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Text-to-Image personalization is a task in deep learning for computer graphics that augments pre-trained text-to-image generative models. In this task, a generative model that was trained on large-scale data (usually a foundation model ), is adapted such that it can generate images of novel, user-provided concepts.
The script outputs an image file based on the model's interpretation of the prompt. [8] Generated images are tagged with an invisible digital watermark to allow users to identify an image as generated by Stable Diffusion, [8] although this watermark loses its efficacy if the image is resized or rotated. [51]
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
The word with embeddings most similar to the topic vector might be assigned as the topic's title, whereas far away word embeddings may be considered unrelated. As opposed to other topic models such as LDA , top2vec provides canonical ‘distance’ metrics between two topics, or between a topic and another embeddings (word, document, or otherwise).
Instead of an autoregressive Transformer, DALL-E 2 uses a diffusion model conditioned on CLIP image embeddings, which, during inference, are generated from CLIP text embeddings by a prior model. [22] This is the same architecture as that of Stable Diffusion , released a few months later.
Upload file; Special pages; Permanent link; Page information; ... fastText is a library for learning of word embeddings and text classification created by Facebook's ...
Make a small multilayered perceptron , so that for any image , the post-processed vector (()) has the same dimensions as an encoded token. That is an "image token". Then, one can interleave text tokens and image tokens. The compound model is then fine-tuned on an image-text dataset.
A text-to-image prompt commonly includes a description of the subject of the art, the desired medium (such as digital painting or photography), style (such as hyperrealistic or pop-art), lighting (such as rim lighting or crepuscular rays), color, and texture. [51] Word order also affects the output of a text-to-image prompt.