Imagen is a new AI system developed by the Google AI team that generates photorealistic images from text input.

In the real world, data is typically presented in a variety of formats. Images, for example, are frequently paired with tags and text explanations; the text may use images to better explain the main topic of the article. Different statistical characteristics distinguish different modalities. 

Texts are typically represented as discrete word count vectors, whereas images are typically represented as pixel intensities or feature extractor outputs. Because different information resources have different statistical features, determining the link between different modalities is critical.

Multimodal learning has emerged as a promising method for representing the combination of representations from various modalities. Text-to-image synthesis and image-text contrastive learning are two recent examples of multimodal learning.

Imagen, a text-to-image diffusion model that combines, is introduced in a new Google Brain Research. Diffusion models with high fidelity In text-to-image synthesis, this model provides an unprecedented level of photorealism and language understanding. 

Imagen's main finding is that text embeddings from massive LMs pretrained on text-only corpora are astonishingly effective for text-to-image synthesis, in contrast to previous work that used only image-text data for model training.

A frozen T5-XXL encoder converts input text into a series of embeddings, which is then followed by a 6464 image diffusion model, and two super-resolution diffusion models generate 256256 and 10241024 images. All diffusion models are conditioned on the text embedding sequence and use classifier-free guidance.

Imagen employs novel sampling techniques that allow for the use of large guide weights while maintaining sample quality. The resulting images have improved image-text alignment over what was previously possible. Imagen creates remarkable results despite the fact that the theory is simple and easy to train

Imagen outperforms other approaches on COCO, according to their findings, with a zero-shot FID-30K of 7.27, far outperforming previous work like GLIDE and concurrent work like DALL-E 2. 

According to their paper, their zero-shot FID score outperforms state-of-the-art COCO-trained models like Make-A-Scene. In image-text alignment, human raters report that Imagen-generated samples are comparable to the reference images on COCO captions.

DrawBench, a new structured set of test questions for evaluating text-to-image conversions, was also introduced by the team. DrawBench allows for deeper insights through a multi-dimensional evaluation of text-to-image models by using text prompts designed to investigate distinct semantic features of models.

Imagen outperforms other current approaches in DrawBench by a wide margin, according to extensive testing despite the fact that the theory is simple and easy to train

human assessment Their work also demonstrates the clear advantages of using large pre-trained language models as Imagen's text encoder over multimodal embeddings like CLIP.

Despite significant work auditing picture-to-text and image labelling models for forms of social bias, researchers say there has been relatively little work on social bias evaluation methods for text-to-image models.

They believe this is critical for future research and intend to investigate benchmark evaluations for social and cultural bias. This includes, for example, determining whether the normalized pointwise mutual information metric can be used to quantify biases in image generation models. 

They also emphasize the urgent need to develop The conceptual vocabulary surrounding the potential dangers of text-to-image models. They believe that this will aid in the development of evaluation criteria as well as the responsible release of models.

This article is based on the study 'Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding.' All credit for this research goes to the project's researchers.