, but an exploration of an imaginary of AI image creation through the collection and exhibition of objects – and in particular, an imaginary of ‘autonomy’ from mainstream capital platforms.		Objects, however, also contain an associative power, that literally can create memories and make a story come alive. This catalogue is therefore not just a collection of the objects that makes generative AI images, but an exploration of an imaginary of AI image creation through the collection and exhibition of objects – and in particular, an imaginary of ‘autonomy’ from mainstream capital platforms.

	~~[IMAGES - CIGARETTE STUBS? + OTHER OBJECTS?]~~		'''Remark: The above text is integrated in the page as is.'''

	=== Maps ===		=== Maps ===

CUA at 13:09, 2 September 2025

2025-09-02T13:09:59Z

← Older revision		Revision as of 15:09, 2 September 2025
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	// THESE are the little text snippets to be used for editing A4 cards and A3 posters - to be used copy/paste-style		// THESE are the little text snippets to be used for editing A4 cards and A3 posters - to be used copy/paste-style

			NB! USE the [[Card texts]] for making cards/posters

			// Perhaps this page is to be deleted?

	== Cards A3 - objects ==		== Cards A3 - objects ==

CUA at 11:29, 2 September 2025

2025-09-02T11:29:47Z

← Older revision		Revision as of 13:29, 2 September 2025
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	[[Category:Objects of Interest and Necessity]]		[[Category:Objects of Interest and Necessity]]

	// THESE are the little text snippets ~~on the~~ A3 ~~cards~~ - to be used copy/paste-style		// THESE are the little text snippets to be used for editing A4 cards and A3 posters - to be used copy/paste-style

	== Cards A3 - objects ==		== Cards A3 - objects ==

CUA at 09:34, 29 August 2025

2025-08-29T09:34:21Z

← Older revision		Revision as of 11:34, 29 August 2025
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	[[Category:Objects of Interest and Necessity]]		[[Category:Objects of Interest and Necessity]]

			// THESE are the little text snippets on the A3 cards - to be used copy/paste-style

	== Cards A3 - objects ==		== Cards A3 - objects ==

CUA: /* Cards A3 - objects */

2025-08-29T07:45:28Z

Cards A3 - objects

← Older revision		Revision as of 09:45, 29 August 2025
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	== Cards A3 - objects ==		== Cards A3 - objects ==
			'''Objects/Maps – Pixel Space/Latent space – Prompt/LoRA – GPU/Currency – Stable Horde/Hugging Face'''

	=== '''Maps~~'''~~ ===		=== Objects of Interest and Necessity ===
			Most people’s experiences with generative AI image creation come from platforms like OpenAI’s DALL-E or other services. Nevertheless, there are also communities who for different reasons seek some kind of independence and autonomy from the mainstream platforms. The outset for this catalogue is ‘Stable Diffusion’, a so-called Free and Open Source Software system for AI image creation.

			With the notion of an ‘object of interest’ a guided tour of a place, a museum or collection likely comes to mind. One may easily read this compilation of texts as a catalogue for such a tour in a social and technical system, where we stop and wonder about the different objects that, in one way or the other, take part in the generation of images with Stable Diffusion.

			'A guided tour' perhaps also limits the understanding of what objects of interest are? In science, for instance, an object of interest sometimes refers to what one might call the potentiality of an object. Take for instance, the famous Kepler telescope whose mission was to search the Milky Way for exoplanets (planets outside our own solar system). Among all the stars, there are candidates for this, or so-called Kepler Objects of Interest (KOI).

			In similar ways, this catalogue is the outcome of an investigative process where we – by trying out different software, reading documentation and research, looking into communities of practice that experiment with AI image creation, and more – have sought to understand the things that make generative AI images with Stable Diffusion possible. We have tried to describe not only the objects, but also their underlying dependencies on relations between communities, models, capital, technical units, and more.

			Objects, however, also contain an associative power, that literally can create memories and make a story come alive. This catalogue is therefore not just a collection of the objects that makes generative AI images, but an exploration of an imaginary of AI image creation through the collection and exhibition of objects – and in particular, an imaginary of ‘autonomy’ from mainstream capital platforms.

			[IMAGES - CIGARETTE STUBS? + OTHER OBJECTS?]

			=== Maps ===
	There is little knowledge of what AI really looks like. The maps presented here are an attempt to abstract the different objects that one may come across when entering the world of autonomous and decentralised AI image creation. It can serve as a useful guide to experience what the objects of this world are called, how they connect each other, to communities or underlying infrastructures – perhaps also as an outset for one's own exploration.		There is little knowledge of what AI really looks like. The maps presented here are an attempt to abstract the different objects that one may come across when entering the world of autonomous and decentralised AI image creation. It can serve as a useful guide to experience what the objects of this world are called, how they connect each other, to communities or underlying infrastructures – perhaps also as an outset for one's own exploration.
	A distinction between 'pixel space' and 'latent space' can be helpful. That is, what you see from what you do not see.		A distinction between 'pixel space' and 'latent space' can be helpful. That is, what you see from what you do not see.
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	[Images: 'Our' map(s) - perhaps surround by other maps]		[Images: 'Our' map(s) - perhaps surround by other maps]

	=== ~~'''~~Pixel Space~~'''~~ ===		=== Pixel Space ===
	In pixel space, you find a range of visible objects that a typical user would normally meet. This includes the [[interfaces]] for creating images. In conventional interfaces like DALL-E or Bing Image Creator, users [[prompt]] in order to generate images. What is particular for autonomous and decentralised AI image generation is that the interfaces have many more parameters and ways to interact with the models that generate the images. It functions more like an 'expert' interface.		In pixel space, you find a range of visible objects that a typical user would normally meet. This includes the [[interfaces]] for creating images. In conventional interfaces like DALL-E or Bing Image Creator, users [[prompt]] in order to generate images. What is particular for autonomous and decentralised AI image generation is that the interfaces have many more parameters and ways to interact with the models that generate the images. It functions more like an 'expert' interface.

CUA: /* Pixel Space */

2025-08-27T13:59:03Z

Pixel Space

← Older revision		Revision as of 15:59, 27 August 2025
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	A useful annotated and categorised dataset – be it for a foundation model or a LoRA – typically involves specialised knowledge of both the technical requirements of model training ([[latent space]]) and the aesthetics and cultural values of visual culture itself. For instance of common visual conventions, such as realism, beauty, horror, and also (in the making of LoRAs) of more specialised conventions – say a visual style that an artist or a cultural community want to generate.		A useful annotated and categorised dataset – be it for a foundation model or a LoRA – typically involves specialised knowledge of both the technical requirements of model training ([[latent space]]) and the aesthetics and cultural values of visual culture itself. For instance of common visual conventions, such as realism, beauty, horror, and also (in the making of LoRAs) of more specialised conventions – say a visual style that an artist or a cultural community want to generate.

			[IMAGES - SUGGESTIONS - VARIOUS INTERFACES + GENERATED IMAGES FROM CIVIT AI + DATASETS ]

	=== '''Latent space''' ===		=== '''Latent space''' ===

CUA: /* Latent space */

2025-08-27T13:57:03Z

Latent space

← Older revision		Revision as of 15:57, 27 August 2025
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	=== '''Latent space''' ===		=== '''Latent space''' ===
			[[Latent space]] is a highly abstract space consisting of compressed representations of images and texts. A key object is the [[Variational Autoencoder, VAE\|Variational Autoencoder (VAE)]] that makes the image-texts available to different kinds of operations – whose results are then decoded back into images. An important operation happening in the latent space is the training of an algorithm. In [[Diffusion\|diffusion-based]] algorithms, the algorithm is trained by learning to apply noise to an image and then reconstruct an image, from complete or random noise (this process is discussed more in-dept in the entry on [[diffusion]]).

			To continue our mapping, it is important to note that the latent space is nurtured by various sources. In the process of model training, [[Dataset\|datasets]] play a crucial role. Many of the datasets that are used to train models are made by 'scraping' the internet, while others are built on repositories like Instagram, flickr, or Getty images. Open Images and ImageNet are commonly used as the backbone of visually training generative AI, built on web-pages, but corporate organisations like Meta and Google also offer open source datasets, as do e.g., research institutions and others. Contrary to common belief, there is not just one dataset used to make a model work, but multiple models and datasets to, for instance, reconstruct missing facial or other bodily details (such as too many fingers on one hand), 'upscale' images of low resolution or 'refine' the details in the image. [[LoRA\|LoRAs]] trained on users own curated datasets are also often used in AI imaging with Stable Diffusion. The latent space is therefore an interpretation of a large pool of visual and textual resources, external to it.

			When it comes to autonomous AI imaging, there is typically an organisation and a community behind each dataset and training. [[LAION]] (Large-scale-Artificial Intelligence Open Network) is a good example of this, and a very important one. It is a non-profit community organisation that develops and offers free models and datasets. Stable Diffusion was trained on datasets created by LAION, using Common Crawl (another non-profit organisation that has built a repository of 250 billion web pages) and [[Clip\|CLIP]] (OpenAI's neural network which learns visual concepts from natural language supervision) to compile an extensive record of links to images with 'alt text' (a descriptive text for non-text content, created by 'web masters' for increased accessibility) – that is a useful set of annotated images, to be used for model training. We begin to see that a model's dependencies have large organisational, social and technical ramifications.

	=== '''Prompt''' ===		=== '''Prompt''' ===

CUA: /* Currency */

2025-08-27T13:55:59Z

Currency

← Older revision		Revision as of 15:55, 27 August 2025
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	The Graphics Processing Unit (GPU) is an electronic circuit for processing computer graphics. It was designed to add graphical interfaces to computing devices and expand the possibilities for interaction. While commonly used for videogames or 3D computer graphics enterprises, the GPU's particular way of computing have made it an object of interest and necessity for the cryptocurrency rush and, more recently, for training and use of generative AI. One of the more material elements of our map, it is often an invisible hardware, playing a crucial role of translating between visual and textual human-understanding and computer models for prediction (for example, between existing images and the "calculation" of new ones). The GPU is both an object that sits next to a desktop computer, and that populates massive cloud data centres racing towards the latest flagship large language model. In a way, a domestic object, used by enthusiast to play and modify the landscape of AI, and the holy grail of the big tech AI industry.		The Graphics Processing Unit (GPU) is an electronic circuit for processing computer graphics. It was designed to add graphical interfaces to computing devices and expand the possibilities for interaction. While commonly used for videogames or 3D computer graphics enterprises, the GPU's particular way of computing have made it an object of interest and necessity for the cryptocurrency rush and, more recently, for training and use of generative AI. One of the more material elements of our map, it is often an invisible hardware, playing a crucial role of translating between visual and textual human-understanding and computer models for prediction (for example, between existing images and the "calculation" of new ones). The GPU is both an object that sits next to a desktop computer, and that populates massive cloud data centres racing towards the latest flagship large language model. In a way, a domestic object, used by enthusiast to play and modify the landscape of AI, and the holy grail of the big tech AI industry.

	=== '''~~Currency~~''' ===		=== '''Currencies''' ===
	While a familiar object, currencies can take wild shapes. Traditionally, a currency is a material medium (commonly, metal or paper) of exchange: coins and bills are one still a common form of currency. Digital platforms and services, however, have multiplied the forms these exchange objects can take. Videogames and blockchain technology have helped to explode what can be understood as currency, and have subverted the dependants on larger organisations: if traditionally states, banks, and large organisations designed and managed these objects, their digital counterparts tend to be more untamed and sometimes community orientated. Autonomous currencies have always existed, as exchange modes for small communities or explicit counter-objects to hegemonic economies, but their digital versions are easy to set-up, distribute, and share with communities around the world. Within our objects of interest, digital currencies make possible to directly generate images, or commodify the possibility of new ones (for example, as a bounty for the ones creating or fine-tuning models). Immaterial versions of currencies also act as the exchange network for sharing non-hegemonic networks of image generation, adding value and circulation to a larger infrastructure of imaginaries of autonomy.		While a familiar object, currencies can take wild shapes. Traditionally, a currency is a material medium (commonly, metal or paper) of exchange: coins and bills are one still a common form of currency. Digital platforms and services, however, have multiplied the forms these exchange objects can take. Videogames and blockchain technology have helped to explode what can be understood as currency, and have subverted the dependants on larger organisations: if traditionally states, banks, and large organisations designed and managed these objects, their digital counterparts tend to be more untamed and sometimes community orientated. Autonomous currencies have always existed, as exchange modes for small communities or explicit counter-objects to hegemonic economies, but their digital versions are easy to set-up, distribute, and share with communities around the world. Within our objects of interest, digital currencies make possible to directly generate images, or commodify the possibility of new ones (for example, as a bounty for the ones creating or fine-tuning models). Immaterial versions of currencies also act as the exchange network for sharing non-hegemonic networks of image generation, adding value and circulation to a larger infrastructure of imaginaries of autonomy.