Google Imagen Article

An unprecedented level of photorealism and language understanding is provided by Google Imagen, which is a text-to-image diffusion model. As a result, the COCO dataset reached a state-of-the-art FID score of 7.27 without training. Images with text aligned correctly and sample quality are more accurate when compared with other models by human raters.

There are several ethical challenges facing text-to-image research broadly. The potential risks of misuse raise concerns regarding responsible open-sourcing of code and demos. Imagen relies on text encoders trained on uncrated web-scale data and inherits large language models' social biases and limitations. Imagen may run into the danger of dropping modes of data distribution, which may further compound the social consequence of dataset bias. A conceptual vocabulary around the potential harms of text-to-image models and established metrics of evaluation metrics are essential components of establishing responsible model release practices.

In this week's issue of the journal Imagen, we look at how large pre-trained frozen text encoders are very effective for the text-to-image task. We show that scaling the trained text encoder size is more important than scaling the diffusion model size. Human raters find Imagen samples on par with reference images regarding image-text alignment.

Over other methods in image-text alignment and image fidelity, human users prefer Imagen. Draw Bench is a comprehensive, challenging benchmark that tests for compositionality, cardinality, spatial relations, long-form text, rare words, and difficult prompts. Human raters strongly prefer Imagen over any other method of text-based analysis.

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