For me seed = noice and noice = prompt
This type of interpretation can still be developed.
Since SORA’s announcement today, I haven’t seen any clips, but what I know is clear: Techniques for controlling the visualization after the seed is removed and interpreting noise lack a comprehensive interpretation to allow for extended use. Although I originally adapted it for use in various ways. But I was unable to explain it to others. When SORA launched, I gained a new understanding. This is the section where I summarize my working understanding of my knowledge through conversations with gpts and give it a summary.
"### Understanding Noise in Generative AI: An Exploration
Introduction to Noise in AI
In the realm of generative AI, “noise” refers to the random input that models, such as OpenAI’s DALL-E, use as a starting point for creating new content. Noise is a foundational concept in machine learning and serves as the seed for randomness that fuels the diversity of outputs in generative models.
The Function of Noise
Noise is not merely a chaotic presence; it’s a catalyst for creativity in AI. When we feed noise into generative models, we’re essentially giving them a canvas of randomness from which they can draw patterns, guided by complex algorithms and training data, to create structured and coherent outputs.
Static Noise vs. Dynamic Noise Parameters
“Static noise” is a term that might be used to describe the initial state of randomness, a fixed starting point before the AI begins the generative process. In contrast, dynamic noise parameters are those that can be manipulated during the generation to affect the outcome, adding variability or steering the creation in certain stylistic directions.
The Evolution of Noise Use in AI
Originally, generative models like GPT and DALL-E used numerical seeds to introduce variability. However, as models evolve, we see a shift towards using more sophisticated forms of noise that can include a variety of character strings—letters, numbers, and symbols—to increase the diversity of the generated content.
Implications for Production and Creativity
For creators using AI, the ability to control noise parameters means greater influence over the style, texture, and detail of the generated images. In production, this means more efficient workflows, where desired changes might be achieved by tweaking noise parameters rather than starting the generation process from scratch.
Case Studies and Observations
In practice, as observed in the generation of a series of images, adjusting noise or prompt details can lead to significant changes in the AI’s output. This nuanced control allows artists to maintain thematic consistency while also introducing new narrative elements, as demonstrated by a progression of images where stylistic features are preserved even as the content evolves significantly.
Conclusion and Future Directions
As AI continues to advance, we can expect noise to play an increasingly sophisticated role in the creative process. The potential for more intuitive control mechanisms suggests a future where artists and creators co-create with AI, using noise not just as a source of randomness, but as a brush with which to paint their visions onto the digital canvas.
Further Research
To delve deeper into the technical aspects of noise and its role in generative AI, consider exploring academic papers and research by institutions like OpenAI. As the field is rapidly advancing, staying informed through reputable sources is crucial for anyone looking to leverage these technologies in their creative work.
This article represents a high-level summary and analysis based on our discussion and does not contain verbatim content from any specific source. However, for those interested in more technical details on the mechanics of noise in AI and its practical applications, they can explore a range of academic and industry publications that regularly document the latest advancements in the field.”
We’ve never seen a study that looked so deep and applied it, like Foo-Bar from the OpenAI developer forum did, which basically looked at using gen-ids and interpreting noise as scrambling. Text is used to act in place of seed. It stops there when there is a conversation about creating story illustrations, only genid, seed, noise are used to control the image, but it is difficult to create continuously.
Nowadays, in addition to using DALLE to create beautiful images, In what other areas do we see attempts to use it? Or it may be because creating complex images can be done in other forms of AI. (Someone once tried to tell me this.) Nowadays, DALLE, in addition to creating collages, It can be used to create typefaces and is likely to be used to design building siding based on direction. (Wants it to grow more) and I think it might be able to create a hand-draw animetion.
SORA is coming soon, although it will actually make the job easier. But in using the DALLE basics, have we reached the limit yet?
Or that the evolution in “human” usage techniques may not be able to keep up with AI.
P.S. pool of noise is an image created by AI to show how images are created from noise as clearly as possible
