Stable Diffusion

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最新综述!扩散语言模型全面盘点~
自动驾驶之心· 2025-08-19 23:32
Core Viewpoint - The article discusses the competition between two major paradigms in generative AI: Diffusion Models and Autoregressive (AR) Models, highlighting the emergence of Diffusion Language Models (DLMs) as a potential breakthrough in the field of large language models [2][3]. Group 1: DLM Advantages Over AR Models - DLMs offer parallel generation capabilities, significantly improving inference speed by achieving a tenfold increase compared to AR models, which are limited by token-level serial processing [11][12]. - DLMs utilize bidirectional context, enhancing language understanding and generation control, allowing for finer adjustments in output characteristics such as sentiment and structure [12][14]. - The iterative denoising mechanism of DLMs allows for corrections during the generation process, reducing the accumulation of early errors, which is a limitation in AR models [13]. - DLMs are naturally suited for multimodal applications, enabling the integration of text and visual data without the need for separate modules, thus enhancing the quality of joint generation tasks [14]. Group 2: Technical Landscape of DLMs - DLMs are categorized into three paradigms: Continuous Space DLMs, Discrete Space DLMs, and Hybrid AR-DLMs, each with distinct advantages and applications [15][20]. - Continuous Space DLMs leverage established diffusion techniques from image models but may suffer from semantic loss during the embedding process [20]. - Discrete Space DLMs operate directly on token levels, maintaining semantic integrity and simplifying the inference process, making them the mainstream approach in large parameter models [21]. - Hybrid AR-DLMs combine the strengths of AR models and DLMs, balancing efficiency and quality for tasks requiring high coherence [22]. Group 3: Training and Inference Optimization - DLMs utilize transfer learning to reduce training costs, with methods such as initializing from AR models or image diffusion models, significantly lowering data requirements [30][31]. - The article outlines three main directions for inference optimization: parallel decoding, masking strategies, and efficiency technologies, all aimed at enhancing speed and quality [35][38]. - Techniques like confidence-aware decoding and dynamic masking are highlighted as key innovations to improve the quality of generated outputs while maintaining high inference speeds [38][39]. Group 4: Multimodal Applications and Industry Impact - DLMs are increasingly applied in multimodal contexts, allowing for unified processing of text and visual data, which enhances capabilities in tasks like visual reasoning and joint content creation [44]. - The article presents various case studies demonstrating DLMs' effectiveness in high-value vertical applications, such as code generation and computational biology, showcasing their potential in real-world scenarios [46]. - DLMs are positioned as a transformative technology in industries, with applications ranging from real-time code generation to complex molecular design, indicating their broad utility [46][47]. Group 5: Challenges and Future Directions - The article identifies key challenges facing DLMs, including the trade-off between parallelism and performance, infrastructure limitations, and scalability issues compared to AR models [49][53]. - Future research directions are proposed, focusing on improving training objectives, building dedicated toolchains, and enhancing long-sequence processing capabilities [54][56].
ICCV 2025|训练太复杂?对图片语义、布局要求太高?图像morphing终于一步到位
机器之心· 2025-07-18 00:38
Core Viewpoint - The article introduces FreeMorph, a novel training-free image morphing method that enables high-quality and smooth transitions between two input images without the need for pre-training or additional annotations [5][32]. Group 1: Background and Challenges - Image morphing is a creative task that allows for smooth transitions between two distinct images, commonly seen in animations and photo editing [3]. - Traditional methods relied on complex algorithms and faced challenges with high training costs, data dependency, and instability in real-world applications [4]. - Recent advancements in deep learning methods like GANs and VAEs have improved image morphing but still struggle with training costs and adaptability [4][5]. Group 2: FreeMorph Methodology - FreeMorph addresses the challenges of image morphing by eliminating the need for training, achieving effective morphing with just two images [5]. - The method incorporates two key innovations: spherical feature aggregation and prior-driven self-attention mechanisms, enhancing the model's ability to maintain identity features and ensure smooth transitions [11][32]. - A step-oriented motion flow is introduced to control the transition direction, allowing for a coherent and gradual morphing process [21][32]. Group 3: Experimental Results - FreeMorph has been evaluated against existing methods, demonstrating superior performance in generating high-fidelity results across diverse scenarios, including images with varying semantics and layouts [27][30]. - The method effectively captures subtle changes, such as color variations in objects or nuanced facial expressions, showcasing its versatility [27][30]. Group 4: Limitations - Despite its advancements, FreeMorph has limitations, particularly when handling images with significant semantic or layout differences, which may result in less smooth transitions [34]. - The method inherits biases from the underlying Stable Diffusion model, affecting accuracy in specific contexts, such as human limb structures [34].
The State of Generative Media - Gorkem Yurtseven, FAL
AI Engineer· 2025-07-16 20:19
Generative Media Platform & Market Overview - File.ai 将自身定义为一个生成式媒体平台,专注于视频、音频和图像的生成 [1] - 生成式媒体正在改变社交媒体、广告、营销、时尚、电影、游戏和电子商务等行业,最终将影响所有内容 [10] - 广告行业预计将成为首批大规模受到生成式媒体影响的行业之一,行业规模预计将会扩大 [13] AI Model Development & Trends - 边缘计算的创作边际成本正在接近于零,但故事叙述和创造力仍然至关重要 [8][9] - 视频模型的使用率正在快速增长,从10月初的几乎为零增长到2月份的18%,并且持续增长,目前约为30% [25][26] - 视频模型预计将比图像生成市场大 100 到 250 倍,因为视频模型计算密集程度是图像的 20 倍,互动性是图像的 5 倍,并且将影响更多行业 [27] - 视频生成技术将朝着更快、更便宜的方向发展,最终实现实时视频生成,这将对用户互动方式产生重大影响,模糊游戏和电影之间的界限 [31] - 图像模型也在不断改进,例如 Flux context 和 GPT4o 引入了新的编辑功能和更好的文本渲染功能,为行业开辟了更多用例 [34] Applications of Generative Media - 个性化广告是生成式媒体的一个重要应用方向,可以针对不同的人口统计群体快速生成大量不同版本的广告,或者根据用户的浏览行为动态生成广告 [15] - 电子商务是生成式媒体的另一个重要应用领域,特别是虚拟试穿技术,许多零售商和初创公司都在采用这项技术 [21][22] - AI 正在帮助创建互动和个性化的体验,例如 A24 电影《内战》的互动广告活动,用户可以将自己的自拍照放在时代广场的玩具士兵上 [18][19]
ICML 2025|多模态理解与生成最新进展:港科联合SnapResearch发布ThinkDiff,为扩散模型装上大脑
机器之心· 2025-07-16 04:21
Core Viewpoint - The article discusses the introduction of ThinkDiff, a new method for multimodal understanding and generation that enables diffusion models to perform reasoning and creative tasks with minimal training data and computational resources [3][36]. Group 1: Introduction to ThinkDiff - ThinkDiff is a collaborative effort between Hong Kong University of Science and Technology and Snap Research, aimed at enhancing diffusion models' reasoning capabilities with limited data [3]. - The method allows diffusion models to understand the logical relationships between images and text prompts, leading to high-quality image generation [7]. Group 2: Algorithm Design - ThinkDiff transfers the reasoning capabilities of large visual language models (VLM) to diffusion models, combining the strengths of both for improved multimodal understanding [7]. - The architecture involves aligning VLM-generated tokens with the diffusion model's decoder, enabling the diffusion model to inherit the VLM's reasoning abilities [15]. Group 3: Training Process - The training process includes a vision-language pretraining task that aligns VLM with the LLM decoder, facilitating the transfer of multimodal reasoning capabilities [11][12]. - A masking strategy is employed during training to ensure the alignment network learns to recover semantics from incomplete multimodal information [15]. Group 4: Variants of ThinkDiff - ThinkDiff has two variants: ThinkDiff-LVLM, which aligns large-scale VLMs with diffusion models, and ThinkDiff-CLIP, which aligns CLIP with diffusion models for enhanced text-image combination capabilities [16]. Group 5: Experimental Results - ThinkDiff-LVLM significantly outperforms existing methods on the CoBSAT benchmark, demonstrating high accuracy and quality in multimodal understanding and generation [18]. - The training efficiency of ThinkDiff-LVLM is notable, achieving optimal results with only 5 hours of training on 4 A100 GPUs, compared to other methods that require significantly more resources [20][21]. Group 6: Comparison with Other Models - ThinkDiff-LVLM exhibits capabilities comparable to commercial models like Gemini in everyday image reasoning and generation tasks [25]. - The method also shows potential in multimodal video generation by adapting the diffusion decoder to generate high-quality videos based on input images and text [34]. Group 7: Conclusion - ThinkDiff represents a significant advancement in multimodal understanding and generation, providing a unified model that excels in both quantitative and qualitative assessments, contributing to the fields of research and industrial applications [36].
马斯克疯狂点赞,Lovart凭什么是世界上第一个设计智能体?
Sou Hu Cai Jing· 2025-07-12 05:18
Core Insights - Lovart, also known as "星流AI" in China, has rapidly gained attention in the AI application field, with significant engagement on social media and a surge of users seeking trial invitations [1][3] - The emergence of Lovart signifies a shift from traditional AI tools to a new model of creative collaboration, redefining the relationship between creators and AI [3][19] Group 1: Old World Challenges - The previous generation of AI tools, referred to as AIGC 1.0, only addressed the initial stages of the creative process, leaving creators to handle the majority of integration and editing tasks manually [6] - The introduction of workflow tools like ComfyUI marked the AIGC 2.0 era, but their complexity deterred most designers, making them more suitable for AI experts rather than general creators [6][7] Group 2: New Model Introduction - Lovart's founder, Chen Mian, identified that creators need a comprehensive solution rather than just advanced tools, likening the new model to a "chef team" that handles all aspects of creative work [7][8] - The core idea of Lovart is to transform AI from a mere tool into a "Creator Team," allowing users to act as clients who provide input while AI manages the execution [8][19] Group 3: Interaction Redefined - Lovart's product design emphasizes a natural interaction model, using a metaphor of a "table" where creators can easily communicate their needs and see the results in real-time [9][11] - The interface consists of a large canvas for visual work and a dialogue box for user instructions, streamlining the creative process and enhancing user experience [10][11] Group 4: Market Positioning - Lovart strategically targets the overlooked "creative individual" and professional consumer segments, avoiding direct competition with industry giants like Adobe and Midjourney [14] - The company focuses on creating unique user experiences by integrating domain knowledge with AI capabilities, rather than simply improving existing tools [14][15] Group 5: Future Outlook - Lovart is positioned at the forefront of the emerging Agent era, which is expected to revolutionize the creative industry by enhancing collaboration and efficiency [15][19] - The founder believes that the true potential of AI lies in its ability to replace not just individual tools but entire collaborative teams, fundamentally changing the creative landscape [19][21]
WPP's dire profit warning is the last thing the ad business needs as it grapples with the impact of AI
Business Insider· 2025-07-09 14:24
Core Viewpoint - The advertising industry is facing significant challenges, with WPP's unexpected profit warning indicating a potential downturn, leading to a decline in shares across major ad groups and raising concerns about the impact of AI on traditional agency business models [1][2][10]. Company Summary - WPP has reported a combination of client losses, a slowdown in new business pitches, and cautious marketing strategies due to economic uncertainty, forecasting a revenue decline of 3% to 5% for 2025 [2][4]. - The outgoing CEO of WPP highlighted that new business pitches in 2025 are at one-third of the level compared to the same period last year, reflecting decreased marketer confidence [4]. - WPP has lost key clients, including Pfizer and Coca-Cola's North America account, and has undergone restructuring efforts to enhance competitiveness, which have caused distractions within the business [16][18]. - WPP plans to invest £300 million (approximately $407 million) annually in AI and related technologies, including an investment in Stability AI and the development of an AI-powered platform called WPP Open [14][15]. Industry Summary - The advertising sector is grappling with the rise of AI, which presents both opportunities and threats, as it may streamline services traditionally offered by agencies and challenge their business models [3][5]. - Analysts have noted a sharp decline in new business pitches, suggesting that corporate clients may be replacing some agency services with in-house AI solutions [5][9]. - Major agency groups like Publicis and Omnicom are committing to invest hundreds of millions in AI to adapt their operations [11]. - The competitive landscape is shifting, with Publicis performing well and maintaining its rating despite downgrades for WPP, IPG, and Omnicom due to immediate risks posed by AI [17][18].
在湍流中寻找航向
Hua Xia Shi Bao· 2025-07-07 13:26
Group 1 - The rapid development of artificial intelligence is reshaping the global economic landscape, creating both opportunities and challenges for businesses [2][7] - The concept of "pulsation speed" is introduced as a key to understanding current business dynamics, emphasizing the need for flexibility and foresight over scale in fast-paced industries [4][5] - The book highlights the transition of supply chain design from a cost center to a strategic asset, showcasing examples from companies like Dell and Chrysler [5][6] Group 2 - The notion that all competitive advantages are temporary challenges traditional strategic theories, as illustrated by Kodak's failure to adapt to digital trends despite having the necessary technology [3][8] - The emergence of AI technologies has accelerated the pace of change, leading to a state of "hyper-competition" where competitive advantages can diminish within days [8][9] - The book provides actionable frameworks for businesses to navigate the evolving landscape, emphasizing the importance of adapting to change rather than relying on static barriers [9][10]
物理学家靠生物揭开AI创造力来源:起因竟是“技术缺陷”
量子位· 2025-07-04 04:40
Core Viewpoint - The creativity exhibited by AI, particularly in diffusion models, is hypothesized to be a result of the model architecture itself, rather than a flaw or limitation [1][3][19]. Group 1: Background and Hypothesis - AI systems, especially diffusion models like DALL·E and Stable Diffusion, are designed to replicate training data but often produce novel images instead [3][4]. - Researchers have been puzzled by the apparent creativity of these models, questioning how they generate new samples rather than merely memorizing data [8][6]. - The hypothesis presented by physicists Mason Kamb and Surya Ganguli suggests that the noise reduction process in diffusion models may lead to information loss, akin to a puzzle missing its instructions [8][9]. Group 2: Mechanisms of Creativity - The study draws parallels between the self-assembly processes in biological systems and the functioning of diffusion models, particularly focusing on local interactions and symmetry [11][14]. - The concepts of locality and equivariance in diffusion models are seen as both limitations and sources of creativity, as they force the model to focus on smaller pixel groups without a complete picture [15][19]. - The researchers developed a system called the Equivariant Local Score Machine (ELS) to validate their hypothesis, which demonstrated a 90% accuracy in matching outputs of trained diffusion models [18][19]. Group 3: Implications and Further Questions - The findings suggest that the creativity of diffusion models may be an emergent property of their operational dynamics, rather than a separate, higher-level phenomenon [19][21]. - There remain questions regarding the creativity of other AI systems, such as large language models, which do not rely on the same mechanisms of locality and equivariance [21][22]. - The research indicates that both human and AI creativity may stem from an incomplete understanding of the world, leading to novel and valuable outputs [21][22].
AI改变了一切,除了猫咪
虎嗅APP· 2025-06-30 10:22
Core Viewpoint - The rise of AI-generated cat videos has transformed into a lucrative business, attracting significant attention and engagement across social media platforms [10][12]. Group 1: Popularity and Engagement - AI cat videos have gained immense popularity, with channels like Batysyr achieving 770,000 new followers and 100 million views in just one month through 20 AI cat videos [8]. - Another channel, Cat channel 91, saw a surge of 2 million new subscribers after switching to AI-generated content, with video views skyrocketing from tens of thousands to millions [8]. - The trend is not limited to international platforms; similar growth is observed in domestic accounts, indicating a widespread appeal [10]. Group 2: Monetization Strategies - Creators are monetizing AI cat videos through various methods, including platform revenue sharing, with TikTok videos generating between 1,200 to 2,000 RMB for 10 million views [12]. - Advertising integration within AI pet short dramas is another revenue stream, as seen with accounts promoting pet products [12]. - Some creators charge for the production process of these videos, indicating a direct monetization approach [13]. Group 3: Technological and Cultural Factors - The current success of AI cat videos is attributed to a convergence of advanced AI technology and cultural trends, creating a "perfect chemical reaction" [15]. - The low production cost of these videos, often only requiring a few dozen RMB, has lowered the barrier to entry for creators [19]. - The ability of AI to simulate physical laws convincingly has enhanced the quality and appeal of the content, making it more engaging for viewers [19]. Group 4: Psychological Appeal of Cats - Cats have been chosen as the primary subject due to their inherent appeal, which aligns with psychological concepts like "neoteny," making them relatable and endearing to audiences [22]. - The use of cats helps avoid the "uncanny valley" effect, allowing AI companies to showcase their technology without risking viewer discomfort [22]. - The extensive history of cat content on the internet has provided a rich dataset for AI training, further enhancing the quality of the generated videos [22]. Group 5: Societal Implications - The phenomenon of AI cat videos reflects a broader societal trend, indicating how advanced technology can resonate with fundamental human emotions [24]. - The engagement with AI-generated content serves as a gentle introduction for society to adapt to the future of AI-generated content (AIGC) [24]. - The emotional responses elicited by AI cats highlight the intersection of technology and human sentiment, suggesting that AI can mirror human experiences and desires [24].
AI改变了一切,除了猫咪
Hu Xiu· 2025-06-30 03:25
Core Insights - The article discusses the rising popularity of AI-generated cat videos, particularly focusing on the "AI cat" phenomenon that combines humor and technology to engage audiences [19][20][29]. Group 1: AI Cat Video Trends - AI cat videos are gaining traction on platforms like TikTok and YouTube, with channels experiencing significant growth in followers and views after switching to AI-generated content [11][13]. - For instance, a YouTube channel named Batysyr gained 770,000 followers and 100 million views in a month by posting 20 AI cat videos [11]. - Another channel, Cat channel 91, saw its subscriber count increase by 2 million after transitioning to AI cat videos, with views jumping from tens of thousands to millions [11]. Group 2: Monetization Strategies - Creators are monetizing AI cat content through various methods, including ad placements in videos and charging for video production services [14][15]. - A creator named Ansheng reported earning around 20,000 RMB monthly from multiple AI cat accounts, with TikTok videos generating 1,200 to 2,000 RMB per million views [14]. - The trend has led to the emergence of low-quality, algorithm-driven content, referred to as "AI Slop," which aims to exploit viewer engagement for profit [16]. Group 3: Technological and Cultural Factors - The success of AI cat videos is attributed to a combination of advanced AI technology and cultural factors, creating a "perfect chemical reaction" [19][20]. - The current AI technology allows for realistic simulations of physical actions, making the videos more engaging and shareable [20][23]. - The low production cost of these videos, often just a few dozen RMB, has lowered the barrier for entry, enabling more creators to participate [23]. Group 4: Psychological Appeal of Cats - Cats have been chosen as the primary subject for these videos due to their inherent appeal, which triggers human emotions and empathy [26][29]. - The concept of "neoteny" suggests that cats' features resemble those of infants, making them universally appealing [26]. - Using cats helps avoid the "uncanny valley" effect associated with AI-generated human faces, allowing for broader acceptance of AI content [26]. Group 5: Future Implications - The popularity of AI cat videos signals a shift in how advanced technology can resonate with human emotions, indicating a potential pathway for AI to integrate into everyday life [29][30]. - The phenomenon serves as a social experiment, preparing audiences for a future where AI-generated content becomes commonplace [30][31].