Each block uses edge-supervised cross-attention learning to further improve image quality.

Text-to-image generation has recently witnessed remarkable achievements.

Text-to-image generation has recently witnessed remarkable achievements. We introduce a text-conditional image diffusion model, termed RAPHAEL, to generate highly artistic images, which accurately portray the text prompts, encompassing multiple nouns, adjectives, and verbs. This is achieved by stacking tens of mixture-of-experts (MoEs) layers, i.e., space-MoE and time-MoE layers, enabling billions of diffusion paths (routes) from the network input to the output. Each path intuitively functions as a painter for depicting a particular textual concept onto a specified image region at a diffusion timestep. Comprehensive experiments reveal that RAPHAEL outperforms recent cutting-edge models, such as Stable Diffusion, ERNIE-ViLG 2.0, DeepFloyd, and DALL-E 2, in terms of both image quality and aesthetic appeal. Firstly, RAPHAEL exhibits superior performance in switching images across diverse styles, such as Japanese comics, realism, cyberpunk, and ink illustration. Secondly, a single model with three billion parameters, trained on 1,000 A100 GPUs for two months, achieves a state-of-the-art zero-shot FID score of 6.61 on the COCO dataset.

Each block uses edge-supervised cross-attention learning to further improve image quality.

Text-to-image generation has recently witnessed remarkable achievements.

Text-to-image generation has recently witnessed remarkable achievements. We introduce a text-conditional image diffusion model, termed RAPHAEL, to generate highly artistic images, which accurately portray the text prompts, encompassing multiple nouns, adjectives, and verbs. This is achieved by stacking tens of mixture-of-experts (MoEs) layers, i.e., space-MoE and time-MoE layers, enabling billions of diffusion paths (routes) from the network input to the output. Each path intuitively functions as a "painter" for depicting a particular textual concept onto a specified image region at a diffusion timestep. Comprehensive experiments reveal that RAPHAEL outperforms recent cutting-edge models, such as Stable Diffusion, ERNIE-ViLG 2.0, DeepFloyd, and DALL-E 2, in terms of both image quality and aesthetic appeal.

In London last night, the 20th Bafta games awards celebrated a year that was stacked with critically acclaimed games. Taking place against the backdrop of an unprecedented year of layoffs and studio closures in the gaming industry, acknowledged by Bafta chair Sara Putt in her speech at the beginning of the evening, it was a much-needed night of recognition of the creative efforts of the video game development community. The sprawling Dungeons & Dragons-inspired role-playing game Baldur's Gate 3 won five awards, including the public voted EE players' choice award and best game, alongside music, narrative and best performer in a supporting role (won by Andrew Wincott for his role at the devilish Raphael). Nintendo picked up the family and multiplayer awards for the exuberant Super Mario Bros Wonder, and technical achievement for The Legend of Zelda: Tears of the Kingdom. Alan Wake 2, the arresting, idiosyncratic horror game from Finnish studio Remedy, won artistic achievement and audio achievement.

Previous research has shown that Artificial Intelligence is capable of distinguishing between authentic paintings by a given artist and human-made forgeries with remarkable accuracy, provided sufficient training. However, with the limited amount of existing known forgeries, augmentation methods for forgery detection are highly desirable. In this work, we examine the potential of incorporating synthetic artworks into training datasets to enhance the performance of forgery detection. Our investigation focuses on paintings by Vincent van Gogh, for which we release the first dataset specialized for forgery detection. To reinforce our results, we conduct the same analyses on the artists Amedeo Modigliani and Raphael. We train a classifier to distinguish original artworks from forgeries. For this, we use human-made forgeries and imitations in the style of well-known artists and augment our training sets with images in a similar style generated by Stable Diffusion and StyleGAN. We find that the additional synthetic forgeries consistently improve the detection of human-made forgeries. In addition, we find that, in line with previous research, the inclusion of synthetic forgeries in the training also enables the detection of AI-generated forgeries, especially if created using a similar generator.

Toward the end of Baldur's Gate 3, widely considered the most outstanding video game released this year, you can literally go to hell. If you do, you'll have a showdown with the game's equivalent of the devil, a charismatic yet demonic trickster who calls himself Raphael. Naturally, developer Larian Studios wanted it to feel monumental. So they decided that the battle should be accompanied by a song, and that Raphael should be the one singing it. "The idea for a song to be performed by Raphael himself came from our director Swen Vincke about six months before the release of the game," says Borislav Slavov, Baldur's Gate 3's music director.

Authenticating works of art is far from an exact science, but a madonna and child painting has sparked a furious row, being dubbed "the battle of the AIs", after two separate scientific studies arrived at contradictory conclusions. Both studies used state-of-the art AI technology. Months after one study proclaimed that the so-called de Brécy Tondo, currently on display at Bradford council's Cartwright Hall Art Gallery, is "undoubtedly" by Raphael, another has found that it cannot be by the Renaissance master. In January, research teams from the universities of Nottingham and Bradford announced the findings of facial recognition technology, which compared the faces in the Tondo with those in Raphael's Sistine Madonna altarpiece, commissioned in 1512. Having used "millions of faces to train an algorithm to recognise and compare facial features", they stated: "The similarity between the madonnas was found to be 97%, while comparison of the child in both paintings produced an 86% similarity."