[功能参数] AI-Generated Images: An Analysis of the Differences and Usage of Seed, gen_id, and Noise

Regarding seed, genid, and noise, let us take ChatGPT as an example. During DALL-E 3 image generation, it is not a completely random, unmanaged file. After each image is generated, it has a serial number, and this number needs to be unique, like Figure 1, Figure 2, and Figure 3. The name of this serial number is gen_id. The seed number is an algorithm often used during image generation; it contains the image’s style elements, colors, stroke placement, contours, shapes, etc. You can obtain this image’s seed number and make use of it. The seed is generated randomly, and the process of these style elements depends on the influence of noise technology. In DALL-E 3, it usually represents pixel points, compression sampling, digital processing, data sensing, etc. Noise is non-public data, but you can tune it through other forms. When we create consistent images, we usually reference seed or genid, but this is unstable, whereas noise technology is the underlying control logic. If you know how to tune noise, you are definitely an expert.

The image shows a static noise picture that GPT is trying to visualize; it can be understood as the initial state of image generation. It gradually forms shapes, colors, textures, light, and other elements according to the prompt, and ultimately it forms the seed.



Below is an AI noise report jointly researched by the moderator and a Thai friend. We have already published it at an internationally renowned institution, and it has now begun to spread internationally.

Introduction to Noise in AI
In the field of generative AI, “noise” refers to the random input that a model (such as OpenAI’s DALL-E) uses as a starting point to create new content. Noise is a fundamental concept in machine learning and the seed of randomness, promoting diversity in the outputs of generative models.

The Role of Noise
Noise is not merely a chaotic presence; it is also a form of existence. It is the catalyst for artificial intelligence creativity. When we input noise into generative models, we are essentially providing them with a random canvas from which they can draw patterns under the guidance of complex algorithms and training data to create structured and coherent outputs.

Static Noise and Dynamic Noise Parameters
“Static noise” is a term that can be used to describe the initial state of randomness, that is, the fixed starting point before artificial intelligence begins the generation process. In contrast, dynamic noise parameters are those that can be manipulated during the generation process to affect the result, increase variability, or guide the creation toward certain stylistic directions.

Evolution of Noise Use in AI
Initially, generative models such as GPT and DALL-E used numeric seeds to introduce variability. However, as models developed, we saw a shift toward using more complex forms of noise, which can include various strings (letters, numbers, and symbols), to increase the diversity of generated content.

Impact on Generation and Creativity
For creators using artificial intelligence, the ability to control noise parameters means greater influence over the style, texture, and details of generated images. In production, this means a more efficient workflow, where desired changes can be achieved by adjusting noise parameters rather than starting the generation process from scratch.

Case Studies and Observations
In practice, as observed when generating a series of images, adjusting noise or prompt details can lead to significant changes in artificial intelligence output. This nuanced control enables artists to maintain consistency of subject while introducing new narrative elements, as demonstrated by a series of images in which stylistic features are preserved even when the content changes significantly.

Conclusion and Future Directions
As artificial intelligence continues to develop, we can expect noise to play an increasingly complex role in the creative process. The potential of more intuitive control mechanisms heralds a future in which artists and creators co-create with artificial intelligence, using noise not only as a source of randomness but also as a brush to paint their vision onto a digital canvas.

Further Research
To delve deeper into the technical aspects of noise and its role in generative artificial intelligence, consider exploring academic papers and research from institutions such as OpenAI. As the field develops rapidly, obtaining information through reputable sources is crucial for anyone wishing to leverage these technologies in creative work.

This article is an advanced summary and analysis based on discussion and does not contain verbatim content from any specific source. However, those interested in more technical details about artificial intelligence noise mechanisms and their practical applications can explore a range of academic and industry publications that regularly document the latest advances in this field.

This is the midway point of noise forming an image; we can see the image beginning to take shape gradually.





We often say, “Help me reference seed:xxxx,” or “Refer to genid,” to achieve the goal of creating consistent images. But have you ever felt that this approach is uncontrollable? You cannot change anything about whether the result looks similar or not; you can only treat it as a gacha pull and say “regenerate.” But when you can precisely control changes to the noise, it will be completely consistent.