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Unveiling Diffusion Models: The Transformers of Image Generation

Unveiling Diffusion Models: The Transformers of Image Generation
Unveiling Diffusion Models: The Transformers of Image Generation

Diffusion Models for Image Generation

Diffusion models are a family of generative machine learning models that excel in converting textual descriptions into realistic images. Their workflow consists of three primary stages:

1. Encoding Text Input

Diffusion models utilize embedding vectors to represent text input. Embedding transforms the text into a uniform tensor format, allowing the model to understand its meaning. This process enables the model to capture the essence of the text and produce images that align with the description.

2. Latent Space Processing

Diffusion models operate in a latent space, where a denoising network gradually removes noise from an initial random array of pixels. This denoising process is conditioned on the text embedding, ensuring that the output image reflects the desi

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