How Stable Diffusion Turns Noise Into Pictures

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Stable Diffusion doesn’t start with a blank canvas. It begins with random noise and gradually shapes it into an image that matches your prompt. First, the text prompt is encoded into vectors by a CLIP text encoder. The heavy computation happens in a compact latent space, which makes the process fast and affordable. A U‑Net model then iteratively predicts and removes noise, guided by the text at each step. Once the latent representation is clean, a decoder expands it back to full‑resolution pixels, delivering the final picture.

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