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    KERRAYAI Art - Tvorba za pomocí umělé inteligence: Midjourney, DALL·E 2, Stable Diffusion, OpenAI
    Vše ohledně umění tvořeného pomocí umělé inteligence - obrázky, 'fotky', galerie, hudba, video, text + články, novinky apod.

    NSFW obsah prosím obalit spoiler tagem - <div class="spoiler">obrázek</spoiler>, a nemá to tu být klub na roštěnky a nahotinky bez nějaké přidané hodnoty

    Příbuzné diskuze:
    - [DALL·E mini i Craiyon - having sex with AI since [date format unknown]]
    - [I Hope This Does Not Exist ​ ​ ​ ▌​ ​ Vedlejší efekty v AI visuálech]
    - AI obecně [Artificial Intelligence AI]
    - Vtipy [Umělá inteligence, chatboti - vtipné konverzace aneb "Hoří hovno?"]
    - [generativní modely] Jak konstruovat prompty, kde získat váhy i jak to vše interpretovat


    Prosím zkusme pro vkládanou tvorbu používat tagy
    #galerie (2-3 obrázky na ukázku, další po rozkliku) #obrázek #video #hudba #text #hry #původní (pro vlastní tvorbu) #roštěnky

    #článek #nástroj

    (návrhy na další tagy apod. vítány)

    Texty, programování: https://beta.openai.com/playground | https://chat.openai.com/
    Obrázky online: https://www.midjourney.com/ | https://beta.dreamstudio.ai/
    Lokálně: https://github.com/AUTOMATIC1111/stable-diffusion-webui | https://github.com/invoke-ai/InvokeAI | Civitai repository custom modelů pro SD
    AI na vytvoření textového zadání z existujícího obrázku: https://huggingface.co/spaces/pharma/CLIP-Interrogator
    rozbalit záhlaví
    E2E4
    E2E4 --- ---
    E2E4: (pro ty ilustrační obrázky stačí free nástroje)
    E2E4
    E2E4 --- ---
    LA_PIOVRA: záleží na co to potřebuje, pro profesionální práci midjourney, pro nějaký ilustrační obrázky kde nepotřebuješ aby mely stejný styl/postavy..

    rovozovat to sám je trošku zbytečná technická challenge.

    přístup k různým free modelům je na Poe.com, i v neplacený verzi, stačí jen google účet. tím bych asi začal.
    FRK_R23
    FRK_R23 --- ---
    LA_PIOVRA: https://civitai.com/

    Tady si můžeš stahovat mraky base modelů a nepřeberný množství LoRA.
    FRK_R23
    FRK_R23 --- ---
    LA_PIOVRA: Fooocus je poměrně versatilní a hlavně běží lokálně na tvym GPU. Takže ti nikdy nedojdou kredity :)

    GitHub - lllyasviel/Fooocus: Focus on prompting and generating
    https://github.com/lllyasviel/Fooocus
    PEETIK
    PEETIK --- ---
    Dnes sem zjistil, že MJ umí udržovat obličeje ze vzorové fotky. Cool :)
    KILLUA
    KILLUA --- ---
    LA_PIOVRA: Když už at si pořídí DALLE3 protože bude mít v ceně i GPT4
    MORPHLER
    MORPHLER --- ---
    LA_PIOVRA: nejlepsi je strasne siroky pojem. je toho hodne. pres web je free treba od MS https://www.bing.com/images/create a lokalne nejaky SD (napr. https://github.com/AUTOMATIC1111/stable-diffusion-webui)
    LA_PIOVRA
    LA_PIOVRA --- ---
    Ptam se pro kamarada, jaky je v soucasnosti nejlepsi free nastroj na generovani obrazku?
    Nebo si ma poridit Midjourney?
    ARAON
    ARAON --- ---
    Tohle asi taky patri sem...
    Suno AI
    https://www.suno.ai/
    EADENN
    EADENN --- ---
    Ahoj, nemáte někdo přehled, jestli existuje AI generátor hudby, který by nějakým způsobem chápal video stopu a přizpůsobil tomu výsledek? Samozřejmě ne obsahově, ale že by si třeba všimnul střihu, změny grafiky apod. a přizpůsobil tomu dejme tomu rytmus?
    MATT
    MATT --- ---
    takovou zhuleneckou logikou jsem si vymyslel, jak by mohli generatory obrazku fungovat, tak by me zajimalo, jak moc je ta idea mimo.. ;)

    Inspirovalo me video od Moulda o rezonancnich frekvencich, kdy foukne do lahve bilej sum a lahev se rozezni rezonancni frekvenci. K tomu jsem si vzpomel, ze nejaka komprese obrazku funguje na principu Furrierovyho rozkladu a ze obrazky se generujou z pocatecniho sumu. Princip by byl v tom, ze bych mel obrazky veci, ty bych komprimoval Furrierovym rozkladem a vysledny "frekvence" priradil ke slovum, ktery odpovidaj veci na obrazku. Nasledna geneze obrazku by vyuzila sum a znamy "rezonancni" frekvence prislusnejch slov a ze sumu by vygenerovala pozadovany obrazek. Okolo by byl nakej balast transformace prostoru slov na vektory, frekvence na vektory a tak..
    THEODORT
    THEODORT --- ---
    Spíš klasický entry level SD1.5 než cokoliv jiného.
    TOMASEQ
    TOMASEQ --- ---
    Buddha in wild west on a horse with two colts and a hat

    Zajimavy dat nejaky hodne silny vzorce co nemaj velkej prunik, takze neni moc z ceho tezit…

    FRK_R23
    FRK_R23 --- ---
    Zkoušel jsem jestli Fooocus umí taky třeba metahuman render :)

    prompt: Unreal engine metahuman, unreal engine render, lumen
    Base model: juggernautXL_v8

    THEODORT
    THEODORT --- ---
    kdyztak je pro neprogramatory video upscale dostupne coby soucast pluginu deforum pro A1111, v zakladu to umi realesrgan, ale jdou tam stahnout i jine libovolne upscale GANs
    FRK_R23
    FRK_R23 --- ---
    ALI: Přitom Voice isolation funguje super.
    ALI
    ALI --- ---
    MORPHLER: potvrzuju, ze upscale v resolve je nepouzitelnej; vyzkouseno na mnoha ruznejch zdrojich
    E2E4
    E2E4 --- ---
    wow

    Co kdyby Hvězdné Války natáčel Karel Zeman? AI trailer.
    https://www.youtube.com/watch?v=ePreSnRMyO4
    INK_FLO
    INK_FLO --- ---
    The Age of Noise - by Eryk Salvaggio - Cybernetic Forests
    https://cyberneticforests.substack.com/p/the-age-of-noise

    Noise is a slippery word. It means both the presence and absence of information. Today it's in the urbanisation of our world, the hum of traffic and jet engines. Noise is also where we go to escape noise. In August of 2023, Spotify announced that users had listened to 3 million hours of white noise recordings. Noise to sleep to, noise to drown out noise. Noise is also the mental cacophony of data, on social media, of smartphones, and the algorithmic spectacle. The age of noise is a logical conclusion, a successful ending for the information age. And information, which was once scarce, is now spilling from the seams of our fibre optic cables and airwaves. The information age is over. Now we enter the age of noise. We can pin the information age to the invention of the transistor in 1947. The transistor was quaint by today's standards, a mechanism for handling on-off signals. Engineers built pathways through which voltage flowed, directing and controlling that voltage in response to certain inputs. We would punch holes into cards and feed them to a machine, running light through the holes into sensors. The cards became a medium, a set of instructions written in the language of yes and no.

    In other words, it all started with two decisions, yes or no, one or zero. The more we could feed the machine, the more the decisions the machine could make. Eventually it seemed the number of decisions began to encroach on our own. The machine said yes or no so that we didn't have to. By the start of the social media era, we were the ones responding to these holes. Like or don't like. Swipe left or swipe right. It all began with that maze of circuitry. The first neural networks, our adding machines, the earliest computers, were designed to reveal information. Noise meant anything that crept into the circuits, and the history of computing is in part a history of noise reduction. The noise in our telephone wires and circuit boards, even our analog TV broadcasts, was background radiation. Energy pulsing invisibly in the air, lingering for millennia after the Big Bang exploded our universe into being. Our task was to remove any traces of it from our phone calls. Today millions of on off calculations can take place in a single second. Put enough of these signals together, run them fast enough and you can do remarkably complex things with remarkable speed. Much of that has been harnessed into lighting up pixels. Put enough pixels together and you get a digital image. You get video games. You get live streams. You get maps, interfaces and you collect and process responses to live streams, maps and interfaces.

    With what we call generative AI today, we obviously aren't using punch cards. Now we inscribe our ones and zeros into digital images. The data mining corporations behind social media platforms take these digital images and they feed them to massive neural nets and data centres. In substance, the difference between punch cards and today's computation is only that our holes are smaller. Every image that we take becomes a computer program. Every caption and every label becomes a point of information. Today's generative AI models have learned from about 2.3 billion images with about 24 bits of information per pixel. All of them still at their core, a yes or no decision moving through a structure. I don't say this to give you a technical overview of image processing. I mention it because the entirety of human visual culture has a new name. We used to call these collections archives or museum holdings or libraries. Today we call them data sets. This collected culture has been harnessed to do the work of analog punch cards. And these cards, these physical objects, were once stamped with a warning. Do not fold, spindle or mutilate. Our collected visual heritage in its digital form carries no such warning.

    We don't feed our visual culture into a machine by hand anymore, and the number of decisions that we have automated are so large that even the words are ridiculous. Teraflops. We upload images to the internet, pictures of our birthday parties, our weddings, embarrassing nights at the club (not so much me anymore). Our drawings, our paintings, these personal images meant to communicate with others are clumped together with other archives. Cultural institutions share a wealth of knowledge online for the sake of human education and the arts history and beyond. And in training an AI model, all of these images are diffused, a word that is so neatly parallel to this diffusion of unfiltered information that we surround ourselves with. And for once, it's a technology named in a way that describes what it actually does. Diffusion models actually diffuse! This word means what it says. It dissolves the images, it strips information away from them until they resemble nothing but the fuzzy chaos of in between television channels. Images are diffused into noise. Billions of good and bad images all diffused into noise for the sake of training an artificial intelligence system that will produce a billion more images. From noise into noise, we move from the noise of billions of images taken from our noisy data-driven visual culture, isolate them and dissolve them into the literal noise of an empty JPEG, to be recreated again into the noise of one more meaningless image generated by AI among the noise of billions of other images, a count of images that already overwhelms any one person's desire to look at them.

    The information age has ended and we have entered the age of noise.

    We often think of noise as a presence. In America, we call it snow, the static. I've heard of other things as well. It's called ants in Thailand. Other places have other metaphors. But snow is a presence. We see snow. We see noise. We hear noise. Noise from a communication engineering perspective is the absence of information. Sometimes that absence is the result of too much information, a slippery paradox. Information which cannot be meaningfully discerned is still noise. Information has been rushing at us for about two decades now, pushing out information in the frame of content to such an extent that almost no signal remains that is worth engaging with. Here's a map of the internet visualised 20 years ago. Since then, it has only grown, today becoming a disorienting flood of good and bad information coming through the same channels. And what we are calling generative AI is the end result of a successful information age, which in just 24 years has rewritten all cultural norms about surveillance, public sharing, and our trust in corporatised collections of deeply personal data. Server farms mined this data through regimes of surveillance and financialisation. The guiding principle of social media has always been to lure us into sharing more so that more data could be collected, sold, and analysed. They've calibrated the speed of that sharing to meet the time scales of data centres rather than human comprehension or our desire to communicate. And all this data has become the food for today's generative AI.

    The words we shared built chat GPT, the images we shared built Stable Diffusion. Generative AI is just another word for surveillance capitalism. Taking our data with dubious consent and activating it through services it sells back to us. It is a visualisation of the way we organise things, a pretty picture version of the technologies that sorted and categorised us all along. Instead of social media feeds or bank loans or police lineups, these algorithms manifest as uncanny images, disorienting mirrors of the world rendered by a machine that has no experience of that world. If these images are unsettling because they resemble nothing like the lives they claim to represent, it's because that is precisely what automated surveillance was always doing to us. The internet was the Big Bang of the information era, and its noisy debris lingers within the Big Bang of generative AI. Famously, Open AI's chatbot stopped learning somewhere in April of 2021. That's when the bulk of its training was complete, and from there it was all just fine-tuning and calibration. Perhaps that marks the start of the age of noise, the age where streams of information blended into and overwhelmed one another in an indecipherable wall of static, so much information that truth and fiction dissolved into the same fuzz of background radiation.

    I worry that the age of noise will mark the era where we turn to machines to mediate this media sphere on our behalf. It follows a simple logic. To manage artificial information, we turn to artificial intelligence. But I have some questions. What are the strategies of artificial intelligence? The information management strategies that are responsible for the current regime of AI can be reduced to two, abstraction and prediction. We collect endless data about the past, abstract it into loose categories and labels, and then we draw from that data to make predictions. We ask the AI to tell us what the future will look like, what the next image might look like, what the next text might read like. It's all based on these abstractions of the data about the past. This used to be the role of archivists. Archivists used to be the custodians of the past, and archives and curators, facing limited resources of space and time, often pruned what would be preserved. And this shaped the archives. The subjects of these archives adapt themselves to the spaces we make for them. Just as mold grows in the lightest part of a certain film, history is what survives the contours we make for it. We can't save everything. But what history do we lose based on the size of our shelves? These are a series of subjective, institutionalised decisions made by individuals within the context of their positions and biases and privileges and ignorances. The funding mandates, the space, and the time. (No offence!)

    Humans never presided over a golden age of inclusivity, but at least the decisions were there on display. The archive provided its own evidence of its gaps. What was included was there, and what was excluded was absent. And those absences could be challenged. Humans could be confronted. Advocates could speak out. I'm reminded of my work with Wikipedia, simultaneously overwhelmed with biographies of men, but also host to a remarkable effort by volunteers to organise and produce biographies of women. When humans are in the loop, humans can intervene in the loop.

    I'm often asked if I fear that AI will replace human creativity, and I don't remotely understand the question. Creativity is where agency rises, and as our agency is questioned, it is more important than ever to reclaim it, through creativity, not adaptability. Not contorting ourselves to machines, but agency — contorting the machines to us. I fear that we will automate our decisions and leave out variations of past patterns based on the false belief that only repetition is possible. Of course, my work is also a remix. It has a lineage. To Nam June Paik, who famously quipped, “I use technology in order to hate it properly.” And this is part of the tension, the contradictions that we're all grappling with. I'm trying to explore the world between archive and training data, between the meaningful acknowledgement of the past and the meaningless reanimation of the past through quantification. Archives are far more than just data points. We're using people's personal stories and difficult experiences for this. There's a beauty of lives lived and the horrors, too. Training images are more than data. There is more to our archives than the clusters of light-coloured pixels. Our symbols and words have meaning because of their context in collective memory. When we remove that, they lose their connection to culture. If we strip meaning from the archive, we have a meaningless archive. We have five billion pieces of information that lack real-world connections. Five billion points of noise. Rather than drifting into the mindset of data brokers, it is critical that we as artists, as curators, as policymakers approach the role of AI in the humanities from a position of the archivist, historian, humanitarian, and storyteller. That is, to resist the demand that we all become engineers and that all history is data science.

    We need to see knowledge as a collective project, to push for more people to be involved, not less, to insist that meaning and context matters, and to preserve and contest those contexts in all their complexity. If artificial intelligence strips away context, human intelligence will find meaning. If AI plots patterns, humans must find stories. If AI reduces and isolates, humans must find ways to connect and to flourish. There is a trajectory for humanity that rests beyond technology. We are not asleep in the halls of the archive, dreaming of the past. Let's not place human agency into the dark, responsive corners. The challenge of this age of noise is to find and preserve meaning. The antidote to chaos is not enforcing more control. It's elevating context. Fill in the gaps and give the ghosts some peace.

    Looking at the Machine | FACT24 Symposium
    https://www.youtube.com/watch?v=Eqw7U8BA5aM
    ARAON
    ARAON --- ---
    More workflow tests. Here it's photoshop sketch to Krea to 3D. It takes roughly 15 seconds or so to generate a model from the sketch.
    https://twitter.com/MartinNebelong/status/1763900406759882944
    Kliknutím sem můžete změnit nastavení reklam