Raw LLM Responses

@strawhat612  this cannot be farther from the truth. If this was the case you wouldn't be able to get what you ask for. For example, if you say a cat on a couch in a living room it would look basically the same every time. It is math though, and what is essentially predictive pattern recognition so you're not really that far off from it, but it's not an averages thing. What is Interesting is that you can skew data and guide the output based on your use case via the prompt yes but by retraining or "fine tuning" the models. Best way to think about it is a storm cloud filled with randomly placed rain drops. Your prompt is lightning that travels between the drops in a random order based on your prompt. Each drop of rain is a piece of data trained in to the model representing a pattern triggered by a word, and organized in a way that related words are closer together. Such as face and nose are closer than face and banana. Your prompt travels between the drops based on that setup, in a somewhat randomized way, and the layout of the drops and the path of the lightning are affected by various factors such as sampling method, scheduler, the model, the vae encoder, the prompt, clip guidance which is essentially prompt adherence, as well as structural guidance you can add in such as control nets like depth maps or edge detection, style reference tools like IP adapter or face swap stuff, blah blah blah it goes on and on. Then you also have the input options for image to image, image to vid, or vid to vid. And then you have people making custom tools for VFX pipelines that can be used as well like datamoshing or glitch art or whatever. Point being, the "all you do is enter words and get back a stolen image" is far from the truth. Unfortunately, since all of the commercialized options like midjourney or the chat ai image generators like chat gpt or grok are all only basic text to most people don't know exactly how deep and complex the systems go. That said, everything else aside, it's the future and unavoidable so artists should be learning the tools so that they can learn to train their own models on their own work and use those models to speedrun concepting and brainstorm sessions, then they can use those for reference or a starting point, effectively increasing their productivity and value (for commercial corporate or large scale application) exponentially, there by adding value to their skillset, and increasing their relevance and likelihood that they will be employed over those who either cannot or who cannot art but who can ai. Companies are forever going to need unique ideas so actual artists will always be needed even if that means being the one to create the dataset for them to train an AI. But trust me. Youd rather be the one making The data set, training the models, story boarding, and fixing / improving / detailing the outputs than the one not making anything at all. Hiring the artist to only make a data set will only last so long. Soon it'll be the standard to hire artists who can make original datasets AND train and use the ai. ☔☔☔