Diffusion models have gained increasing attention for their impressive generation abilities but currently struggle with rendering accurate and coherent text. To address this issue, we introduce TextDiffuser, focusing on generating images with visually appealing text that is coherent with backgrounds. TextDiffuser consists of two stages: first, a Transformer model generates the layout of keywords extracted from text prompts, and then diffusion models generate images conditioned on the text prompt and the generated layout. Additionally, we contribute the first large-scale text images dataset with OCR annotations, MARIO-10M, containing 10 million image-text pairs with text recognition, detection, and character-level segmentation annotations. We further collect the MARIO-Eval benchmark to serve as a comprehensive tool for evaluating text rendering quality.

To create a passage representation, the passage title and text are concatenated ([CLS]title [SEP]passage [SEP]), following common practice (Karpukhin et al., 2020). We retrieve top 10 passages and use them as input to mGEN. We differentiate those paragraphs from the question using special tokens (

vs. He graduated with a B.S. degree in Biology in 1957. As in the case of machine translation, we found that the language code does not need to be specified during inference as our model learns the question language automatically. Yet, we found that training with language codes is particularly useful to augment training data for Ltarget without any question data in Ltarget.

Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance, our model automatically understands the underlying task and performs the same task on a new query image following the text guidance.

By filtering the content that users see, social media platforms have the ability to influence users' perceptions and decisions, from their dining choices to their voting preferences. This influence has drawn scrutiny, with many calling for regulations on filtering algorithms, but designing and enforcing regulations remains challenging. In this work, we examine three questions. First, given a regulation, how would one design an audit to enforce it? Second, does the audit impose a performance cost on the platform?

Fans want to reclaim the music and myth of Michael Jackson in the new biopic while critics call for accountability. Still from, which opened April 24. Is truth determined by the size of the audience it reaches? If so, --a new film about the pop singer Michael Jackson that is on track to have the biggest-ever opening for a music biopic, with projected earnings of $70 million at the US box office, despite critics saying it sanitizes the reality of who Jackson actually was--intends to supplant the King of Pop as the apotheosis of artistic virtue. The film's release has sparked a familiar but newly intensified civil war online, between those eager to reclaim the music and myth of Jackson, and those who see any celebration of him as a failure of accountability.

With A.I., Anyone Can Be an Influencer TikTok and Instagram made it easy to monetize the physical self. Now the social-media-savvy can use A.I. to play with their identity, or overhaul it entirely. A few months ago, a forty-five-year-old homemaker living in Georgia, whom I'll call Robin, started playing around with an A.I. image generator. Growing up, Robin had loved reading; she dabbled in writing, too, but after her first child was born, the habit faded. A.I. offered something different--a kind of world-building that allowed her to project herself into places and situations she'd never inhabited.

The lack of publicly available high-quality and accurately labeled datasets has long been a major bottleneck for singing voice synthesis (SVS). To tackle this problem, we present M4Singer, a free-to-use Multi-style, Multi-singer Mandarin singing collection with elaborately annotated Musical scores as well as its benchmarks. Specifically, 1) we construct and release a large high-quality Chinese singing voice corpus, which is recorded by 20 professional singers, covering 700 Chinese pop songs as well as all the four SATB types (i.e., soprano, alto, tenor, and bass); 2) we take extensive efforts to manually compose the musical scores for each recorded song, which is necessary to the study of the prosody modeling for SVS. 3) To facilitate the use and demonstrate the quality of M4Singer, we conduct four different benchmark experiments: score-based SVS, controllable singing voice (CSV), singing voice conversion (SVC) and automatic music transcription (AMT). Audio samples can be found at http://m4singer.github.io.