https://www.youngju.dev/blog 천천히 올바르게. AI Researcher & DevOps Engineer Youngju's blog. GPU/CUDA, LLM, MLOps, Kubernetes AI workloads, and data engineering — plus mindset essays on confidence, routines, health, and sport psychology. ko fjvbn2003@gmail.com (Youngju Kim) fjvbn2003@gmail.com (Youngju Kim) Mon, 24 Aug 2026 00:00:00 GMT https://www.youngju.dev/blog/ai/tiny-models-06-text-to-image.en https://www.youngju.dev/blog/ai/tiny-models-06-text-to-image.en We built a conditional diffusion model with 1.11 million parameters that draws a 0 when you type "zero". The forward process that adds noise is a single formula; the reverse process that restores the image is that same line walked backwards 400 times. Using the actual generated output, we look at how diffusion sidesteps the washed-out colour problem from Part 5, and why the model is trained to predict noise rather than the image. Mon, 24 Aug 2026 00:00:00 GMT fjvbn2003@gmail.com (Youngju Kim) aidiffusionddpmgenerativepytorchtiny-models https://www.youngju.dev/blog/ai/tiny-models-06-text-to-image.ja https://www.youngju.dev/blog/ai/tiny-models-06-text-to-image.ja "zero" と書けば0を描く条件付き拡散モデルを111万パラメータで作りました。ノイズを混ぜる forward は数式1行、復元する reverse はその1行を400回逆にたどるだけです。第5回で L1 損失が色を褪せさせた問題を拡散がどう回避するのか、そしてなぜモデルがノイズを予測するよう学習されるのかを、実際の生成結果で確認します。 Mon, 24 Aug 2026 00:00:00 GMT fjvbn2003@gmail.com (Youngju Kim) aidiffusionddpmgenerativepytorchtiny-models https://www.youngju.dev/blog/ai/tiny-models-06-text-to-image https://www.youngju.dev/blog/ai/tiny-models-06-text-to-image "zero"라고 쓰면 0을 그리는 조건부 디퓨전 모델을 111만 파라미터로 만들었습니다. 노이즈를 섞는 forward는 공식 한 줄이고, 복원하는 reverse는 그 한 줄을 400번 거꾸로 밟는 것뿐입니다. 5편에서 L1 손실이 색을 바래게 만든 문제를 디퓨전이 어떻게 피해 가는지, 그리고 왜 모델이 노이즈를 예측하도록 학습되는지를 실제 생성 결과로 확인합니다. Mon, 24 Aug 2026 00:00:00 GMT fjvbn2003@gmail.com (Youngju Kim) aidiffusionddpmgenerativepytorchtiny-models https://www.youngju.dev/blog/ai/tiny-models-06-text-to-image.zh https://www.youngju.dev/blog/ai/tiny-models-06-text-to-image.zh 我们用111万参数做了一个条件扩散模型：输入 "zero" 就画出 0。加噪的 forward 只是一行公式，还原的 reverse 就是把这一行倒着走 400 次。本文用真实生成结果说明扩散如何绕开第5篇中 L1 损失导致的颜色发灰问题，以及为什么模型被训练去预测噪声而不是图像。 Mon, 24 Aug 2026 00:00:00 GMT fjvbn2003@gmail.com (Youngju Kim) aidiffusionddpmgenerativepytorchtiny-models