![]() Essentially, P represents the textual-conditioning space, where an input instance “ p” belonging to P (which has passed through a text encoder) is injected into all attention layers of a U-net during synthesis.Īn overview of the text-conditioning mechanism of a denoising diffusion model is presented below. The conditioning space in these models is referred to as the P space, defined by the language model’s token embedding space. This process results in high-resolution and diverse images that match the input text, achieved through a U-net architecture that captures and incorporates visual features of the input text. They encode the given textual description into a latent vector, which affects the noise distribution, and iteratively refine the noise distribution using a diffusion process. Text-to-image diffusion models generate images by iteratively refining a noise distribution conditioned on textual input. ![]() □ JOIN the fastest ML Subreddit CommunityĪmong the most adopted text-to-image generative networks, we find diffusion models. ![]()
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