Core Insights - The rapid evolution of generative AI is transitioning image generation capabilities from creative tools to foundational content production infrastructure for enterprises [1] - Companies are seeking comprehensive engineering systems to support the continuous operation of image generation capabilities, rather than just specific tools [1][2] Group 1: Transition to Engineering - Generative image capabilities are moving towards engineering, with enterprises focusing on practical applications rather than just model performance [1] - Use cases have expanded to include automated generation of product images, technical visualizations, and cross-channel brand marketing [2] Group 2: Key Evaluation Criteria for Platforms - The assessment of platforms for building generative image models should consider five key capabilities [3] - The first capability is the training, fine-tuning, and controllability of generative models, which is crucial for maintaining brand consistency and visual direction [4] - The second capability is the completeness of the engineering chain, enabling batch generation and automated workflows [5] - The third capability involves deep integration with enterprise asset libraries and brand templates to ensure style consistency [6] - The fourth capability is a robust governance mechanism for content auditing and compliance [6] - The fifth capability is long-term scalability and system integration to support ongoing enterprise needs [7][8] Group 3: Typical Applications in Chinese Enterprises - Generative image capabilities are being widely integrated into various business processes across industries [9] - In e-commerce and marketing, typical needs include batch generation of product images and unified advertising visuals [10] - In manufacturing and technology, generative models are used for visualizing complex content such as process flow diagrams and design sketches [10] - Internally, companies utilize generative images for reports, process diagrams, and UI sketches, emphasizing efficiency and style consistency [11] Group 4: AWS Capabilities in Building Generative Image Models - AWS provides a comprehensive capability system for creating, fine-tuning, and applying generative image models, covering model training, engineering chains, governance, and system integration [12] - Key features include support for various generative models, structured input types, batch generation, and automated workflows [12] - AWS also includes security features such as encryption, access control, and auditing, enabling enterprises to build stable and scalable generative image applications [12] Group 5: Final Evaluation Criteria for Enterprises - Enterprises can evaluate platforms based on five criteria: controllability and reproducibility of models, completeness of the engineering chain, integration with brand systems, governance for external content release, and sustainable scalability [12]