![]() MagicMix, a novel method based on text-conditioned image diffusion-based generative models, is proposed to solve this. What would you draw if you were asked to combine “a corgi and a coffee machine?”. Of course, such a task is difficult since even a human user may not know how the mixed objects should look at the end. We are trying to fuse two concepts to generate a new object, and you don’t know what a corgi-alike coffee machine should look like. On the other hand, in semantic mixing, this is unknown information. It can be confused with semantic mixing, but in the compositional generation, each individual component has already been known. Compositional generation mixes multiple components into a single, more complex scene. For example, in style transfer, the content image is preserved while transferring the style of another one. Semantic mixing is conceptually different from other image editing and generation tasks. What if we are interested in semantically blending two separate ideas to synthesize a fresh concept? This is what MagicMix is trying to achieve. However, what if you wanted to mix two objects? Unlike style transfer, where you preserve the shape of the object, semantic mixing aims to combine two objects in a meaningful way. In the end, you will get an image of the Mona Lisa drawn in Van Gogh style. That will preserve the structure of the Mona Lisa but change the style using Van Gogh’s image. You can take Mona Lisa and ask to transfer the style from the “Starry Light” from Van Gogh. Moreover, it is also possible to transfer styles. Regardless of the new combination, each object instance is known, given the learned priors. For example, you can ask them to generate “Pikachu as a gladiator, riding a horse in space,” and they will produce a relatively realistic image, despite the concept being totally new. These models are trained with extremely large image-text caption pairs.īecause of the strong semantic prior learned from a huge collection of image-caption pairings, such models may even produce new concepts by combining different compositions. They can generate realistic-looking images given a text prompt. Urn:oclc:870981928 Republisher_date 20170619164143 Republisher_operator Republisher_time 691 Scandate 20170619061655 Scanner -scale text-conditioned image generation models have shown impressive results in recent years. OL16664287W Page-progression lr Page_number_confidence 86.24 Pages 678 Ppi 300 Related-external-id urn:isbn:0141335742 Sanrafaelpubliclibrary Edition First U.S. What if he's now attached to Roman ways? Does he still need his old friends? As the daughter of the goddess of war and wisdom, Annabeth knows she was born to be a leader, but never again does she want to be without Seaweed Brain by her sideĪccess-restricted-item true Addeddate 18:08:43.5493 Bookplateleaf 0008 Boxid IA1152809 Boxid_2 CH128914 City New York Donor What more does Athena want from her? Annabeth's biggest fear, though, is that Percy might have changed. Annabeth already feels weighed down by the prophecy that will send seven demigods on a quest to find - and close - the Doors of Death. In her pocket Annabeth carries a gift from her mother that came with an unnerving demand: Follow the Mark of Athena. Annabeth hopes that the sight of their praetor Jason on deck will reassure the Romans that the visitors from Camp Half-Blood are coming in peace. With its steaming bronze dragon masthead, Leo's fantastical creation doesn't appear friendly. As Annabeth and her friends Jason, Piper, and Leo fly in on the Argo II, she can't blame the Roman demigods for thinking the ship is a Greek weapon. Just when she's about to be reunited with Percy - after six months of being apart, thanks to Hera - it looks like Camp Jupiter is preparing for war.
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