How does a diffusion model generate an image from random noise?

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How does a diffusion model generate an image from random noise?

Asked on Sep 16, 2025

Answer

Diffusion models, such as those used in Stable Diffusion, generate images by starting with random noise and iteratively refining it through a reverse diffusion process. This process involves gradually denoising the image using learned patterns until a coherent image emerges.

Example Concept: A diffusion model generates an image by reversing a diffusion process that initially adds noise to an image. During training, the model learns to predict and remove noise at each step, effectively reconstructing the image from random noise. This iterative denoising continues until the final image is produced, capturing the desired features and details.

Additional Comment:

Diffusion models are trained on large datasets to learn the distribution of images, which helps them effectively denoise and reconstruct images.
The process is computationally intensive, often requiring powerful hardware to perform efficiently.
These models are particularly effective at generating high-quality, detailed images by leveraging the learned noise patterns.

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