Training of an AI model, and understanding diffusion simply

Just for a seed to become a tree, it first becomes a sapling, then a plant, then a larger tree, and a final tree. Training AI model is similar. Speaking from intuition. First, basic lower level network layers (or processing parts are learnt, first in approximate, which gradually become accurate), once lower level parts or better, the higher level layers can begin to form associations in a weak sense. Once higher level parts learnt the associations, the network is trained on the dataset, for a particular kind of output. The complexity of information encoded increases at first (given the data at input), and decreases depending on the demands of what is requested ( or trained to output). An 8GB diffusion model can pack more than 8GB of image information. Which should be obvious for its use. However, some models are overly sized for the tasks they do, and usually are just overkill in many applications, just like using a generic all-purpose microprocessor for doing only one task, but the solution is scalable, since its a one-can-do-all strategy, which works after mass production (scaling in quantity which is copy-paste for software) and use (although required hardware still needs to be bought, but assembly line stays the same for same hardware, hence allowing multipurpose of a single generic AI trained model, with limitless potential.

Also, when finetuning or training more an existing model on a different or more data, the lower levels are already learned (assuming the previous dataset accounted for the generality existing in the dataset), and then only the delta changes in the weight volume (just imagine, not literal numbers but voluminous structure of the weights in the network, say all weights spread in 3dimensions), there are small changes added to the existing large network, in certain regions where learning is needed. So model changes by a delta amount when fine-tuned, or a more data is added.

In diffusion networks, where a single network learns the denoising mapping, depending on the input, the same function acts (and becomes dense) so it can just direct the output each time by delta.

Diffusion networks work by mapping the input to slightly different part of space where noise is different , hence the space is represented by different space of pixels in its higher dimension . For e.g. in following Figure, starting from space at left (where each point on line is in N_Dimension-space), the output space at each iteration is changed to different space (but same dimension so same model can be reused on the output consecutively), where no space overlaps (which prevents same operation being applied twice for subsequent outputs)

Or the same idea can be shown using concentric circles mapping the input space to space outside, hence a same network (this 5x5x1) block function can be applied consecutively to obtain the mapping. (note that here the input output is 2d vector, but in reality, its the size of image (i.e. could be 512x512x3) for example, or arbitrary (NxMxC, say 1000x1000x1000).

So for the details regarding diffusion networks, I asked chatGPT to show how this post is related to it:

How Diffusion Models Work: An Intuitive Guide 🚀

Diffusion models are powerful generative AI models that create images by iteratively refining noise, guided by conditioning (like text prompts). Understanding them intuitively is key to grasping why they work so well. Let’s break it down step by step.

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📌 Step 1: Learning Like a Growing Tree 🌱 → 🌳

Training an AI model is not an instant process—it grows like a tree:

1️⃣ First, basic structures form—just like a seed turning into a sapling.
2️⃣ Then, higher-level patterns develop—like leaves, branches, and associations in the model.
3️⃣ Finally, the tree is fully grown—the model learns to generate specific outputs.

In diffusion models, this means:

• Lower layers first learn fundamental pixel features (edges, colors).
• Higher layers then form meaningful structures (faces, objects).
• At the highest level, the network learns associations (cat sitting on a beach).

This is why a trained model can later be fine-tuned efficiently—it only needs to adjust some parameters (“delta updates”), rather than relearning everything.

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📌 Step 2: The Diffusion Process – A Dirty Window Analogy 🪟

Diffusion models work in reverse from random noise to a clear image:

🔄 Imagine cleaning a dirty window step by step:

• At first, you wipe roughly to remove most of the dirt (coarse denoising).
• Then, as the window gets clearer, you use finer motions for precision cleaning (fine details).
• Eventually, you reveal a perfect view—just like diffusion models generating realistic images.

Mathematically,

this means:
1️⃣ The model starts with pure noise.
2️⃣ Each step removes a small amount of noise, gradually revealing the real image.
3️⃣ Over T steps, the image becomes fully denoised and realistic.

This process is not random—it is guided by learned patterns from training data.

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📌 Step 3: Why Does One Function fθf_\thetafθ​ Work at Every Step? 🤔

This is where diffusion models get efficient. Instead of training a different model for every denoising step, they use one universal function fθf_\thetafθ​.

🔹 Think of it like using the same cleaner for every part of the window—but adjusting their movements based on how dirty each area is.
🔹 fθf_\thetafθ​ is conditioned on time ttt, so it adapts its behavior dynamically.
🔹 Early on, it removes large amounts of noise; later, it fine-tunes the details.

Mathematically:ϵ^θ=fθ(xt,t)\hat{\epsilon}_\theta = f_\theta(x_t, t)ϵ^θ​=fθ​(xt​,t)

• xtx_txt​ is the noisy image at step ttt
• ttt tells the model how much noise is left
• fθf_\thetafθ​ predicts the noise at each pixel, so it can be removed

This is similar to your 5x5x1 mapping function, where the same block function is applied iteratively, shifting the representation slightly each time.

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📌 Step 4: How Text Prompts Guide the Model 📝**

Without guidance, diffusion models would generate random images. Text conditioning solves this by acting like a GPS.

🔹 Imagine an artist drawing a picture based on your description:

• If you say “A cat on the beach,” the artist gradually refines the sketch until it matches your idea.
• If you change your description, the artist adjusts the details accordingly.

🔹 In diffusion models:

• A text encoder (like CLIP or T5) converts your prompt into a vector ccc.
• This vector is passed into the model:

ϵ^θ=fθ(xt,t,c)\hat{\epsilon}_\theta = f_\theta(x_t, t, c)ϵ^θ​=fθ​(xt​,t,c)

• Now, the network removes noise while ensuring the output matches the prompt.

This is why each pixel is denoised while staying aligned with the text input.

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📌 Step 5: Why the Same Network Works for Every Step? 🔄

Your intuition about parameter ttt keeping the network in different spaces is exactly right!

🔹 ttt tells the model which part of the noise removal process it’s in.
🔹 The network does not apply the same transformation twice on the same space—it moves the representation forward step by step.
🔹 This ensures that the model doesn’t erase useful details while removing noise.

Instead of learning separate models for each step, diffusion models use a single function that adapts dynamically. This makes them efficient and scalable.

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📌 Final Takeaway: Diffusion Models Are Like Iterative Mapping Functions 🔄

Your 5x5x1 mapping idea is a perfect analogy!

• A diffusion model iteratively applies the same function.
• It moves data through latent space without overlapping itself.
• The same function, guided by ttt, works in every step, ensuring smooth transitions.

In short, diffusion models don’t generate images in one step—they iteratively refine noise into meaningful patterns. They are efficient, scalable, and generalizable, which is why they work so well for AI-generated content.