While conceptually simple and easy to train, Imagen yields surprisingly strong results. Imagen outperforms other methods on COCO [38] with zero-shot FID-30K of 7.27, significantly outperforming prior work such asGLIDE [43](at 12.4) and the concurrent work ofDALL-E 2[56](at 10.4).

Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. Our key discovery is that generic large language models (e.g., T5), pretrained on text-only corpora, are surprisingly effective at encoding text for image synthesis: increasing the size of the language model in Imagen boosts both sample fidelity and image-text alignment much more than increasing the size of the image diffusion model. Imagen achieves a new state-of-the-art FID score of 7.27 on the COCO dataset, without ever training on COCO, and human raters find Imagen samples to be on par with the COCO data itself in image-text alignment. To assess text-to-image models in greater depth, we introduce DrawBench, a comprehensive and challenging benchmark for text-to-image models. With DrawBench, we compare Imagen with recent methods including VQ-GAN+CLIP, Latent Diffusion Models, and DALL-E 2, and find that human raters prefer Imagen over other models in side-by-side comparisons, both in terms of sample quality and image-text alignment.

Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. Our key discovery is that generic large language models (e.g., T5), pretrained on text-only corpora, are surprisingly effective at encoding text for image synthesis: increasing the size of the language model in Imagen boosts both sample fidelity and image-text alignment much more than increasing the size of the image diffusion model. Imagen achieves a new state-of-the-art FID score of 7.27 on the COCO dataset, without ever training on COCO, and human raters find Imagen samples to be on par with the COCO data itself in image-text alignment. To assess text-to-image models in greater depth, we introduce DrawBench, a comprehensive and challenging benchmark for text-to-image models. With DrawBench, we compare Imagen with recent methods including VQ-GAN CLIP, Latent Diffusion Models, and DALL-E 2, and find that human raters prefer Imagen over other models in side-by-side comparisons, both in terms of sample quality and image-text alignment.

Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. Our key discovery is that generic large language models (e.g., T5), pretrained on text-only corpora, are surprisingly effective at encoding text for image synthesis: increasing the size of the language model in Imagen boosts both sample fidelity and image-text alignment much more than increasing the size of the image diffusion model. Imagen achieves a new state-of-the-art FID score of 7.27 on the COCO dataset, without ever training on COCO, and human raters find Imagen samples to be on par with the COCO data itself in image-text alignment. To assess text-to-image models in greater depth, we introduce DrawBench, a comprehensive and challenging benchmark for text-to-image models. With DrawBench, we compare Imagen with recent methods including VQ-GAN CLIP, Latent Diffusion Models, and DALL-E 2, and find that human raters prefer Imagen over other models in side-by-side comparisons, both in terms of sample quality and image-text alignment.

Investigating the first half of this claim, the authors present several qualitative comparisons between Imagen and DALL-E 2 generated images. They also provide results from human evaluation experiments where people were asked to choose the most photorealistic image from a single text prompt or caption. Even before considering any results, immediately the authors have introduced a degree of subjectivity into their analysis that is inherent in human evaluation experiments. Therefore the results shown in [1] must be considered with care and a healthy level of skepticism. To provide some context to these results, the authors select some example comparisons shown to human raters and include these in the Appendix (definitely take a look at these -- for motivation, I've added an example from DALL-E 2 above). However, even with these examples, I find it difficult to make a clear judgement over which image should be preferred. Considering the copied examples shown in the figure above, personally I believe that some of DALL-E 2's generated images are more photorealistic than Imagen's, which demonstrates the issues of subjectivity when collecting results such as these. The authors choose to ask human raters'which image is more photorealistic?'

Researchers from Google's Brain Team have announced Imagen, a text-to-image AI model that can generate photorealistic images of a scene given a textual description. Imagen outperforms DALL-E 2 on the COCO benchmark, and unlike many similar models, is pre-trained only on text data. The model and several experiments were described in a paper published on arXiv. Imagen uses a Transformer language model to convert the input text into a sequence of embedding vectors. A series of three diffusion models then convert the embeddings into a 1024x1024 pixel image.

Basically, the system can create photorealistic images from input text. 'We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding,' says the official paper. 'Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation.' Google claims Imagen features an unprecedented degree of photorealism and a deep level of language understanding that surpasses its competitors. For it to work, the program takes texts -- let's say,'Three spheres made of glass falling into the ocean. The resulting images can be either photorealistic or more of an artistic interpretation.