PROMPT RECORD图像记录
AI 图像生成的循环通常是这样工作的： 1. 你给它一个提示（prompt） 示例： a futuristic city at sunset, cinematic, ultra realist...
中文说明
AI 图像生成的循环通常是这样工作的： 1. 你给它一个提示（prompt） 示例： a futuristic city at sunset, cinematic, ultra realistic AI 接收到你的文本，并试图理解： objects（物体） style（风格） mood（氛围） composition（构图） visual details（视觉细节） 2. 文本被转化为数值表示 提示（prompt）不会被像人类阅读那样被读取。 它会被转换为数值向量（numerical vectors），这些向量捕捉了词语的含义以及它们之间的关系。 例如： city（城市）暗示 buildings（建筑）、streets（街道）、skyline（天际线） sunset（日落）增加暖色调和低光照 cinematic...
原始 Prompt
The AI image generation cycle usually works like this:
1. You give it a prompt
Example:
a futuristic city at sunset, cinematic, ultra realistic
The AI receives your text and tries to understand:
the objects
the style
the mood
the composition
the visual details
2. The text is turned into a numerical representation
The prompt is not read like a human reads it.
It is converted into numerical vectors that capture the meaning of the words and the relationships between them.
For example:
city suggests buildings, streets, skyline
sunset adds warm colors and low light
cinematic influences the visual style
3. The model often starts from random noise
In many modern systems, especially diffusion models, the image does not begin as a clean picture.
It starts as something like random visual noise.
4. The AI gradually removes the noise
This is the core of the process.
At each step, the model asks:
If this image is supposed to match the prompt, what should a slightly less noisy version look like?
Then it repeats this process many times:
full noise
blurry shapes
rough forms
clearer objects
fine details
final image
5. The model guides the image toward the prompt
During the denoising process, the AI keeps aligning the image with the meaning of the text.
So it keeps adjusting:
shape
color
lighting
texture
overall coherence
That is why the prompt strongly affects the final result.
6. The latent image becomes a visible image
In many systems, the model first works in a compressed space called latent space.
Then a decoder turns that compressed version into a visible pixel image.
7. Extra steps can be added
Depending on the system, there may also be:
upscaling to increase resolution
inpainting to modify a specific area
face enhancement to improve faces
color correction to refine the look
safety filtering to block certain content
Ultra simple version
The cycle is:
prompt
→ text understanding
→ random noise
→ gradual denoising
→ alignment with the prompt
→ final image
One sentence summary
An AI image generator takes a text prompt, converts it into mathematical meaning, often starts from random noise, and then transforms that noise step by step into an image that matches the prompt.