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Yang Zhao
Yang Zhao
Yang Zhao
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Yang Zhao (楊昭) (21 February 584[1] – 30 August 606[2]), formally Crown Prince Yuande (元德太子, literally "the discerning and nurturing crown prince"), posthumously honored as Emperor Xiaocheng (孝成皇帝, literally "the filial and successful emperor") with the temple name Shizong (世宗) during the brief reign of his son Yang Tong, was a crown prince of the Chinese Sui dynasty. He was the oldest son of Emperor Yang (Yang Guang) who predeceased his father. Yang Zhao was described as a fat-looking man with great physical strength.

Key Information

Background

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Yang Zhao was born in February 584, while his father Yang Guang was the Prince of Jin under his grandfather Emperor Wen. His mother was Yang Guang's wife Princess Xiao, and he was their oldest son. In his infancy, he was raised by his grandparents Emperor Wen and Empress Dugu. In 590, Emperor Wen created him the Prince of Henan. He initially married the daughter of the official Cui Hongsheng (崔弘昇) as his wife and princess, but after Princess Cui's aunt, who married his uncle Yang Jun the Prince of Qin, was discovered to have poisoned Yang Jun over her jealousness for Yang Jun's concubines in 597, Yang Zhao divorced his wife and married the daughter of the official Wei Shou (韋壽) the Duke of Hua as his wife and princess. After his father Yang Guang displaced his uncle Yang Yong as crown prince in 600, Yang Zhao was created his father's old title of Prince of Jin in 601.

As crown prince

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In 604, Emperor Wen died—a death that most traditional historians believed to be a murder ordered by Yang Guang, but admitted a lack of direct evidence—and Yang Guang took the throne as Emperor Yang. Soon, believing the words of the sorcerer Zhangchou Taiyi (章仇太翼) that the geography of the region or feng shui surrounding the capital Chang'an was unsuitable for him, Emperor Yang took up residence at Luoyang and made it the eastern capital. He left Yang Zhao in charge at Chang'an. In spring 605, Emperor Yang created Yang Zhao crown prince. Emperor Yang considered restoring Princess Cui, but when he sent a messenger to the house of Princess Cui's uncle Cui Hongdu (崔弘度) to discuss the matter, Cui Hongdu did not realize that an imperial messenger had arrived and therefore did not respond. Emperor Yang was displeased, and the matter was not again discussed.

In 606, Yang Zhao went to Luoyang to greet his parents, and he spent several months there. He requested permission to remain longer at Luoyang, but Emperor Yang denied it. Meanwhile, because he was required to kneel before his father and had to do so repeatedly while at Luoyang, and he was severely overweight, he became ill from the fatigue, and he died in summer 606. When his son Yang Tong later took the throne in 618 during the dynasty's disintegration, he was posthumously honored as an emperor.

Family

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Consorts and their respective issue(s):

  • Princess Consort of Henan, of the Cui clan of Boling (河南王妃 博陵崔氏)
  • Crown Princess, of the Wei clan of Jingzhao (太子妃 京兆韦氏)
    • Yang You, Emperor Gong (隋恭帝 楊侑, 605 – 14 September 619 ), third son
  • Older Lady Liu, of the Liu clan (大刘良娣 刘氏)
    • Yang Tong, Emperor Gong (隋恭帝 楊侗; 600s–619), second son
  • Younger Lady Liu, of the Liu clan (小劉良娣 劉氏)
    • Yang Tan, Prince of Yan (燕王 楊倓, 603 – 618), first son

References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Yang Zhao

View on Grokipedia
from Grokipedia
Yang Zhao is a Member of Technical Staff at xAI, formerly a Research Scientist at Google DeepMind, specializing in generative AI models, particularly in areas such as text-to-image generation and diffusion models. He is best known for his contributions to efficient text-to-image technologies, including co-authoring the UFOGen paper, which introduces a novel generative model for ultra-fast, one-step text-to-image synthesis using diffusion GANs. His work also includes publications on mobile diffusion models for subsecond text-to-image generation on devices and image customization with text-to-image diffusion models. Zhao's research focuses on advancing , with notable efforts in taming encoders for zero fine-tuning image customization and developing Instruct-Imagen for multi-modal instruction-based image generation.

Early Life and Education

Childhood and Early Interests

Specific anecdotes from Yang Zhao's childhood, such as self-taught programming or school projects that sparked his passion for AI, are not publicly documented in available sources. His professional profiles indicate a strong interest in technology and computer science, leading to his pursuit of higher studies in the United States at the University at Buffalo.

Academic Background

He then earned his PhD in Computer Science from the , completing the degree between 2018 and 2021, with his doctoral research focusing on , including contributions to and related techniques in machine learning. During his , Zhao gained practical experience through internships, including a research internship at Baidu USA in 2020, where he contributed to projects in AI research. He also served as a research intern at in 2021, working on advancements in and efficient AI systems.

Professional Career

Positions at Google

Yang Zhao joined Google DeepMind as a Research Scientist in December 2024, where he focused on , including advancements in text-to-image synthesis technologies. His role involved broad responsibilities in developing efficient , with team affiliations centered on and on-device AI systems. Prior to his full-time position, Zhao served as a Research Intern at starting in May 2021, contributing to early-stage AI projects during his PhD studies at . This internship marked his transition from academia to industry research, building on his academic background. At DeepMind, Zhao participated in key internal projects and collaborations that advanced , leading to high-impact publications in . These efforts included work on diffusion-based models and their integration with GAN architectures, emphasizing efficiency for large-scale applications. His contributions at Google DeepMind, from December 2024 to October 2025, focused on optimizing models for practical deployment, such as on-device .

Role at xAI

Yang Zhao joined xAI as a Member of Technical Staff in October 2025, following his tenure as a Research Scientist at Google DeepMind. In this role, he contributes to the development of advanced AI models, with a focus on efficient generative systems that build on his prior expertise in text-to-image technologies. His LinkedIn profile indicates involvement in projects such as "Cooking Grok Imagine," aimed at enhancing Grok's image generation capabilities. His prior experience at DeepMind has informed his work at xAI by providing insights into multimodal on-device models.

Research Contributions

Work on Generative Models

Yang Zhao's research in centers on advancing efficient architectures for image synthesis, particularly through the integration of diffusion processes and generative adversarial networks (GANs). His work explores how diffusion models can generate high-quality images by iteratively , while GANs enhance this process by incorporating adversarial training to improve realism and diversity in outputs. These core concepts have been pivotal in his contributions, aiming to balance with generative fidelity, especially for resource-constrained environments. During his PhD at , Zhao's thesis laid foundational groundwork for , including and improvements to GAN training. This academic evolution transitioned seamlessly into his professional roles, where he applied these principles to scalable systems at Google DeepMind and later xAI, adapting generative techniques for practical deployment in multimodal AI frameworks, including hybrids of diffusion models and GANs. For instance, his involvement in projects like UFOGen exemplifies this progression by showcasing efficient diffusion GANs for rapid image generation. The impact of Zhao's research extends to significant efficiency improvements in , enabling to operate on with reduced and , which has influenced broader advancements in on-device AI applications. By optimizing sampling techniques and , his approaches have contributed to reducing the of , making them viable for real-world scenarios like mobile multimedia generation. This has fostered greater accessibility in the field, with his methods cited in subsequent works on lightweight generative systems.

Text-to-Image Synthesis Projects

Yang Zhao has made significant contributions to text-to-image synthesis through innovative diffusion-based approaches that emphasize efficiency and customization. In particular, his work on UFOGen introduces a hybrid diffusion-GAN model designed for ultra-fast, one-step generation of high-quality images from textual prompts. This method addresses the high computational costs of traditional diffusion models by integrating a novel diffusion-GAN objective and initializing with pre-trained diffusion models, enabling single-step synthesis while maintaining coherence and detail in generated outputs. Building on these foundations, Zhao's involvement in MobileDiffusion advances on-device text-to-image generation, achieving subsecond inference times on . This project optimizes both and to tackle challenges in and speed, making accessible for resource-constrained environments without sacrificing image fidelity. By focusing on lightweight designs and efficient sampling, MobileDiffusion exemplifies efforts to deploy text-to-image technologies in real-world, . Zhao's research also extends to customization techniques in text-to-image diffusion models, as seen in the development of methods for zero fine-tuning image personalization. The "Taming Encoder" approach refines the encoding process to allow subject-driven customization directly within diffusion frameworks, enabling users to generate tailored images from text descriptions without extensive retraining. This innovation enhances flexibility in synthesis tasks, addressing limitations in adapting pre-trained models to specific user inputs while preserving overall efficiency. These projects collectively highlight Zhao's focus on overcoming key barriers in text-to-image synthesis, such as inference latency and adaptability, through targeted advancements in diffusion-based architectures. His association with Google DeepMind has positioned him to contribute to on-device multimodal models, including variants like Gemini Nano Banana, which build on similar principles for efficient, mobile-friendly generation.

Notable Publications

UFOGen and Diffusion GANs

UFOGen represents a significant advancement in , introduced as a novel diffusion GAN model designed for ultra-fast, one-step text-to-image synthesis. Co-authored by Yang Zhao and colleagues, the model was detailed in a paper published on arXiv in November 2023 and presented at the in . By integrating diffusion processes with a GAN objective, UFOGen enables the generation of high-quality images directly from textual prompts in a single , addressing the computational inefficiency of traditional multi-step diffusion models. A core innovation of UFOGen is the "You Forward Once" (YFO) mechanism, which modifies the to predict clean image samples (x₀) directly from the forward diffusion process, bypassing iterative denoising. This is complemented by an improved reconstruction loss using the between predicted and actual clean samples , which enhances training stability and supports one-step sampling. For large-scale training, UFOGen initializes both the generator and discriminator with pre-trained diffusion models like Stable Diffusion, allowing efficient fine-tuning on web-scale text-image data in a single stage, while overcoming issues like mode collapse and text-visual misalignment common in prior diffusion-GAN hybrids. In terms of performance, UFOGen achieves a score of 22.5 on the MS-COCO-2017 dataset with one sampling step, outperforming Progressive Distillation (FID-5k of 37.2 for 1 step) and CFG-Aware Distillation (FID-5k of 24.2 for 8 steps), while being competitive with InstaFlow models (FID-5k of 22.4-23.4 for one step). It also attains a CLIP score of 0.311, indicating strong text-image alignment, and generates images in just 0.09 seconds per image, significantly faster than multi-step baselines like Stable Diffusion (2.9 seconds for 50 steps). Compared to GAN-based models such as GigaGAN, UFOGen offers greater flexibility for downstream tasks like controllable generation, with qualitative advantages in sharpness and detail. The paper has garnered 163 citations as of January 2026, underscoring its impact in the field.

MobileDiffusion and On-Device Generation

MobileDiffusion is a pioneering text-to-image diffusion model developed by Yang Zhao and colleagues at Google, aimed at enabling subsecond image generation directly on . Published as a preprint in November 2023 and later accepted to the European Conference on Computer Vision (ECCV) in 2024, the project addresses the challenges of deploying on by significantly reducing model size and inference latency. The model achieves high-quality in under 0.5 seconds on premium and devices, with a compact size of approximately 520 million , making it suitable for while preserving through . Key techniques in MobileDiffusion focus on and optimization to enable instant generation. Architectural optimizations include a lightweight UNet with more transformer blocks at the bottleneck for efficiency, skipped self-attention at higher resolutions, and separable convolutions in deeper layers to minimize computational overhead. The text encoder employs a small CLIP-ViT/L14 variant with 125 million parameters, while the image decoder uses a pruned that compresses images into an 8-channel , resulting in a lightweight decoder of just 9.8 million parameters that outperforms Stable Diffusion in quality metrics like (30.2) and (0.84). For sampling, the model integrates a DiffusionGAN hybrid approach, fine-tuning a pre-trained diffusion UNet to perform one-step denoising, which converges rapidly in under 10,000 iterations and eliminates multi-step sampling delays typical of traditional diffusion models. The preprint has garnered significant attention in the research community, accumulating 67 citations as of the latest available data, reflecting its impact on efficient for . These advancements in MobileDiffusion have informed broader on-device multimodal generation efforts. By prioritizing low-latency inference and reduced parameter counts, the project sets a benchmark for integrating advanced text-to-image synthesis into everyday mobile experiences without relying on cloud resources.

Personal Life and Recognition

Awards and Citations

Yang Zhao's research has garnered significant academic impact, with his publications collectively cited over 1,100 times according to metrics as of late 2025. His work in , particularly in text-to-image synthesis, has contributed to this recognition, exemplified by the UFOGen paper, which has received 163 citations since its 2024 publication. These citations reflect the influence of his contributions on efficient diffusion-based generation techniques. Zhao's profile indicates an of 16, signifying that 16 of his papers have each been cited at least 16 times, underscoring the sustained relevance of his research in areas like mobile diffusion models and . Key papers such as MobileDiffusion, with 67 citations, further highlight the practical adoption of his on-device AI innovations. No specific formal awards or fellowships directly attributed to Zhao were identified in publicly available academic records from his time at or subsequent roles.

Public Engagements

Yang Zhao has actively participated in major computer vision conferences to present his research on . He co-authored the paper "UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs," which was presented at the in 2024, where the work introduced a novel diffusion GAN approach for ultra-fast text-to-image synthesis. In addition, Zhao contributed to the presentation of "MobileDiffusion: Instant Text-to-Image Generation on Mobile Devices" at the European Conference on Computer Vision (ECCV) in 2024, focusing on efficient on-device generation techniques for mobile platforms. This presentation highlighted advancements in latent diffusion models optimized for sub-second inference on smartphones. Zhao has also engaged with the broader AI community through public writing, including co-authoring a detailed blog post on the Google Research site in January 2024, which explained the development and deployment of MobileDiffusion for rapid text-to-image generation on iOS and Android devices.
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