Highlights: Every computer vision pipeline that touches color starts with the same mistake: using RGB. RGB is built for screens, not for human perception. In this post we build a complete color scoring system for living room images — from the right color space (Lab), through palette extraction (K-means), to a two-color harmony scorer tested on 10 global brand palettes. We discover why luxury brands deliberately score low, and what that means for your model.…
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Highlights: Every modern LLM agent — from ChatGPT plugins to Claude tools — relies on a single learned skill: outputting a structured JSON function call instead of free text. In this post we teach that skill to a 3-billion parameter model using QLoRA on a free Google Colab T4. We start from the fundamentals — why fine-tuning, when LoRA, how quantization works — then build the full training pipeline from scratch. By the end, your…
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Highlights: Stable Diffusion doesn’t paint an image in one shot — it sculpts one from static, guided by your words. In this post we disassemble the entire machine. We start with the VAE that compresses pixels into a tractable latent space, walk through the forward and reverse diffusion processes, open up the UNet to see how cross-attention physically connects text tokens to spatial regions, and finish with the complete Latent Diffusion architecture diagram that ties…
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Highlights: Every time you use Stable Diffusion, DALL-E, or Sora, the model never touches a single pixel during its main computation. It works entirely inside a compressed, structured space of floating-point numbers — a latent space learned by a VAE. In this post we build that space from scratch. We start from the simplest possible compression — an AutoEncoder on MNIST digits — understand why it fails at generation, fix it with the VAE’s probabilistic…
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Highlights: In this post we build a complete understanding of diffusion models from the ground up — what they are, how images are represented, how the network is trained, what it geometrically learns, and finally how we turn that geometry into samples using DDIM and DDPM. Every formula is accompanied by concrete numbers you can verify by hand. So let’s begin! Tutorial Overview: What Are Diffusion Models? How Images Are Represented The Denoiser Network Noise…
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In this post we build a complete diffusion model from scratch — training a UNet on a custom dataset, implementing the full DDPM pipeline, and understanding the math that makes iterative denoising work. We cover noise schedules, the reparameterization trick, FID evaluation, and three diffusion objectives (ε, x₀, v). By the end you’ll have generated novel images from pure Gaussian noise, and understand why diffusion models overtook GANs as the dominant paradigm for image generation.…
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Highlights: We implement a complete DDPM from scratch on 1D sine waves — same math as image diffusion, but every intermediate state is plottable. We track 100 parallel trajectories, measure when the model “commits” to a specific sample, then design a controlled experiment that reveals manifold compactness as the key factor determining whether diffusion succeeds or fails. So let’s begin! Tutorial Overview: Why 1D? The Dataset Forward Process Model and Training Generating from Noise What…
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Highlights: In this post, we take a deep dive into the architecture that changed everything — the Transformer — and trace its evolution from NLP into computer vision. We start with the original encoder-decoder model, walk through self-attention and multi-head attention step by step, and then show how Vision Transformers (ViT) apply the exact same mechanism to image patches instead of words. Along the way, we answer the questions that trip everyone up: if we…
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